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UNIVERSIDADE FEDERAL DE ALAGOAS
FACULDADE DE MEDICINA
PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS MÉDICAS

Thaysa Kelly Barbosa Vieira Tomé

Associação entre ritmo circadiano, câncer e diabetes utilizando ferramentas da
bioinformática

Maceió
2021

THAYSA KELLY BARBOSA VIEIRA TOMÉ

Associação entre ritmo circadiano, câncer e diabetes utilizando ferramentas da
bioinformática

Exame de Defesa do Mestrado apresentada ao Programa de
Pós-graduação em Ciências Médicas da Universidade
Federal de Alagoas-UFAL, como parte das exigências para
a obtenção do título de Mestre em Ciências Médicas.
Área de Concentração: Estudos clínicos e laboratoriais em
ciências médicas
Orientador: Prof(a). Dr(a). Carlos Alberto de Carvalho
Fraga
Coorientador: Prof(a). Dr(a).Carolline de Sales Marques

Maceió
2021

Catalogação na fonte
Universidade Federal de Alagoas
Biblioteca Unidade Palmeira dos Índios
Divisão de Tratamento Técnico
Bibliotecária: Lívia Silva dos Santos – CRB-4 - 1670
T855a

Tomé, Thaysa Kelly Barbosa Vieira
Associação entre ritmo circardiano, câncer e diabetes utilizando
ferramentas da bioinformática/ Thaysa Kelly Barbosa Vieira Tomé. - 2021.
100 f. : il.
Orientador: Carlos Alberto de Carvalho Fraga.
Coorientadora: Carolline de Sales Marques.
Dissertação (Mestrado em Ciências Médicas) – Universidade Federal de
Alagoas. Faculdade de Medicina. Programa de Pós-Graduação em Ciências
Médicas, Maceió, 2021.
Bibliografia: f. 80 – 85
1. Ritmo circadiano. 2. Síndrome metabólica. 3. Diabetes. 4. Câncer. I.
Título.
CDU: 616

Folha de Aprovação
Thaysa Kelly Barbosa Vieira Tomé
Associação entre ritmo circadiano, câncer e diabetes utilizando ferramentas da bioinformática
Dissertação submetida ao corpo docente do
Programa de Pós-Graduação em Ciências
Médicas da Universidade Federal de Alagoas e
aprovada em (data).

_____________________________________________
Prof.Dr Carlos Alberto de Carvalho Fraga
UFAL/ FAMED
Orientador(a)

_____________________________________________
Carolline de Sales Marques
UFAL/ FAMED
Coorientador(a)
Banca Examinadora:
_______________________________________________
André Luiz Sena Guimarães
UNIMONTES/ODONTOLOGIA
Examinador externo

Juliana Célia de Farias Santos
UFAL/ FANUT
Examinador interno

Fabiana Andréa Moura
UFAL/ FANUT
Examinador interno

AGRADECIMENTOS
Agradeço a Deus por ter me dado forças e me guiado até aqui, por me segurar quando
pensei em desistir, a Tales que me apoiou em todos os momentos e teve paciência com todas
as etapas percorridas, a Isa que mesmo em meu ventre já me acompanhava nesta jornada e
após o nascimento, onde tive que abdicar de momentos ao seu lado para me dedicar ao
mestrado, aos meus pais e irmã por todo apoio incondicional, ao meu orientador que teve
paciência, que me guiou com maestria até o final o meu muito obrigada.

RESUMO
Introdução: A tumorigênese é afetada pelos genes do relógio. Alterações da expressão dos
genes do relógio podem aumentar a susceptibilidade ao câncer através dos efeitos nos
mecanismos biológicos que regulam o dano e reparo ao DNA, o metabolismo energético,
crescimento e morte celular em tecidos neoplásicos. Objetivo: analisar a associação do ritmo
circadiano, diabetes tipo 2 e câncer. Metodologia: O estudo é uma meta-análise realizada em
amostras de tecido de paciente com diabetes tipo 2 e amostras de tecido normal, genes
relacionados ao ritmo circadiano e dados do transcriptoma associado ao câncer de mama,
bexiga, fígado, pâncreas, cólon e reto usando a integração de perfis de expressão gênica com
biomolecular em escala de genoma redes em amostras de diabetes, os bancos de dados
pesquisados foram o Pubmed e TCGA. Foram utilizados os descritores ritmo circadiano,
diabetes tipo 2 e câncer em inglês com operadores boleanos and ou or. Resultados: KLF10,
NTKR3, IGF1, USP2, EZH2 foram regulados negativamente em amostras de diabetes tipo 2 e
câncer, enquanto ARNTL2 E AGRP foram regulados positivamente. Parece que as alterações
no mRNA estão contribuindo para as alterações fenotípicas no diabetes tipo 2, resultando em
alterações fenotípicas associadas à transformação maligna. Tomando esses genes para realizar
uma análise de sobrevivência, encontramos apenas os genes IGF1, USP2 e ARNTL2 associados
à sobrevida. Enquanto a regulação negativa de IGF1 e USP2 teve um impacto negativo, a
regulação positiva de ARNTL2 foi associada a uma pior sobrevida em amostras de câncer
BLCA e BRCA. Conclusão: Essas moléculas biológicas não apenas representam a associação
de diabetes tipo 2 e biomarcadores de ritmo circadiano com câncer de mama, bexiga, fígado,
cólon e reto, mas também têm potencial significativo para serem consideradas como
biomarcadores em nível de sistema que podem ser usados para triagem ou terapêutica
finalidades.
Palavras-chave: Genes do relógio. IGF1. USP2. Síndrome metabólica. Hipotálamo. Córtex.
Câncer.

ABSTRACT
Introduction: Tumorigenesis is affected by clock genes. Changes in the expression of clock
genes can increase cancer susceptibility through the effects on biological mechanisms that
regulate DNA damage and repair, energy metabolism, cell growth and death in neoplastic
tissues Objective: to analyze the association of circadian rhythm, type 2 diabetes and cancer.
Methodology: The study is a meta-analysis performed on type 2 diabetes, genes related to
circadian rhythm and transcriptome data associated with breast, bladder, liver, pancreas, colon
and rectum cancer using the integration of gene expression profiles with biomolecular in
genome scale networks in diabetes samples, the databases searched were Pubmed and TCGA.
The descriptors circadian rhythm, type 2 diabetes and cancer in English were used with Boolean
operators and or or. Results: several common genes deregulate in diabetes mellitus and cancer.
KLF10, NTKR3, IGF1, USP2, EZH2 were both down-regulated in samples of type 2 diabetes
and cancer, while ARNTL2 AND AGRP were up-regulated. It appears that changes in mRNA
are contributing to phenotypic changes in type 2 diabetes, resulting in phenotypic changes
associated with malignant transformation. Taking these genes to perform a survival analysis,
we found only the IGF1, USP2 and ARNTL2 genes associated with patient results. While
negative regulation of IGF1 and USP2 had a negative impact, positive regulation of ARNTL2
was associated with poor survival in BLCA and BRCA cancer samples. Conclusion: These
biological molecules not only represent the association of type 2 diabetes and circadian rhythm
biomarkers with breast, bladder, liver, colon and rectum cancer, but also have significant
potential to be considered as system-level biomarkers that can be used for screening or
therapeutic purposes.
Keywords:

Clock

Cortex. Cancer.

genes.

IGF1.

USP2.

Metabolic

syndrome.

Hypothalamus.

LISTA DE ILUSTRAÇÕES

Figura 1 – Resumo dos bancos de dados. ...................................................................

LISTA DE SILGAS
ACS

American Cancer Society

ADA

American Diabetes Association

APC

Célula Apresentadora De Antígeno

ARNTL2

Aryl Hydrocarbon Receptor Nuclear Translocator Like 2

ATP

Adenosina Trifosfato

BLCA

Câncer Da Bexiga Urotelial

BRCA

Carcinoma Invasivo da Mama

CCG

Cuidados e Controles Gerais

CCR

Câncer Colorretal

COAD

Adenocarcinoma de Cólon

DEGs

Genes Diferencialmente Expressos

DNA

Ácido Desoxirribonucléico

EGR-1

Resposta de Crescimento Inicial 1

GLUT-4

Transportador de Glicose 4

Ontologia Genética

HDL

Lipoproteína de Alta Densidade

IDF

Federação Internacional de Diabetes

IGF-1

Fator de Crescimento Semelhante a Insulina tipo 1

IL-6

Interleucina 6

IL-1β

Interleucina 1 Beta

KEGG

Enciclopédia de Genes e Genomas de Kyoto

LIHC

Carcinoma Hepatocelular do Fígado

NCBI

National Center for Biotechnology Information

NF-

Fator Nucler Kappa 

PAAD

Adenocarcinoma do Pâncreas

PANTHER

Análise de Proteínas por Relações Evolutivas

PI3K

Fosfatidilinositol 3-Quinase

PTEN

Phosphatase and Tensin Homologue

READ

Adenocarcinoma do Reto

RNA

Ácido Ribonucleico

STK11

Serine/Threonine Kinase 1

TCGA

The Cancer Genome Atlas Program

TIMER

Tumor Immune Estimation Resource

TNF-α

Fator de Necrose Tumoral Alfa

SUMÁRIO

1 INTRODUÇÃO ......................................................................................................... 12
2 OBJETIVOS .............................................................................................................. 14
2.1 Objetivo Geral ......................................................................................................... 14
2.2 Objetivos Específicos .............................................................................................. 14
3 REVISÃO DE LITERATURA .................................................................................. 15
4 METODOLOGIA ...................................................................................................... 23
4.1 Critérios de coleta e inclusão de estudos ................................................................ 23
4.2 Dados de microarray e processamento de dados ..................................................... 24
4.3 Lista de genes .......................................................................................................... 25
4.4 Análise funcional e de enriquecimento de vias ...................................................... 25
4.5 Dados de RNA-seq e dados clínicos do TCGA ...................................................... 25
4.6 Análises de expressão de câncer ............................................................................. 26
5 PRODUTOS .............................................................................................................. 27
5.1 Produto 1- Artigo: Correlation between circadian rhythm related genes, type 2
diabetes, and cancer: insights from metanalysis of transcriptomics data …………....... 28
5.2 Produto 2 - Patente: Painel genético para diagnóstico e prognóstico do câncer de
mama ............................................................................................................................

6 CONCLUSÕES.......................................................................................................... 79
REFERÊNCIAS ...........................................................................................................

ANEXOS ...................................................................................................................... 86
ANEXO A – Regras para publicação no periódico

1 INTRODUÇÃO
Relógios biológicos são sistemas intrínsecos adaptados, que permitem aos organismos
anteciparem as mudanças no ambiente ao seu redor, como por exemplo, a disponibilidade de
comida e a predação e, com isso, permitem que eles adaptem seu comportamento e fisiologia
às diferentes fases do dia, coordenando esses processos em ciclos de 24 horas (ZEE et al., 2013).
A nível molecular, os ritmos biológicos são mantidos por uma maquinaria composta,
fundamentalmente, por um conjunto de genes, os chamados genes relógio, que apresentam uma
alça de retroalimentação e regulam o padrão circadiano de transcrição e tradução deles mesmos
e de muitos genes controlados por eles, os chamados genes controlados pelo relógio
(KENNAWAY et al., 2006; BURDELAK et al., 2013).
A influência externa e interna de diversos fatores como a intensidade e a duração do
tempo de luz no ambiente, o estresse e as condições de saúde também são capazes de alterar os
padrões rítmicos, como de atividade e repouso. Variações fisiológicas ao longo do dia podem
ser observadas durante o ciclo regular, como alterações na atividade física e mental, na função
cardiovascular e regulação de temperatura corporal. Também os parâmetros do sistema
imunológico, como número de leucócitos, função, proliferação e produção de citocinas
apresentam uma marcada variação circadiana (ZEE et al., 2013; ZHU et al., 2012; BERGER,
2008; KENNAWAY et al., 2006).
A maquinaria molecular de transcrição circadiana não está apenas relacionada com a
expressão dos genes relógio, eles estão envolvidos em diversas funções no organismo, como
por exemplo, a secreção de hormônios, o envelhecimento, o ciclo celular, a resposta ao dano no
DNA, dentre outros (RUTTER et al., 2002; MAZZOCCOLI et al., 2012; BOZEK et al., 2009).
A desregulação do ritmo circadiano tem sido relacionada com o surgimento de diferentes
patologias, como transtornos de humor, desordens metabólicas e também o câncer (MILLER
et al., 2007).
A tumorigênese é afetada pelos genes do relógio. Alterações da expressão dos genes do
relógio podem aumentar a susceptibilidade ao câncer através dos efeitos nos mecanismos
biológicos que regulam o dano e reparo ao DNA, o metabolismo energético, crescimento e
morte celular em tecidos neoplásicos (NIRVANI et al.,2018; GERY; KOEFFLER, 2010).
Além da relação entre a dessincronização e a maior incidência de alguns tipos de câncer,
o ritmo circadiano também atua na regulação do metabolismo pelos genes do ciclo circadiano
e afeta diferentes processos metabólicos, como o metabolismo da glicose, colesterol e função
renal, pois os níveis dos hormônios metabólicos glucagon, insulina, grelina, leptina e

corticosterona oscilam de acordo com o ciclo circadiano (SINHA et al., 1996; ECKELMAHAN; SASSONE-CORSI, 2009; SAHAR; SASSONE-CORSI, 2012). As consequências
da desregulação desses genes a longo prazo podem causar síndrome metabólica e obesidade
(TUREK et al., 2005; MARCHEVA et al., 2010; SASSONE-CORSI, 2012).
Existe uma forte associação entre obesidade, diabetes e câncer. Em 2010, a American
Diabetes Association (ADA) e a American Cancer Society (ACS) publicaram um documento
que afirma que o diabetes aumenta o risco de câncer de fígado, pâncreas, endométrio, cólon e
reto, mama e bexiga (GIOVANNUCCI et al. 2010). Diabetes mellitus pode influenciar no
surgimento de câncer de três formas principais: hiperinsulinemia, hiperglicemia ou inflamação
crônica e desregulação dos hormônios sexuais (CALIMLIOGLU et al., 2015; GIOVANNUCCI
et al., 2010). Cabe ressaltar que existem inúmeros fatores de riscos comuns entre diabetes
mellitus e câncer como: idade, atividade física, obesidade, dieta, tabagismo e etilismo
(GIOVANNUCCI et al., 2010).
CALIMLIOGLU em seu estudo demonstrou a associação entre diabetes mellitus tipo 2 e
câncer de pulmão, pâncreas, próstata e colorretal (CCR), demonstrando que as proteínas APC,
EGFR, KPCA, MDM2, MK01, MK08, MYC, P53, TF65, TNR6, P85A 15 e SMAD3 estão
associadas a mais de um tipo de câncer, as proteínas são oriundas de proto-oncogenes e genes
supressores de tumor que, quando estão mutados, levam ao desenvolvimento do câncer
(CALIMLIOGLU et al., 2015).
Apesar da interação entre ritmos circadianos e câncer ser bastante descrita na literatura,
pouco se sabe sobre genes envolvidos com o desenvolvimento de tumores que possam ser
modulados pelo sistema de temporização circadiano, assim como se existe a associação entre o
surgimento de câncer e diabetes relacionados a alteração do ritmo circadiano e dos genes do
relógio envolvidos no processo. No presente estudo, buscou-se os elementos de ligação entre
alteração do ritmo circadiano - câncer – diabetes.

2 OBJETIVOS
2.1 Objetivo geral


Avaliar a associação do ritmo circadiano, diabetes tipo 2 e câncer utilizando
ferramentas da bioinformática

2.2 Objetivos específicos


Comparar a expressão diferencial de genes em diferentes amostras teciduais de
indivíduos com e sem diabetes tipo 2;



Diferenciar a expressão gênica em modelos animais para distúrbios do ritmo
circadiano;



Analisar a expressão gênica em tecidos de indivíduos sem câncer e em tecidos
com tumores de mama, bexiga, reto, cólon, fígado e pâncreas;



Associar a expressão diferencial de genes nas diferentes amostras de modelo
animal de ritmo circadiano, diabetes tipo 2 e câncer;



Analisar a sobrevida dos pacientes com os diferentes tipos de câncer,
considerando o perfil de expressão gênica e vias metabólicas alteradas;

3 REVISÃO DE LITERATURA
Diabetes Mellitus
O Diabetes mellitus é uma doença crônica, autoimune, caracterizada por alterações na
secreção e/ou atuação da insulina, com consequente hiperglicemia crônica, a qual é responsável
pelas manifestações clínicas de polidipsia, poliúria e emagrecimento de forma involuntária
(CALIMLIOGLU et al.,2015; DEEPTHI et al., 2017). Outros sintomas que levantam a suspeita
clínica são: fadiga,fraqueza, letargia, prurido cutâneo e vulvar, balanopostite e infecções de
repetição. Algumas vezes o diagnóstico é feito a partir de complicações crônicas como
neuropatia, retinopatia ou doença cardiovascular aterosclerótica (BRASIL, 2006; ADA, 2017)
De acordo com a Federação Internacional de Diabetes, (IDF), 1 a cada 11 adultos possuía
diabetes em 2019, o equivalente a 423 milhões de pessoas (FEDERAÇÃO INTERNACIONAL
DE DIABETES, 2019). No Brasil, há 12,5 milhões de pessoas com diagnóstico de diabetes,
ocupando o 4º lugar entre os 10 países com maior número de indivíduos acometidos por ela; o
3º em número de crianças e adolescentes com diabetes tipo 1; e o 6º país do mundo em gastos
com a doença (AMERICAN DIABETES ASSOCIATION, 2017). Entretanto, o Brasil não se
enquadra entre os 10 países com maior investimento médio por individuo com diabetes (Ibid.).
As estimativas de mortalidade por diabetes e suas complicações a colocam entre as
principais causas de morte por doença crônica no mundo, juntamente com doenças cardíacas
isquêmicas

doenças

cerebrovasculares

(ZIMMET

al.,

2016).

Em 2019, foi estimado um total de 4,2 milhões de mortes por diabetes, o equivalente a uma
morte a cada oito segundos (FEDERAÇÃO INTERNACIONAL DE DIABETES, 2019). Em
Alagoas, o diabetes mellitus representa a quarta maior causa de internações por condições
sensíveis à atenção primária e a terceira causa de óbitos totalizando 13.103 no período de 2007
a 2016 (SESAU, 2017).
A depender de sua patogenia, é classificado em diabetes tipo 1, tipo 2, gestacional ou de
outras causas específicas, sendo o diabetes tipo 2 o mais prevalente (90-95% dos casos)
(AMERICAN DIABETES ASSOCIATION, AMERICAN DIABETES ASSOCIATION,
2019), sendo comum em adultos mais velhos. No entanto, em resposta às taxas crescentes de
obesidade e sedentarismo, a incidência entre crianças e adolescentes está em crescente aumento
(Ibid.).
Indivíduos com diabetes tipo 2 apresentam produção deficiente de insulina e resistência
periférica à insulina concomitantemente, que estão associados a processo inflamatório crônico
e estresse oxidativo às células β pancreáticas, produtoras de insulina, e aos tecidos que

respondem à ação do hormônio (DHARMALINGAM; MARCUS, 2019). A hiperglicemia é
associada ao estresse oxidativo, o qual é caracterizado por um desequilíbrio na geração de
radicais livres (espécies reativas de oxigênio-ROS- e espécies reativas de nitrogênio-RNS-) e
sua neutralização pelos mecanismos anti-oxidantes, o aumento de ROS E RNS acarreta
destruição das ilhotas pancreáticas por apoptose e consequentemente leve à resistência à
insulina (Ibid.). A insulina tem atividade anti-inflamatória: suprime a transcrição de fatores proinflamatórios como o fator nucler kappa  (NF-), resposta de crescimento inicial 1 (EGR-1)
e proteína ativadora 1 (AP-1) e seus genes correspondentes que medeiam a inflamação, porém,
a resistência insulínica causa a ativação dessas transcrições levando à inflamação (Ibid.)
O desenvolvimento da resistência periférica à insulina relaciona-se à ação de mediadores
pró-inflamatórios, tais como interleucina 1 beta (IL-1β), interleucina 6 (IL-6), fator de necrose
tumoral alfa (TNF-α) (AKASH; REHMAN; LIAQAT, 2017; DHARMALINGAM; MARCUS,
2019). Níveis elevados de TNF-α reduzem a expressão gênica do transportador de glicose 4
(GLUT-4) responsável pelo transporte da glicose para os adipócitos e células musculares
esqueléticas e cardíacas - e induzem a fosforilação do substrato-1 do receptor de insulina (IRS1), inativando-o e reduzindo a resposta fisiológica dos tecidos à insulina (AKASH; REHMAN;
LIAQAT, 2017). Devido à inibição da captação da glicose e da ação da insulina, não há
estímulo para produção de adenosina trifosfato (ATP) a partir da glicose, para glicogênese e
para captação de aminoácidos e produção de proteínas nos tecidos musculares esquelético e
cardíaco e para a lipogênese nos adipócitos. Além disso, a insulina torna-se incapaz de inibir a
lipólise nos adipócitos e produção de glicose pelo fígado. Todos esses efeitos levam à
hiperglicemia e ao desenvolvimento do diabetes (PETERSEN; SHULMAN, 2018). De modo
que, os níveis elevados de glicose no sangue alteram vias metabólicas teciduais e provocam
estresse oxidativo. No pâncreas endócrino, o acúmulo das espécies reativas de oxigênio altera
o microambiente celular, acarretando a destruição das células beta e na produção deficiente de
insulina, corroborando para maior elevação dos níveis sanguíneos de glicose e para a progressão
do diabetes (DHARMALINGAM; MARCUS, 2019)
Tanto o processo inflamatório quanto o estresse oxidativo parecem ser desencadeados por
fatores genéticos e epigenéticos, ainda não bem compreendidos, e por fatores de risco
modificáveis,

sendo

obesidade

central

principal

(AMERICAN

DIABETES

ASSOCIATION, 2019; DEEPTHI, 2019), fator esse que pode estar relacionando tanto ao
surgimento de neoplasias como pior sobrevida naqueles pacientes que a apresentam
(GIOVANNUCCI et al., 2010).

Diabetes mellitus e Câncer
Diabetes mellitus pode influenciar no surgimento de câncer de três formas principais:
hiperinsulinemia, hiperglicemia ou inflamação crônica e desregulação dos hormônios sexuais
(CALIMLIOGLU et al., 2015; GIOVANNUCCI et al., 2010). Muitas células neoplásicas
expressam receptores de insulina e receptores de fator de crescimento semelhante à insulina
tipo 1 (IGF-1), a hiperinsulinemia leva ao aumento da biodisponibilidade de IGF-1 por meio da
diminuição dos níveis de suas proteínas de ligação, os receptores de IGF-1 são expressos em
quase todos os tecidos do corpo e ativam vias mitogênicas na proliferação celular
(GIOVANNUCCI et al., 2010; ESPOSITO et al., 2012). Por sua vez, os receptores de insulina,
em particular, sua isoforma A podem estimular a mitogênese, mesmo em células deficientes de
receptores de IGF-1, assim como é capaz de estimular a proliferação e metástase de células
neoplásicas (GIOVANNUCCI et al., 2010; COHEN; LEHOITH, 2012). A hiperglicemia
também permite que o IGF-1 estimule a proliferação da célula do músculo liso vascular que
está associado a fisiopatologia da aterosclerose assim como está presente no câncer
(GIOVANNUCCI et al., 2010), leva, ainda, à redução da síntese hepática e redução no sangue
dos níveis de globulinas de ligação dos hormônios sexuais, acarretando aumento da
biodisponibilidade do estrogênio em homens e mulheres, o aumento de estrogênio nas mulheres
pós-menopausadas aumenta o risco de câncer de endométrio e de mama (GIOVANNUCCI et
al., 2010).
Existe uma forte associação entre obesidade, diabetes e câncer. Em 2010, a American
Diabetes Association (ADA) e a American Cancer Society (ACS) publicaram um documento
que afirma que o diabetes aumenta o risco de câncer de fígado, pâncreas, endométrio, cólon e
reto, mama e bexiga (GIOVANNUCCI et al., 2010). Cabe ressaltar que existem inúmeros
fatores de riscos comuns entre diabetes mellitus e câncer como: idade, atividade física,
obesidade, dieta, tabagismo e etilismo (GIOVANNUCCI et al., 2010).
CALIMLIOGLU em seu estudo demonstrou a associação entre diabetes mellitus tipo 2 e
câncer de pulmão, pâncreas, próstata e colorretal (CCR), demonstrando que as proteínas APC,
EGFR, KPCA, MDM2, MK01, MK08, MYC, P53, TF65, TNR6, P85A e SMAD3 estão
associadas a mais de um tipo de câncer, as proteínas são oriundas de proto-oncogenes e genes
supressores de tumor que, quando estão mutados, levam ao desenvolvimento do câncer
(CALIMLIOGLU et al., 2015).

Fatores relacionados ao diabetes como a doença hepática não gordurosa, a qual se
manifesta com esteatose e cirrose podem aumentar a suscetibilidade ao câncer hepático
(GIOVANNUCCI et al., 2010; BHATT; SIMTH, 2015). Em relação ao câncer de pâncreas, o
mesmo pode ser a causa do metabolismo anormal da glicose, porém uma associação positiva
foi encontrada quando o diagnóstico de diabetes mellitus antecede o câncer em 5 anos,
diminuindo as chances da causa do diabetes mellitus ser o próprio câncer, provavelmente, pela
associação entre a presença do diabetes e o surgimento de câncer pelos mecanismo de
hiperinsulinemia, hiperglicemia ou inflamação crônica e desregulação dos hormônios sexuais
(CALIMLIOGLU et al., 2015; GIOVANNUCCI et al., 2010).
Obesidade é um fator de confusão com relação a associação entre diabetes mellitus e o câncer
de mama, já que ambas estão associadas à resistência periférica à insulina, o aumento dos níveis
de insulina e de IGF-1 levam ao aumento dos níveis de estrogênio circulante pela redução das
proteínas de ligação dos hormônios sexuais (estrógeno e andrógenos) ambos associados ao
câncer de mama (AMERICAN DIABETES ASSOCIATION, 2017; DANTAS et al., 2009;
BUONO et al., 2017).
HARDEFELDT et al. (2012,) em sua metanálise demonstraram um aumento de 20% no
risco de câncer de mama em mulheres com diabetes mellitus e de 29% em homens (Ibid.). Por
sua vez, BUONO demonstrou que pacientes que tinham a associação de obesidade, diabetes
mellitus e câncer de mama apresentavam menores taxas de sobrevida livre de doença,
independentemente do tamanho e subtipo do tumor, idade e/ou tratamento realizado,
destacando-se, assim, o diabetes como um fator de mau prognóstico de forma isolada (BUONO
et al., 2017).
Mutações nos genes BRCA 1 e 2 estão relacionados com o câncer de mama hereditário,
mulheres com mutações no BRCA 1 apresentam 87% de chance de desenvolver câncer e 85%
quando a mutação é em BRCA 2 (HARDEFELDT et al ,2012). Outros genes como p53, PTEN,
STK11/LKB1, MLH1 e MLH2 também podem ter associação com síndromes hereditárias e seu
risco aumentado para câncer de mama (LOFRANO et al., 2009).
O gene PTEN foi descrito com papéis tanto no crescimento celular quanto na sinalização
metabólica. Ele atua como gene supressor de tumor, principalmente devido à sua atividade de
fosfatase lipídica que regula negativamente a via de sinalização da fosfatidilinositol 3-quinase
(PI3K) -AKT que funciona como um mediador da maioria dos receptores de tirosina quinases
(WANG et al, 2014), ele também foi implicado no diabetes tipo 2, pois a via PI3K-AKT
desempenha papel na sinalização de insulina (PAL et al., 2012). Mutações germinativas do

PTEN predispõem ao processo de desenvolvimento neoplásico, assim como ao
desenvolvimento de obesidade (PAL et al., 2012).
O mecanismo biológico entre diabetes mellitus e CCR, não está claramente definido,
havendo a possibilidade de estar relacionado aos fatores de risco comuns associados à
obesidade e inflamação, assim como hiperinsulinemia e hiperglicemia (ZHU et al., 2017). Del
Puerto-Nevado et al. (2017) demonstraram a expressão de alguns genes no diabetes mellitus
que têm relação com câncer colorretal: APC; KRAS; p53; MSH2; MSH6 estão aumentados,
assim como o aumento de STK11 que está associada à síndrome de Peutz-Jegher (Ibid.).

Ritmo circadiano
Relógios biológicos são sistemas intrínsecos adaptados, que permitem aos organismos
anteciparem as mudanças no ambiente ao seu redor, como por exemplo, a disponibilidade de
comida e a predação e, com isso, permitem que eles adaptem seu comportamento e fisiologia
às diferentes fases do dia, coordenando esses processos em ciclos de 24 horas (ZEE et al., 2013).
Essa sincronização aos ciclos ambientais provoca a sincronização interna dos eventos
fisiológicos circadianos. Como consequência, os organismos conseguem antecipar os eventos
fisiológicos, economizando energia e otimizando as reações. Essas observações evidenciam a
importância de manter a ordem temporal interna, com as relações de fase bem estabelecidas
entre as variáveis fisiológicas (Ibid.).
O termo “ritmo circadiano” (do Latim circa diem que significa “cerca de um dia”) foi
criado para descrever oscilações endógenas em organismos, que foram assim classificadas por
conter um período aproximado ao da rotação diária do planeta Terra (ZEE et al., 2013; ZHU et
al., 2012). Uma das características dos relógios circadianos é a capacidade de serem
sincronizados por estímulos externos, mantendo, entretanto, as oscilações mesmo na ausência
desses estímulos, ou seja, são autossustentáveis (ZEE et al., 2013; ZHU et al., 2012;
KENNAWAY et al., 2006).
Um grande número de processos biológicos é regulado pelo relógio circadiano, como
por exemplo: comportamento alimentar, ciclos de sono-vigília, variação hormonal, temperatura
corporal e metabolismo energético ( ZEE et al., 2013; KENNAWAY et al., 2006). Contudo, a
influência externa e interna de diversos fatores como a intensidade e a duração do tempo de luz
no ambiente, o estresse e as condições de saúde também são capazes de alterar os padrões
rítmicos, como de atividade e repouso. Variações fisiológicas ao longo do dia podem ser
observadas durante o ciclo regular, como alterações na atividade física e mental, na função

cardiovascular e regulação de temperatura corporal. Também os parâmetros do sistema
imunológico, como número de leucócitos, função, proliferação e produção de citocinas
apresentam uma marcada variação circadiana (ZEE et al., 2013; ZHU et al., 2012; BERGER,
2008; KENNAWAY et al., 2006).
A nível molecular, os ritmos biológicos são mantidos por uma maquinaria composta,
fundamentalmente, por um conjunto de genes, os chamados genes relógio, que apresentam uma
alça de retroalimentação e regulam o padrão circadiano de transcrição e tradução deles mesmos
e de muitos genes controlados por eles, os chamados genes controlados pelo relógio (CCGs, do
inglês clock controlled genes) (KENNAWAY et al., 2006; BURDELAK et al, 2013).

Ritmo Circadiano, Câncer e Síndrome metabólica
A maquinaria molecular de transcrição circadiana não está apenas relacionada com a
expressão dos genes relógio, eles estão envolvidos em diversas funções no organismo, como
por exemplo, a secreção de hormônios, o envelhecimento, o ciclo celular, a resposta ao dano no
DNA, dentre outros (RUTTER et al., 2002; MAZZOCCOLI et al., 2012; BOZEK et al., 2009).
A desregulação do ritmo circadiano tem sido relacionada com o surgimento de diferentes
patologias, como transtornos de humor, desordens metabólicas e também o câncer (MILLER
et al., 2007).
O relógio circadiano e o ciclo celular possuem semelhanças conceituais e moleculares,
pois ambos são constituídos por processos que apresentam alças de regulação interligadas e
apresentam sequências de transcrição, tradução, modificação e degradação de proteínas
(MAZZOCCOLI et al., 2012; LANDGRAF; SHOSTAK; OSTER, 2012; SOTÁK et al., 2013).
Existem interações entre esses dois ciclos e, quando há uma desregulação no ritmo circadiano,
há uma alteração no ciclo celular, fazendo com que as células se proliferem desordenadamente.
Alterações da expressão dos genes do relógio podem aumentar a susceptibilidade ao
câncer através dos efeitos nos mecanismos biológicos que regulam o dano e reparo ao ácido
desoxirribonucléico (DNA), o metabolismo energético, crescimento e morte celular em tecidos
neoplásicos (NIRVANI et al., 2018; GERY; KOEFFLER, 2010). Genes que fazem parte do
mecanismo de regulação do ciclo celular, como Myc, p53, Cyclin D1 e Wee1, já foram descritos
por apresentarem alteração em sua expressão em pacientes com leucemias associadas à
desregulação de genes do relógio (ZEE et al., 2013; MAZZOCCOLI et al., 2012; LANDGRAF;
SHOSTAK; OSTER, 2012)

Além da relação entre a dessincronização e a maior incidência de alguns tipos de câncer,
o

ritmo

circadiano

também

atua

regulação

metabolismo

pelos

genes do ciclo circadiano e afeta diferentes processos metabólicos, como o metabolismo da
glicose, colesterol e função renal, pois os níveis dos hormônios metabólicos glucagon, insulina,
grelina,

leptina

corticosterona

oscilam

acordo

com o ciclo circadiano (SINHA et al., 1996; ECKEL-MAHAN; SASSONE-CORSI,
2009;

SAHAR;

SASSONE-CORSI,

desses

genes

longo

prazo

2012).

podem

causar

consequências

síndrome

metabólica

desregulação
e

obesidade

mundo

(TUREK et al., 2005; MARCHEVA et al., 2010; SASSONE-CORSI, 2012).
A

incidência

síndrome

metabólica

está

aumentando

ocidental e sua correlação com o câncer tem se tornado mais aparente (BRAUN et
al., 2011). A síndrome metabólica é uma combinação de condições patológicas que coexistem
num mesmo indivíduo, incluindo hiperglicemia, hipertensão, hipertrigliceridemia, baixo nível
da lipoproteína de alta densidade (HDL) e aumento da circunferência abdominal (ALBERTI et
al., 2009; BATTELLI et al., 2019). Essa combinação de fatores predispõe à obesidade, ao
aumento de doenças cardiovasculares, diabetes tipo 2 e vários estudos têm associado à
prevalência

certos

tipos

câncer

(BITZUR et al., 2016; BELLASTELLA, G. et al., 2018). No estudo de Esposito (2012), nos
homens, a síndrome metabólica foi associada com os cânceres de fígado, cólon e bexiga.
Enquanto, que, nas mulheres, esse estudo revelou uma associação com cânceres endometrial,
pancreático, mama pós-menopausa, retal e colorretal (ESPOSITO et al., 2012).
A fisiopatologia da síndrome metabólica está relacionada principalmente com a
capacidade do tecido adiposo secretar uma ampla variedade de mediadores bioativos que
modulam várias cascatas de sinalização, os quais são chamados de adipocinas (OUCHI et al.,
2011; MENDONÇA et al., 2015). Nos pacientes com síndrome metabólica, ocorre uma
desregulação na produção de adipocitocinas, as quais podem desencadear um estado
inflamatório crônico (BRAUN et al., 2011; MENDONÇA et al., 2015). A inflamação e seus
mediadores aumentam o risco de desenvolver câncer de esôfago, mama, vesícula biliar, renal,
pancreático, colorretal e gástrico (GRAI et al., 2015; HRISTOVA, 2018).
Na obesidade, há um aumento dos ácidos graxos livres, citocinas inflamatórias no plasma,
infiltração e ativação de macrófagos e decréscimo dos níveis de adiponectina, os quais podem
contribuir para a resistência à insulina (BRAUN et al., 2011). O aumento da biodisponibilidade
da insulina causa o diabetes tipo 2, podendo ativar as vias de efeito mitogênico (Ras /Raf/
MAPK ou PI3K /AKT/ mTOR), diminuir a apoptose e aumentar a síntese de proteínas (WONG

et al., 2010; BELLASTELLA et al., 2018). A ativação dessas vias carcinogênicas predispõe ao
aumento do risco de câncer (WONG et al., 2010; BELLASTELLA et al., 2018).
Pacientes obesos têm níveis elevados de leptina, que tem atividade mitogênica/antiapoptótica, estimula a migração e invasão das células neoplásicas, pode aumentar a produção
de citocinas pelos macrófagos e pode promover a neoagiogênese através da indução e ativação
de fatores pro-angiogênicos (COHEN; LEROITH, 2012; LIGIBEL; STRICKLER, 2013) e
possuem baixos níveis de adiponectina que tem atividade anti-mitótica/pro-apoptótica
(LIGIBEL; STRICKLER, 2013).
Apesar da interação entre ritmos circadianos e câncer ser bastante descrita na literatura,
pouco se sabe sobre genes envolvidos com o desenvolvimento de tumores que possam ser
modulados pelo sistema de temporização circadiano, assim como se existe a associação entre o
surgimento de câncer e diabetes relacionados a alteração do ritmo circadiano e dos genes do
relógio envolvidos no processo.

4 METODOLOGIA
O estudo é uma metanálise, onde os bancos de dados pesquisados foram o Pubmed e The
Cancer Genome Atlas Program (TCGA). Foram utilizados os descritores ritmo circadiano,
diabetes tipo 2 e câncer em inglês com operadores boleanos and ou or.
4.1 Critérios de coleta e inclusão de estudos
Foi pesquisado o banco de dados GEO (https://www.ncbi.nlm.nih.gov/geo/) para estudos
disponíveis publicados. Após uma revisão sistemática, 6 estudos de GSE foram encontrados.
Os critérios de inclusão para os estudos foram os seguintes:
(1) amostras diagnosticadas com diabetes mellitus tipo 2 e amostras normais
(2) experimentos que envolviam ritmo circadiano e / ou análise do sono
(3) perfil de expressão gênica de mRNA (ácido ribonucléico).
Seis perfis de diabetes de expressão gênica tipo 2 (GSE56931, GSE20966, GSE25724,
GSE26168, GSE23343 e GSE29221) foram baixados do banco de dados GEO para análise, já
para avaliação de privação de sono, apenas o GSE6514 foi baixado
Os perfis de expressão de genes humanos de pacientes diabéticos tipo 2 e não diabéticos
de tecido enriquecido com células beta foram obtidos por microdissecção de captura a laser
foram:
- GSE20966 (20 amostras / 7 mulheres e 13 homens)
- GSE25724 (ilhotas pancreáticas - 13 amostras / 6 mulheres e 7 homens)
- GSE26168 (sangue - 24 amostras masculinas)
- GSE23343 (fígado - 17 amostras / 7 mulheres e 10 homens)
- GSE29221 (músculo esquelético - 24 amostras masculinas)
As coletas de sangue a cada 4 horas durante um estudo de 3 dias: linha de base normal de
24 horas, 38 horas de vigília contínua e sono de recuperação subsequente, para um total de 19

pontos de tempo por sujeito, com cada avaliação de teste de vigilância psicomotora de 2 horas
quando acordado (GSE56931).
Experimentos de privação de sono GSE6514 foram realizados em camundongos machos
amostras C57 / BL6 com, aproximadamente, 10 semanas de idade com ± 1 semana de diferença.
Os animais foram alojados em um ciclo claro/escuro de 12:12 h com água disponível ad libitum,
foram submetidos a 14 dias de aclimatação, durante os quais foi estabelecido um padrão
alimentar noturno. Os camundongos foram sacrificados após 3, 6, 9 e 12 horas de privação total
de sono. A privação foi iniciada com a luz acesa e realizada por meio de um manuseio delicado.
As amostras de GSE82113 foram analisadas em condições normais de privação de sono e
condições de tempo de sono anormais para avaliar a robustez do preditor. Amostras de sangue
cobrindo pelo menos um ciclo circadiano para medir a abundância de mRNA (ácido
ribonucleico) e medir o ritmo da melatonina. Também foram realizadas análises em perfis de
tecidos periféricos. GSE4239 foi usado para determinar genes regulados pelo relógio na
glândula adrenal. Animais Per2Brdm1 / Cry1− / - foram usados para análise de expressão. Os
animais foram conduzidos a um ciclo de 12 horas de luz: 12 horas de escuridão por duas
semanas e depois liberados em escuridão constante. A alimentação restrita impacta o relógio
circadiano hepático de Cry1, camundongos duplo Knockout Cry2 sem um relógio circadiano
foi realizada usando o perfil GSE13062. Da mesma forma, o tecido do fígado de tipo selvagem,
mutante de Clock e C57BL / 6 deficiente em Cry camundongos machos de 8 a 10 semanas de
idade foi examinado no perfil GSE454. Os camundongos foram arrastados sob 12 h de luz
branca e 12 h de escuridão por 14 dias. O perfil GSE6904 mostrou que os dados de
camundongos foram expostos a partir de 1 h após as luzes apagadas a um pulso de luz de 30
minutos. No final do pulso de luz, os ratos foram sacrificados e os cérebros extraídos. As células
do núcleo supraquiasmático foram extraídas usando microscopia de captura a laser e a
expressão gênica foi quantificada usando Affymetrix microarrays.
4.2 Dados de microarray e processamento de dados
GEO2R foi aplicado para rastrear mRNAs diferencialmente expressos entre diabetes
mellitus tipo 2 e amostras de tecido normal.
GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) é uma ferramenta da web interativa
para comparar dois grupos de dados que podem analisar qualquer série GEO. Os valores de p

ajustados usando o método de de Benjamini e Hochberg padrão foram aplicados para corrigir a
ocorrência de resultados falso-positivos.
4.3 Lista de genes
Entrez

Gene

NCBI

(www.ncbi.nlm.nih.gov/gene/)

GeneCards

(https://www.genecards.org/) foram usados como identificadores dos genes relacionados ao
ritmo circadiano. A lista de genes e DEGs downregulated / upregulated dos perfis de expressão
gênica

foram

combinados

identificados

com

Diagrama

Venn

2.1.0

(http://bioinfogp.cnb.csic.es/tools/venny/index.html).

4.4 Análise funcional e de enriquecimento de vias
A análise da ontologia genética (GO) dos processos biológicos relevantes, componentes
celulares e funções moleculares foi realizada usando o programa Análise de Proteínas por
Relações Evolutivas (PANTHER, www.pantherdb.org), um banco de dados que reúne famílias
de proteínas, funções e vias. Os termos GO atribuídos às moléculas identificadas foram
classificados de acordo com a sua função (BASTOS et al., 2011). A Enciclopédia de Genes e
Genomas de Kyoto (KEGG) é um banco de dados integrado para interpretação biológica de
sequências de genoma e outros dados de alto rendimento (KANEHISA et al., 2016). As análises
do KEGG estavam disponíveis no banco de dados DAVID (https://david.ncifcrf.gov/), um
recurso de dados composto por uma base de conhecimento de biologia integrada e ferramentas
de análise para extrair informações biológicas significativas de grandes quantidades de genes e
coleções de proteínas. As análises KEGG foram realizadas usando o banco de dados DAVID
para identificar DEGs (genes expressos diferencialmente). Um valor de p <0,05 foi estabelecido
como o critério de corte (HUANG et al., 2009).
4.5 Dados de RNA-seq e dados clínicos do TCGA
Foi

utilizado

TCGAbiolinks

R/Bioconductor

(http://bioconductor.org/

packages/release/bioc/html/TCGAbiolinks.html)

(COLAPRICO

al.,

2016)

TCGAbiolinksGUI (SILVA et al., 2018) para baixar dados genômicos e clínicos de tecidos
normais e de tumores sólidos para seis tipos de câncer do TCGA. Os tipos de câncer foram:
câncer da bexiga urotelial (BLCA), carcinoma invasivo da mama (BRCA), adenocarcinoma do

cólon (COAD), carcinoma hepatocelular do fígado (LIHC), adenocarcinoma do pâncreas
(PAAD) e adenocarcinoma do reto (READ). Foram recuperados dados de nível para a
expressão de mRNA e miRNA (ILLUMINA HISEQ, 2000).
4.6 Análises de expressão de câncer
Utilizamos o servidor da web Tumor Immune Estimation Resource (TIMER)
(https://cistrome.shinyapps.io/timer/), uma ferramenta analítica abrangente que reanalisou
dados do TCGA, para detectar a expressão gênica em vários tipos de câncer (LI et al., 2017).
Os perfis de expressão de BLCA, BRCA, COAD, LIHC, PAAD e READ para amostras normais
comparadas com as neoplásicas foram obtidos usando a opção TIMER diff.exp
(https://cistrome.shinyapps.io/timer/). A análise de sobrevida dos dados do TCGA foi realizada
utilizando o módulo Survival do Tumor Immune Estimation Resources (TIMER). Gráficos de
Kaplan-Meier foram desenhados para explorar a associação entre o resultado clínico e a
expressão gênica e para visualizar as diferenças de sobrevida. Dados de proteína baseados em
imagens de imunohistoquímica para amostras normais e de câncer estão disponíveis no Atlas
de Proteínas humanas (https://www.proteinatlas.org/).
Figura 1 - Resumo dos bancos de dados utilizados e as finalidades para qual foram utilizados

Fonte: elaborado pelos autores

5 PRODUTOS

Correlation

between

circadian

rhythm

genes,

type

diabetes, and cancer: insights from metanalysis of transcriptomics data, segundo as
normas da Molecular and Cellular Endocrinology
2.

Patente: Privilégio de Inovação. Número do registro: BR10202002668, título: "Painel
genético para diagnóstico e prognóstico do câncer de mama”, Instituição de registro: INPI
- Instituto Nacional da Propriedade Industrial. Depósito: 24/12/2020

5.1 PRODUTO 1
Correlation between circadian rhythm related genes, type 2diabetes, and cancer: insights
from metanalysis of transcriptomics data. Publicado no periódico Molecular and Cellular
Endocrinology

Correlation between circadian rhythm related genes, type 2 diabetes, and cancer:
insights from metanalysis of transcriptomics data
Thaysa Kelly Barbosa Vieira1#, Myra Jurema da Rocha Leão1#, Luciana Xavier Pereira1,
Laryssa Cristina Alves da Silva1, Bruno Batista Pereira da Paz1, Ricardo Jansen Santos
Ferreira1, Christiane Cavalcante Feitoza1, Ana Kelly Fernandes Duarte1, Amanda Karine Barros
Ferreira Rodrigues1, Aline Cavalcanti de Queiroz1, Karol Fireman de Farias1, Bruna Del Vechio
Koike2*, Carolinne de Sales Marques1*, Carlos Alberto de Carvalho Fraga1*
1

Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom

Sucesso, Arapiraca, AL 57309-005, Brazil.
2

Federal University of the São Francisco Valley, Petrolina. Av. José de Sá Maniçoba, S/N -

Centro, Petrolina - PE, 56304-917, Brazil.
#

Both authors contributed equally to this work

* Corresponding authors Carlos Alberto de Carvalho Fraga, Carolinne de Sales Marques and
Bruna Del Vechio Koike.
e-mail:carlos.fraga@arapiraca.ufal.br

ABSTRACT
Clock genes work as an auto-regulated transcription-translational loop of circadian genes that
drives the circadian rhythms in each cell and they are essential to physiological requests. Since
metabolism is a dynamic process, it involves several physiological variables that circadian
cycling. The clock genes alterations can affect multiple systems concomitantly, because they
constitute the promoter factors for relevant metabolic pathways. Considering the intertwined
structure of signaling, regulatory, and metabolic processes within a cell, we employed a
genome-scale biomolecular network. Accordingly, a meta-analysis of diabetic-associated
transcriptomic datasets was performed, and the core information on differentially expressed
genes (DEGs) was obtained by statistical analyses. In the current study, meta-analysis was
performed on type 2 diabetes, circadian rhythm-related genes, and breast, bladder, liver,
pancreas, colon and rectum cancer-associated transcriptome data using the integration of gene
expression profiles with genome-scale biomolecular networks in diabetes samples. First, we
detected downregulated and upregulated DEGs in mouse cortex and hypothalamus samples of
mice with sleep deprivation. In summary, upregulated genes active genes associated with
oxidative phosphorylation, cancer and diabetes, mainly in hypothalamus specimens. In cortex,
we observed mainly downregulation of immune system. DEGs were combined with 214
circadian rhythm related genes to type 2 DM and cancer samples. We observed that several
common genes deregulated in both diseases. Klf10, Ntkr3, Igf1, Usp2, Ezh2 were both
downregulated in type 2 DM and cancer samples, while Arntl2 and Agrp were upregulated. It
seems that the changes in mRNA are contributing to the phenotypic changes in type 2 DM,
resulting in phenotypic changes associated with the malignant transformation. Taking those
genes to perform a survival analysis, we found only Igf1, Usp2 and Arntl2 genes associated
with patient outcomes. While Igf1 and Usp2 downregulation had a negative impact, Arntl2
upregulation was associated with poor survival both in BLCA and BRCA cancer samples. Our
data stimulate efforts in news studies to achieve the experimental and clinical validation about
these biomolecules.
Key-words: clock genes; IGF1; USP2; metabolic syndrome; hypothalamus; cortex; cancer

INTRODUCTION
Tumorigenesis is affected by clock genes. Clock genes control gene expression and cell
cycle being directly involved in the regulation of cell division (Gery et al., 2006; Reddy et al.,
2005), cell proliferation or apoptosis (Hua et al., 2006; Rana et al., 2014), control of cell-cycle
checkpoints (Ben-Shlomo, 2014; Borgs et al., 2009), and response to DNA damage (Fu et al.,
2002; Gery et al., 2006). When these functions are altered, raise cancer development propensity
(Gery and Koeffler, 2010).
Clock genes work as an auto-regulated transcription-translational loop of circadian
genes that drives the circadian rhythms in each cell and they are essential to physiological
requests (Dunlap, 1999; Lowrey and Takahashi, 2011). The circadian proteins are altered in
tumor cells when compared to the adjacent normal cells in several tissues, such as breast tumors
(Chen et al., 2005); endometrial carcinoma (Yeh et al., 2005); and lung cancer (Gery et al.,
2006). Several clock genes may function as oncogenes, such as Arntl2, Nr1d1, and Npas2, while
other clock genes may function as a tumor suppressors, such as Pers, Crys and Rors (Ye et al.,
2016). Once they control de cell cycle, the metabolism is also controlled by the internal
temporal system. In this system, the central oscillator (the suprachiasmatic nucleus of
hypothalamus) orchestrates the peripheral oscillators, which include systems, organs, tissues
and cells.
Since metabolism is a dynamic process, it involves several physiological variables that
circadian cycling. Clinical and experimental studies have demonstrated that clock genes
expression are closely related to metabolic syndrome (Gómez-Abellán et al., 2008; Scott et al.,
2008) and to an increased risk for cardiometabolic disorders (de Oliveira et al., 2019; Golombek
et al., 2013; Karatsoreos et al., 2011). This increased risk includes alterations on the hepatic
insulin pathway, indicative of hepatic insulin resistance (de Oliveira et al., 2019). The gene
expression of pancreatic islet cells (Perelis et al., 2015; Saini et al., 2016) and renninangiotensin system (Herichova et al., 2014; Ohashi et al., 2017) are regulated by the core clock
genes. The clock genes alterations can affect multiple systems concomitantly, since they
constitute promoter factors for type 2 diabetes (Cao and Wang, 2017). We recently published
biological molecules not only represent association of type 2 diabetes and breast, bladder, liver,
pancreas, colon and rectum cancer but also have significant potential to be considered as
systems-level biomarkers that may be used for screening or therapeutic purposes (Pereira et al.,
2019); however, the link between them and clock genes altered expressions are still unknown.

The cellular metabolism relies on circadian control, both within normal or tumor cells,
in this way, the circadian desynchronization can occur at molecular level and/or systemically.
Individuals with shift work are prone to develop cancer and metabolic syndrome. The
International Agency for Research on Cancer of the World Health Organization determined that
shift work at night is the most disruptive for the circadian clock and, for this reason, increase
the tendency to cancer development (Blair et al., 2010). It was classified as “probably
carcinogenic to humans” (International Agency for Research on Cancer, 2019). All these data
converge to the same point: the circadian disruption can lead to the metabolic disorders and
cancer development. We, in the present study, are searching the cross-talk elements to close the
circuit: chrono disruption – cancer – diabetes.

METHODS
Collection and inclusion criteria of studies
We searched the GEO database (https://www.ncbi.nlm.nih.gov/geo/) for publicly
available studies. The inclusion criteria for studies were as follows: (1) samples diagnosed with
type 2 DM and normal samples, (2) experiments that involve circadian rhythm and/or sleeping
analysis and, (3) gene expression profiling of mRNA. After a systematic review, seven gene
expression profiles (GSE56931, GSE20966, GSE25724, GSE26168, GSE23343, GSE29221
and GSE6514) were collected. GSE20966 (20 samples/ 7 female and 13 male) is a human gene
expression profiles of beta-cell enriched tissue obtained by Laser Capture Microdissection from
subjects with type 2 diabetes. GSE25724 (pancreatic islets – 13 samples/ 6 female and 7 male),
GSE26168 (blood – 24 male samples), GSE23343 (liver – 17 samples/ 7 female and 10 male),
GSE29221 (skeletal muscle – 24 male samples) are human expression data from type 2 diabetic
and non-diabetic male and female patients.
Blood draws every 4 hours during a 3-day study: 24-hour normal baseline, 38 hours of
continuous wakefulness and subsequent recovery sleep, for a total of 19 time-points per subject,
with every 2-hr psychomotor vigilance test assessment when awake (GSE56931). GSE6514
sleep deprivation experiments were performed on male mice (C57/BL6), 10 weeks of age ±1
week. Animals were housed in a light/dark cycle of 12:12 h with water available ad libitum.
Animals were subjected to 14 days of acclimatization during which a nighttime feeding pattern
was established. Mice were euthanized following 3, 6, 9 and 12 h of total sleep deprivation.
Deprivation was initiated at lights on and performed through gentle handling.
GSE82113 samples was analyzed under normal, sleep-deprivation and abnormal sleeptiming conditions to assess robustness of the predictor. Blood samples covering at least one
circadian cycle to measure mRNA abundance and measuring melatonin rhythms. We also
performed analysis in peripheral tissue profiles. GSE4239 was used to determine clock
regulated genes in the adrenal gland. Per2Brdm1/Cry1−/− animals was used for expression
analysis. Animals were entrained to a 12 hr light:12 hr dark cycle for two weeks and after
released into constant darkness. Restricted feeding impacts the hepatic circadian clock of Cry1,
Cry2 double KO mice lack a circadian clock was performed by using GSE13062 profile.
Similarly, liver tissue of wildtype, Clock mutant and Cry deficient C57BL/6 8- to 10-week-old
male mice was examined in GSE454 profile. Mice were entrained under 12 h of white light and
12 h of darkness for 14 days. GSE6904 profile showed data from mice were exposed starting
at 1 hour after lights off to a 30-minute light pulse. At the end of the light pulse, mice were

euthanized and the brains extracted. Cells from the suprachiasmatic nucleus were extracted
using laser capture microscopy and gene expression was quantified using Affymetrix
microarrays.
Microarray data and Data processing
GEO2R was applied to screen differentially expressed mRNAs between type control
and experimental tissue samples. GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) is an
interactive web tool for comparing two groups of data that can analyze any GEO series. The pvalues using Benjamin and Hochberg false discovery rate method by default were applied to
correct the occurrence of false positive results.
Cancer expression analyses
Gene list collection, Functional and pathway enrichment analysis, RNA-seq and clinical
information data from TCGA were performed as previously described (Pereira et al., 2019). We
performed survival analysis using Tumor Immune Estimation Resource (TIMER) a web server
(https://cistrome.shinyapps.io/timer/) (Li et al., 2017). Kaplan–Meier for BLCA, BRCA,
COAD, LIHC, PAAD and READ expression profiles plots were drawn to explore the
association between clinical outcome and gene expression. Immunohistochemistry protein data
for normal and cancer samples were performed with the Human Protein Atlas
(https://www.proteinatlas.org/).

RESULTS
Identification of differentially expressed genes, gene ontology enrichment and functional
classification
In an attempt to characterize the molecular signaling events in temporal changes in gene
expression during spontaneous sleep and extended wakefulness, we adopted a dataset of
expression profiling of mRNA from GEO database. This dataset contains the total mRNA of
animals euthanized at different times during the lights on period. The experiments address
temporal changes in gene expression during spontaneous sleep and extended wakefulness in
the mouse cerebral cortex, a neuronal target for processes that control sleep; and the
hypothalamus, an important site of sleep regulatory processes. To identify the differentially
expressed genes (DEGs), we applied the online tool GEO2R and found out upregulated and
downregulated genes.
We performed a GO term enrichment and functional classification by DAVID to
investigate the biological and functional roles of these DEGs. In cortex, we observed
downregulation of cell adhesion molecules (CAMs) after 3h and 6h of sleep deprivation.
Neuroactive ligand-receptor interaction pathway was downregulated after 9h and 12 h of sleep
deprivation. Galactose metabolism pathway and Viral myocarditis, Asthma and, Allograft
rejection pathways were upregulated after 6h and 12h, respectively. According to hypothalamus
analysis, downregulation of Retinol metabolism and Complement and coagulation cascades
pathways were observed after 3h and, neurotrophin signaling pathway, Pathways in cancer and
signaling pathway were the top three pathways downregulated after 6h of sleep deprivation.
Calcium signaling pathway was downregulated after 9h and, colorectal cancer and
Melanogenesis pathways were found after 12h. Phosphatidylinositol signaling system and
oxidative phosphorylation pathways were found active after 3h and 6h, respectively. Active
pathways found after 9h of sleep deprivation were pathways in cancer, maturity onset diabetes
of the young, intestinal immune network for IgA production and natural killer cell mediated
cytotoxicity. Finally, PPAR signaling pathway and cytokine-cytokine receptor interaction were
active after 12h of sleep deprivation.
To analyze the functional classification and to facilitate the high-throughput analysis of
these DEGs, a protein classification analysis according to family and subfamily of the identified
DEGs was performed by the PANTHER classification system. According to the study, after 3h,
6h, 9h and 12h of sleep deprivation in both hypothalamus and cortex samples, we identified
that binding (GO:0005488), catalytic activity (GO:0003824) and molecular function regulator

(GO:0098772) are the top three abundant protein classes.
Data from hepatic tissue of Cry1, 2 double knockout temporally restricted feeding mice
showed Fatty acid biosynthesis and insulin signaling pathways were downregulated when
compared to wildtype mice. Drug and linoleic acid metabolisms were upregulated (GSE13062).
Downregulation of cell cycle, Pathways in cancer, axon guidance, and Wnt signaling was
observed in clock mutant mice. Cry deficient showed downregulation of cell cycle and
neuroactive ligand receptor, while Pathways in cancer was upregulated when compared to wild
type mice (GSE454). Expression data from mice suprachiasmatic nucleus after 30 minutes
light pulse (GSE6904) showed downregulation of ECM-receptor interaction and upregulation
of Mapk signaling, Focal adhesion and Wnt signaling pathways (Supplementary Table 1-16).

Overview of the type 2 diabetes and cancer transcriptomic analysis
We previously characterized the molecular signaling events in the diabetes by using
datasets containing the total mRNA of normal/diabetic pairs of pancreatic islets, blood and
skeletal muscle tissues (Pereira et al., 2019). We applied the online tool GEO2R and found out
upregulated and downregulated genes in isolated human pancreatic islets, liver, blood dataset,
and skeletal muscle. By using Entrez Gene and KEGG pathway analysis, we identified 214
circadian rhythm-related genes. Venn diagram was performed to show the overlap between
DEG genes identified from the meta-analysis and those from the circadian rhythm-related
genes. In diabetic samples, abundant circadian rhythm expression variations were observed
indicating that different gene expression patterns may exist in diabetic tissues (Figure 1).
We obtain the gene expression data of specimens across 6 cancer types from TCGA and
preprocessed the data of each cancer type with standard methods. These cancer types include
Bladder

Urothelial

Cancer

(BLCA),

breast

invasive

carcinoma

(BRCA),

Colon

Adenocarcinoma (COAD), Liver Hepatocellular carcinoma (LIHC), Pancreas adenocarcinoma
(PAAD) and Rectum Adenocarcinoma (READ). We conducted a systematic and integrative
cancer analysis to explore cancer type-specific circadian rhythm subnetworks. Specifically, to
construct a cancer network, we first determine DEGs by comparing expression level of tumors
to normal samples. In tumors abundant circadian rhythm expression variations were observed
indicating that different gene expression patterns may exist in cancer tissues. Klf10 was
downregulated in BLCA, BRCA, LIHC and READ; Ntrk3 and Igf1 were downregulated in
BLCA, BRCA, COAD, LIHC and READ; Similarly, Klf9 and Usp2 downregulation were
found in BLCA, BRCA, COAD and READ. Ezh2, Cdk1, Top2a, Agrp, Aanat and Ren were

upregulated in BLCA, BRCA, COAD and READ, while Arntl2 was upregulated in BLCA,
BRCA, COAD and READ. Klf10 was also found to be downregulated in blood samples from
type 2 diabetic patients. Similarly, Ntrk3 and Igf1 were downregulated in blood and skeletal
samples and, skeletal and liver samples, respectively. Usp2 was observed to be downregulated
in blood samples. Ezh2 was upregulated in blood samples, while Arntl2 and Agrp was
upregulated in skeletal muscle (Figure 1, Supplemetary Table 17-19, respectively).
Survival analysis showed that Igf1 and Usp2 low expression was associated with poor
survival in BLCA, LIHC, AND BRCA and high expression of Arntl2 was associated with poor
survival in LIHC and BRCA samples (Supplementary Figure 1). We found a positive
correlation between Klf10, Ntrk3, Igf1 and Usp2 in almost all cancer samples. The DEG
analyses are supported by immunohistochemistry analysis (Supplementary Figure 2-6, Figure
2-5).
IGF1 and ARNTL2 have been shown to play a role in immune modulation. Then, we
investigate the association of their expression with immune cells in cancer. We found that their
mRNA expressions were negatively associated with tumor purity and positively associated with
infiltration of B Cells, CD8+ T cells, CD4+ T cells, Macrophages, Neutrophils and Dendritic
cells (Supplementary Figure 7 and 8, respectively).

DISCUSSION
Over the last decade, substantial research has been undertaken to understand the
multiple biological processes directed by endogenous clock genes that lead to type 2 DM and
carcinogenesis. Integration of the genome-wide biological data with biomolecular networks is
required to make a clear conclusion on mechanisms for signature of those diseases (Buttar et
al., 2005; Prabhakar et al., 2014). In our previous study associating type 2 DM and cancer, we
observed association of type 2 DM and renin-angiotensin biomarkers with breast, bladder,
liver, pancreas, colon and rectum cancer and also a significant potential to be considered as
systems-level biomarkers that may be used for screening or therapeutic purposes (Pereira et al.,
2019). We are now analyzing those samples according to circadian rhythm-related genes.
The aim of our study is to identify the link between type 2 DM, circadian rhythm-related
genes and cancer by analyzing animal-based research of sleep deprivation, type 2 DM samples
and, BLCA, BRCA, COAD, LIHC, PAAD and READ cancer samples. We performed several
analytical methods to determine the underlying molecular mechanisms. First, we detected
downregulated and upregulated DEGs in mouse cortex and hypothalamus samples. The authors
determine the changes by comparing expression in sleeping animals euthanized at different
times during the lights on period, to that in animals sleep deprived and euthanized at the same
diurnal time. In summary, upregulated genes active genes associated with oxidative
phosphorylation, cancer and diabetes, mainly in hypothalamus specimens. In cortex, we
observed mainly downregulation of immune system.
In mammals, light information is perceived by the retina and transmitted to the
suprachiasmatic nuclei through the retinohypothalamic tract. The projections of the
suprachiasmatic nuclei, have at least four neuronal targets: endocrine neurons, autonomic
neurons of the paraventricular nucleus of the hypothalamus, other hypothalamic structures, and
areas outside the hypothalamus (Bozek et al., 2009; de Oliveira et al., 2019). These efferent
pathways are able to synchronize peripheral clocks, controlling various physiological functions,
such as hormone releasing, food behavior and temperature fluctuations. It has been shown that
shift work promotes sleep disturbances (Zhu and Zee, 2012). These disturbing external signals
induce loss of coherence between the central oscillator and the peripherals and can lead to
diseases that characterize the internal desynchronization: insomnia, cardiovascular disorders,
obesity, depression, diabetes, dysregulation of metabolic rhythms and endocrine and even
cancer. Several reports have revealed that clock genes are found to be deregulated in several
cancer types. In comparison with surrounding non-cancerous cells, breast cancer cells reveal

disturbances in the expression of the clock genes attributable to methylation, which is associated
with decreased gene expression (Basudhar et al., 2019; Soták et al., 2013; Zhu and Zee, 2012).
We first propose that sleeping disturbance in animal models could promote type 2 DM
and cancer development, by hypothalamus and cortex gene expression deregulation. Next,
DEGs were combined with 214 circadian rhythm related genes to type 2 DM and cancer
samples. We observed that several common genes deregulated in both diseases. Ntkr3, Igf1 and
Usp2 were both downregulated in type 2 DM and cancer samples, while Arntl2 and Agrp were
upregulated. It seems that the changes in mRNA are contributing to the phenotypic changes in
type 2 DM, resulting in phenotypic changes associated with the malignant transformation.
Taking those genes to perform a survival analysis, we found only Igf1, Usp2 and Arntl2 genes
associated with patient outcomes. While Igf1 and Usp2 downregulation had a negative impact,
Arntl2 upregulation was associated with poor survival both in BLCA and BRCA cancer
samples.
IGF1 is a mitogen for a variety of cells and exerts this action through the MAP kinase
signaling pathway by increasing DNA synthesis and stimulating the expression of cyclin D1,
which accelerates progression of the cell cycle from the G1 to S phase. The circulating IGF1
and IGF2 bind to IGF1 receptors (IGF1R) and trigger a signal transduction cascade (Shi et al.,
2014). This signaling is very critical for the processes of oncogenesis. IGF1R activity is
mediated by the Ras and AKT pathways and results in upregulation of cyclin D1 and its CDK4
linker, leading to retinoblastoma protein phosphorylation, release of the E2F transcription factor
and expression of downstream target genes such as cyclin E. Plasma IGF1 level changes during
the day, suggesting some circadian control, but molecular mechanisms are not clear. In our
study, we observed downregulation of Igf1 mRNA expression in several cancer types.
However, protein analysis showed higher levels of IGF1 in cancer compared to normal samples.
A previous study analyzing around-the-clock blood sampling, showed that both healthy and
cancer individuals were found to be similarly synchronized to the 24-h sleep-wake schedule.
However, growth hormone (GH)-IGF-l axis function, cortisol secretion and IL-2 serum levels
were altered in cancer patients. In cancer patients there was an increasing trend and progressive
loss of circadian rhythmicity of GH secretion, accompanied by a decreasing of IGF-1 serum
levels, upregulation of cortisol secretion and IL-2 serum levels. It has been well established that
IGF1 promotes cancer cell proliferation, migration and metastasis. IGF1 has been shown to be
controlled by CRY1/2 proteins. Cry2 mRNA was downregulated in BLCA and BRCA samples
in our study. However, we observed higher CRY2 protein expression in cancer samples. This
data suggests that CRY2 could control IGF1 levels and these alterations may be related to the

process of carcinogenesis and, could favor cancer progression.
The deubiquitinating enzyme Ubiquitin Specific Protease 2 (USP2) is highly dependent
on the circadian cycle, showing biological clock oscillations(Yang et al., 2012). It can be
expressed under two isoforms, Usp2a and Usp2b. Usp2b has been shown having oscillations
more dependent on circadian regulation. This enzyme has been reported as a stabilizer of Bmal1
gene (Scoma et al., 2011), and acts on the ubiquitination of Per1, a central gene, essential for
the production of circadian behavioral rhythms. Animal models have shown that total inhibition
of USP2 can compromise the rhythm circadian pathway thereby altering several other genes
dependent on these biological oscillations. USP2 directly affects the levels of deubiquitinating
proteins such as MDM2, cyclin D1 and CRY1 and their substrates, conferring resistance to
apoptosis in mutated cells, acting as an oncogene. Since USP2 is the only enzyme capable of
deubiquitinating cyclin D1 in human cells, cyclin D1 is reported as the most effective substrate
for USP2.
Taking into account that several diseases can be triggered by circadian rhythm
dysregulation, among them we can highlight diabetes and cancer (Renehan et al., 2012). The
role of Usp2 expression would be to induce prolonged fasting, occurring at the end of the clear
phase of the circadian rhythm, thus stimulating the occurrence of hepatic gluconeogenesis. In
addition, USP2 is required to maintain glucose homeostasis and regulate glucose tolerance
through glucocorticoid signaling. Both circadian and USP2 deregulations accelerate the process
of hepatic gluconeogenesis by increasing glucose secretion. Experiments have shown that a
deregulated rhythm changes serum glucose levels, the development associated with
glucocorticoid receptors. Excessive activity in glucocorticoid signaling may lead to the
development of glucose intolerance. We suggest that changes in circadian rhythm and USP2
levels may be associated with the initiation and progression of type 2 diabetes, leading to cancer
development. We also found that Igf1 and Usp2 expressions were negatively associated with
tumor purity and positively associated with infiltration of B Cells, CD8+ T cells, CD4+ T cells,
Macrophages, Neutrophils and Dendritic cells. Besides, TIMER analysis confirms that those
genes overexpression are positively associated with macrophage population. The results show
that these genes are playing an important role in immune modulation.
Taking all data together, we hypothesized that changes in the expression of circadian
rhythm related-genes in type 2 DM and cancer could be responsible for metabolic changes that
could lead to the cancer development. Such shifts in tissue metabolism results, at least in part,
from profound recruitment of inflammatory cell types, particularly myeloid cells, such as
neutrophils and monocytes. Expression of Igf1 and Usp2 may be associated with the immune

system in both the diabetes and cancer samples. These disturbances may lead to alterations in
immunity by enhancing the infiltration of leukocytes and increased expression of proinflammatory cytokines. These diverse cells communicate with each other by means of direct
contact or by cytokine and chemokine production and act in autocrine and paracrine manners
to control and shape tumor growth and metastasis. Interestingly, we observed lower expression
of ARNTL2 protein in cancer samples. This protein plays an important role in circadian rhythm
system and it has been shown to be associated with immune escape mechanism. Besides,
previous results have demonstrated that Arntl2, in association with Nr1d1 and Npas2 may
function as an oncogene. This indicates that the altered expression levels of those genes could
be associated with cancer progression and aggressive metastatic phenotype.
These biological molecules not only represent the association of type 2 DM and
circadian rhythm biomarkers with breast, bladder, liver, pancreas, colon and rectum cancer but
also have significant potential to be considered as systems-level biomarkers that may be used
for screening or therapeutic purposes. Our data stimulate efforts in news studies to achieve the
experimental and clinical validation about these biomolecules.
Competing interests
The authors declare that they have no competing interests.
Ethics approval and consent to participate
Not applicable.

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Figure 1 -Venn Diagrams of differentially expressed genes in mRNA expression profiling
datasets. We are showing downregulated (A) and upregulated (B) genes in human Beta Cells
(GSE20966 and GSE25724), blood (GSE56931 and GSE26168), skeletal muscle (GSE29221),
and liver (GSE23343). We are also showing downregulated and upregulated cancer samples (C
and D, respectively) combined with clock gene list. The number in each intersecting region
represents the number of overlapping genes. Volcano plots of differentially genes expression
in breast invasive carcinoma (E), Colon Adenocarcinoma (F)Liver Hepatocellular carcinoma
(G), Pancreas adenocarcinoma (H) and Rectum Adenocarcinoma (I). Red: up-regulation; green:
down-regulation. Klf10 (ENSG00000155090) was downregulated in BLCA, BRCA, LIHC and
READ; Ntrk3 (ENSG00000140538) and Igf1 (ENSG00000017427) were downregulated in
BLCA, BRCA, COAD, LIHC and READ; Similarly, Klf9 (ENSG00000119138) and Usp2
(ENSG00000036672) downregulation were found in BLCA, BRCA, COAD and READ. Ezh2
(ENSG00000106462), Cdk1 (ENSG00000170312), Top2a (ENSG00000131747), Agrp
(ENSG00000159723), Aanat (ENSG00000129673) and Ren (ENSG00000143839) were
upregulated in BLCA, BRCA, COAD and READ, while Arntl2 (ENSG00000029153) was
upregulated in BLCA, BRCA, COAD and READ.

Fugure 2 - Representative immunohistochemical staining characteristics of ARNTL2
expressions in normal and cancer patients. Data are extracted from The Human Protein Atlas
(https://www.proteinatlas.org/).

Figure 3 - Representative immunohistochemical staining characteristics of CRY2 expressions in normal
and cancer patients. Data are extracted from The Human Protein Atlas (https://www.proteinatlas.org/).

Figure 4 - Representative immunohistochemical staining characteristics of IGF1 expressions in normal
and cancer patients. Data are extracted from The Human Protein Atlas (https://www.proteinatlas.org/).

Figure 5 - Representative immunohistochemical staining characteristics of USP2 expressions in normal
and cancer patients. Data are extracted from The Human Protein Atlas (https://www.proteinatlas.org/).

Supplementary Figure 1 - Kaplan-Meier analysis of Klf10, Ppargc1a, Prkaa2, Per3, Igf1, Klf9, Usp2, Vip, Mapk10, Prokr1 and nr3c1 in Bladder Urothelial
Cancer (BLCA), breast invasive carcinoma (BRCA), Colon Adenocarcinoma (COAD), Liver Hepatocellular carcinoma (LIHC), Pancreas adenocarcinoma
(PAAD) and Rectum Adenocarcinoma (READ). Data are extracted from TIMER web server. P < 0,05 was considered statistically significant.

-- High (Top 50%)
--_Low (Bottom 50%

Supplementary Figure 2 - Correlation between Klf10, Ntrk3, Igf1 and Usp2 in Bladder Urothelial
Cancer (BLCA). Data are extracted from TIMER web server. P < 0,05 was considered statistically
significant.

Supplementary Figure 3 - Correlation between Klf10, Ntrk3, Igf1 and Usp2 in Colon Adenocarcinoma
(COAD). Data are extracted from TIMER web server. P < 0,05 was considered statistically significant.

Supplementary Figure 4 - Correlation between Klf10, Ntrk3, Igf1 and Usp2 in Pancreas
adenocarcinoma (PAAD). Data are extracted from TIMER web server. P < 0,05 was considered
statistically significant.

Supplementary Figure 5 - Correlation between Klf10, Ntrk3, Igf1 and Usp2 in Liver Hepatocellular
carcinoma (LIHC). Data are extracted from TIMER web server. P < 0,05 was considered statistically
significant.

Supplementary Figure 6 - Correlation between Klf10, Ntrk3, Igf1 and Usp2 in Rectum
Adenocarcinoma (READ). Data are extracted from TIMER web server. P < 0,05 was considered
statistically significant.

Supplementary Figure 7 - Correlation of IGF1 expression with immune infiltration level in in Bladder
Urothelial Cancer (BLCA), breast invasive carcinoma (BRCA), Colon Adenocarcinoma (COAD), Liver
Hepatocellular carcinoma (LIHC), Pancreas adenocarcinoma (PAAD) and Rectum Adenocarcinoma
(READ). The scatterplots are generated and displayed after inputs are submitted successfully, showing
the purity-corrected partial Spearman’s correlation and statistical significance. Data are extracted from
TIMER web server. P < 0,05 was considered statistically significant.

Supplementary Figure 8 - Correlation of ARNTL2 expression with immune infiltration level in in
Bladder Urothelial Cancer (BLCA), breast invasive carcinoma (BRCA), Colon Adenocarcinoma
(COAD), Liver Hepatocellular carcinoma (LIHC), Pancreas adenocarcinoma (PAAD) and Rectum
Adenocarcinoma (READ). The scatterplots are generated and displayed after inputs are submitted
successfully, showing the purity-corrected partial Spearman’s correlation and statistical significance.
Data are extracted from TIMER web server. P < 0,05 was considered statistically significant.

Supplementary Table 1 - Functional annotation analysis of downregulated differentially expressed
genes in hypothalamus (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized
following 3 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu00830:Retin
ol metabolism
mmu04610:
Complement and
coagulation
cascades
mmu04310:Wnt
signaling
pathway

PValue
Genes
0.0269414357 RDH12,
3768382
ALDH1A2,
CYP26B1,
RDH16
0.0346466248 F13B,
63555325
C5AR1,
FGA,
BDKRB2
0.0536675151 CSNK1A1,
0526847
NKD2,
PRICKLE1,
PPP2CB,
FZD3

Supplementary Table 2 - Functional annotation analysis of upregulated differentially expressed genes
in hypothalamus (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized
following 3 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu04070:Phosphatidylinositol
signaling system
mmu04512:ECM-receptor
interaction
mmu04020:Calcium signaling
pathway
mmu05222: Small cell lung cancer
mmu04510:Focal adhesion
mmu05200:Pathways in câncer

PValue
Genes
0.011670372 PRKCA, DGKG,
824607887
PLCD3, PLCD4,
ITPR3
0.074857973 ITGA9, TNC, ITGB1,
33737988
COL4A6
0.075004981 PRKCA, EDNRA, ATP2A2,
14905663
PLCD3, PLCD4, ITPR3
0.079162015 TRAF1, ITGB1, TRAF4,
86803795
COL4A6
0.084543745 PRKCA, ITGA9, TNC, FLNC,
6343312
ITGB1, COL4A6
0.086267420 TRAF1, PRKCA, FGF14,
06122387
NKX3-1, ITGB1, TRAF4,
STAT3, COL4A6

Supplementary Table 3 - Functional annotation analysis of downregulated differentially expressed
genes in hypothalamus (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized
following 6 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu04722:Ne
urotrophin
signaling
pathway

PValue
Genes
1,70E-06 IRS4, TRP53, PIK3CD, MAPK10, KIDINS220,
IRAK4, NTRK3, IRAK3, CRKL, RPS6KA4, CAMK4,
RPS6KA2, SOS1, MAPK14, NTRK2, CAMK2D,
MAPK8, IKBKB, CAMK2A, MAP2K7, PIK3R1,
AKT3
mmu05200:Pat
1,14E-05 BID, FGFR1, XIAP, APC2, STK36, STAT5B,
hways in
BCL2L1, KIT, CTNNB1, TPM3, WNT1, SOS1,
cancer
ITGAV, TPR, PIK3R1, AKT3, APC, DVL2, TRP53,
BCR, PIK3CD, FZD1, SKP2, ITGA3, MAPK10,
CTNNA3, FZD6, WNT7B, FZD10, CRKL, PIAS3,
PDGFRA, PDGFRB, MDM2, MAPK8, IKBKB
mmu04310:Wn 1,56E-05 TRP53, DVL2, TBL1XR1, APC2, BTRC, FZD1,
t signaling
MAPK10, FZD6, CTNNB1, CSNK2A2, MAP3K7,
pathway
WNT1, FZD10, WNT7B, PLCB4, CCND2, NFAT5,
CAMK2D, MAPK8, PRKACB, CAMK2A, APC
mmu04910:Ins
1,64E-05 IRS4, PHKA1, PIK3CD, PRKAB2, MKNK2, HK1,
ulin signaling
MAPK10, PRKAR2B, PRKAR2A, PPP1R3C, CRKL,
pathway
INPP5K, SLC2A4, GCK, SOS1, PRKAR1B, MAPK8,
PRKACB, IKBKB, AKT3, PIK3R1
mmu05210:Col 2,10E-05 TRP53, DVL2, APC2, PIK3CD, FZD1, MAPK10,
orectal cancer
FZD6, CTNNB1, FZD10, SOS1, PDGFRA, PDGFRB,
MAPK8, AKT3, PIK3R1, APC
mmu04930:Ty
2,37E-05 IRS4, GCK, SLC2A4, PIK3CD, HK1, MAPK8,
pe II diabetes
MAPK10, PRKCE, IKBKB, KCNJ11, PIK3R1,
mellitus
CACNA1B
mmu04210:Ap
2,42E-05 BID, TRP53, XIAP, PIK3CD, BCL2L1, IRAK4,
optosis
PRKAR2B, IRAK3, PRKAR2A, PRKAR1B, APAF1,
PRKACB, IKBKB, AKT3, IL3RA, PIK3R1
mmu04010:M
1,41E-04 FGFR1, MKNK2, PPM1B, MAP3K7, SOS1,
APK signaling
PRKACB, MAP2K7, AKT3, TRP53, CACNA2D1,
pathway
CACNG3, MAPK10, FLNB, STK3, DDIT3,
RPS6KA4, CRKL, RASGRF2, RPS6KA2, MAPK14,
NTRK2, PDGFRA, MAPK8IP3, PDGFRB, MAPK8,
IKBKB, DUSP8, PPP5C, CACNA1B
mmu05217:Bas 3,59E-04 DVL2, TRP53, WNT1, FZD10, WNT7B, APC2,
al cell
STK36, FZD1, FZD6, CTNNB1, APC
carcinoma
mmu04916:Me
4,48E-04 DVL2, ADCY3, ADCY7, FZD1, KIT, FZD6,
lanogenesis
CTNNB1, WNT1, FZD10, WNT7B, PLCB4,
CAMK2D, GNAS, PRKACB, CAMK2A
mmu04062:Ch
0,001953 ADCY3, PARD3, ADCY7, PIK3CD, STAT5B,
emokine
58093342 CX3CL1, ELMO1, CCR9, CCL25, CRKL, PLCB4,
9

signaling
pathway
mmu04912:Gn
RH signaling
pathway
mmu04120:Ubi
quitin mediated
proteolysis
mmu05222:
Small cell lung
cancer
mmu04070:Ph
osphatidylinosi
tol signaling
system
mmu05213:En
dometrial
cancer
mmu05214:Gli
oma
mmu05220:Chr
onic myeloid
leukemia
mmu04520:Ad
herens junction
mmu04510:Foc
al adhesion
mmu04914:
Progesteronemediated
oocyte
maturation
mmu04620:
Toll-like
receptor
signaling
pathway
mmu04540:Ga
p junction
mmu04012:Erb
B signaling
pathway
mmu04810:
Regulation of

TIAM1, SOS1, CX3CR1, CCR2, GNG2, PRKACB,
IKBKB, AKT3, PIK3R1
ADCY3, PLD1, ADCY7, MAPK10, PLCB4,
MAPK14, SOS1, CAMK2D, MAPK8, GNAS,
PRKACB, CAMK2A, MAP2K7
XIAP, UBE4A, BTRC, SKP2, KEAP1, UBE2I,
UBE2H, UBE2B, CDC27, MGRN1, PIAS3, UBE2K,
NEDD4, MDM2, SMURF1, TRIP12
TRP53, XIAP, PIAS3, ITGAV, PIK3CD, SKP2,
ITGA3, BCL2L1, APAF1, IKBKB, PIK3R1, AKT3

0,003324
67696788
8
0,003386
64678245
7
0,003421
47353034
9
0,004162 PLCB4, DGKB, INPP5K, PIK3C2A, DGKG,
53760426 PIK3CD, PIKFYVE, PIP5K1C, SYNJ2, PIK3R1,
6 PIP4K2C
0,004166
74275270
8
0,004506
66168915
7
0,004584
85955340
4
0,004584
85955340
4
0,005041
25667864

TRP53, APC2, SOS1, PIK3CD, PIK3R1, CTNNA3,
AKT3, CTNNB1, APC
TRP53, SOS1, PIK3CD, CAMK2D, PDGFRA,
PDGFRB, MDM2, CAMK2A, PIK3R1, AKT3
TRP53, BCR, CRKL, SOS1, STAT5B, PIK3CD,
MDM2, BCL2L1, IKBKB, PIK3R1, AKT3
PTPRB, CSNK2A2, MAP3K7, FGFR1, PARD3,
WASF3, SSX2IP, YES1, CTNNA3, CTNNB1, FARP2

XIAP, TLN2, PIK3CD, PIP5K1C, ITGA3, MAPK10,
FLNB, CTNNB1, CRKL, CCND2, ITGA5, SOS1,
ITGAV, PDGFRA, PDGFRB, MAPK8, AKT3,
PARVB, PIK3R1, PARVA
0,010085 ADCY3, ADCY7, RPS6KA2, MAPK14, PIK3CD,
21196957 MAPK8, MAPK10, PRKACB, CDC27, PIK3R1,
7 AKT3
0,010820 IRAK4, MAP3K7, IFNAR2, TOLLIP, MAPK14,
21430612 PIK3CD, MAPK8, MAPK10, IKBKB, MAP2K7,
6 PIK3R1, AKT3
0,010918
59030232
7
0,011803
26535751
9
0,013078
79643093
9

ADCY3, PLCB4, ADCY7, CSNK1D, SOS1,
PDGFRA, PDGFRB, GNAS, GUCY1B3, PRKACB,
HTR2C
CRKL, SOS1, STAT5B, PIK3CD, CAMK2D,
MAPK8, MAPK10, MAP2K7, CAMK2A, PIK3R1,
AKT3
GNA13, FGFR1, APC2, SSH1, PIK3CD, NCKAP1L,
PIP5K1C, ITGA3, ITGAM, CRKL, ITGA5, TIAM1,

actin
cytoskeleton
mmu05215:Pro
state cancer
mmu04020:Cal
cium signaling
pathway
mmu05414:Dil
ated
cardiomyopath
y
mmu00562:Ino
sitol phosphate
metabolism
mmu02010:AB
C transporters
mmu04144:En
docytosis
mmu05212:Pan
creatic cancer
mmu04660: T
cell receptor
signaling
pathway
mmu04130:
SNARE
interactions in
vesicular
transport
mmu04630:
Jak-STAT
signaling
pathway
mmu04664: Fc
epsilon RI
signaling
pathway
mmu04666: Fc
gamma Rmediated
phagocytosis
mmu05218:Me
lanoma

SOS1, ITGAV, PIKFYVE, PDGFRA, PDGFRB,
PIP4K2C, PIK3R1, APC
0,014783 TRP53, FGFR1, SOS1, PIK3CD, PDGFRA, PDGFRB,
94290592 MDM2, IKBKB, PIK3R1, AKT3, CTNNB1
3
0,015843 SLC8A3, ADCY3, TRPC1, ADCY7, PHKA1, PLCB4,
97571717 CAMK4, ATP2A2, ATP2A3, PDE1C, CAMK2D,
5 PDGFRA, PDGFRB, GNAS, PRKACB, HTR2C,
CAMK2A, CACNA1B
0,017062 ADCY3, CACNA2D1, ATP2A2, ADCY7, ITGA5,
03690947 ITGAV, GNAS, ITGA3, CACNG3, PRKACB, TPM3
4
0,018032 PLCB4, INPP5K, PIK3C2A, PIK3CD, PIKFYVE,
55562798 PIP5K1C, SYNJ2, PIP4K2C
0,024479
43860889
4
0,025955
94320513
8

ABCB8, ABCB1B, ABCC10, ABCC12, ABCA1,
ABCB7, ABCC6

PLD1, PARD3, DNM1L, LDLR, STAM2, PIP5K1C,
KIT, RAB11FIP4, RAB11FIP3, TFRC, ACAP3,
NEDD4, PIKFYVE, PDGFRA, DNAJC6, MDM2,
SMURF1, RAB11FIP1
0,027838 TRP53, PLD1, PIK3CD, MAPK8, BCL2L1,
02150986 MAPK10, IKBKB, PIK3R1, AKT3
8
0,035403 MAP3K7, BCL10, CD40LG, SOS1, MAPK14,
43213552 PIK3CD, NFAT5, IKBKB, MAP2K7, PIK3R1, AKT3,
3 DLG1
0,040755 STX17, GOSR2, VAMP3, VAMP2, GOSR1, STX1B
05517889
1
0,042967 STAM2, PIK3CD, STAT5B, BCL2L1, IL7R,
08372493 IFNAR2, PRLR, PIAS3, CCND2, SOS1, IL13RA1,
9 PIK3R1, AKT3, IL3RA
0,054010 SOS1, MAPK14, PIK3CD, MAPK8, MAPK10,
54951779 PRKCE, MAP2K7, PIK3R1, AKT3
0,058455 PLD1, CRKL, DNM1L, WASF3, PIK3CD, PIKFYVE,
97437169 PIP5K1C, PRKCE, PIK3R1, AKT3
3
0,065976 TRP53, FGFR1, PIK3CD, PDGFRA, PDGFRB,
65686755 MDM2, PIK3R1, AKT3
9

mmu05221:Ac
ute myeloid
leukemia
mmu05412:
Arrhythmogeni
c right
ventricular
cardiomyopath
y (ARVC)

0,066302
38935643
4
0,083302
61152267
4

SOS1, STAT5B, PIK3CD, KIT, IKBKB, PIK3R1,
AKT3
CACNA2D1, ATP2A2, ITGA5, ITGAV, ITGA3,
CACNG3, CTNNA3, CTNNB1

Supplementary Table 4 - Functional annotation analysis of upregulated differentially expressed genes
in hypothalamus (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized
following 6 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu00190:Oxidative
phosphorylation
mmu05012:Parkinson's disease

mmu05016:Huntington's disease

mmu05010:Alzheimer's disease

mmu04260:Cardiac muscle
contraction
mmu03010:Ribosome
mmu00410:beta-Alanine
metabolismo

PValue
Genes
4,69E-07 NDUFA4, NDUFA5, ATP5E, ATP5J2,
NDUFA3, COX7A2, COX8B, ATP4B,
COX6C, UQCR11, UQCRH, COX6A2,
ATP5L, ATP5O, ATP5K, UQCRB
8,51E-05 NDUFA4, NDUFA5, ATP5E,
COX7A2, NDUFA3, COX8B, COX6C,
UQCR11, CASP9, UQCRH, COX6A2,
ATP5O, UQCRB
1,32E-04 NDUFA4, NDUFA5, ATP5E,
NDUFA3, POLR2E, COX7A2,
COX8B, COX6C, UQCR11, CASP9,
UQCRH, COX6A2, TGM2, ATP5O,
UQCRB
4,53E-04 NDUFA4, NDUFA5, ATP5E,
COX7A2, NDUFA3, COX8B, COX6C,
UQCR11, CASP9, UQCRH, BACE2,
COX6A2, ATP5O, UQCRB
0,002760 FXYD2, UQCR11, COX7A2, COX8B,
52945879 UQCRH, COX6A2, COX6C, UQCRB
4
0,065553 RPL41, RPL35, RPL27, RPS27L,
36507805 RPL37, RPS24
8
0,099424 ALDH2, DPYS, AOC3
52102923
5

Supplementary Table 5 - Functional annotation analysis of downregulated differentially expressed
genes in hypothalamus (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized
following 9 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu04020:Calcium signaling pathway
mmu04010:MAPK signaling pathway

PValue
Genes
0,045568518 EGFR, PLCZ1, P2RX1,
049453 CACNA1H, PLCD1, IGHVJ558
0,053398346 EGFR, MAP3K7, RPS6KA3,
139521 RELB, CACNA1H, MAP3K14,
FGF3

Supplementary Table 6 - Functional annotation analysis of upregulated differentially expressed genes
in hypothalamus (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized
following 9 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
PValue
Genes
mmu05200:Pathways in cancer 0,013810995 TCF7, CYCT, SKP2, FZD1, FZD3,
520305 ZBTB16, CBLC, LAMA3, ITGAV,
PAX8, PTCH1, IKBKB, FGF2,
MMP1B
mmu04950: Maturity onset
0,015265675 HNF1B, IAPP, HNF4G, PDX1
diabetes of the young
716387
mmu04672: Intestinal immune 0,023738635 CCR9, IGHG, CD80, H2-DMB2,
network for IgA production
669089 IL2
mmu04650: Natural killer cell
0,037758267 KLRA16, CD48, IGHG, CD244,
mediated cytotoxicity
809113 TNFRSF10B, KLRA7, VAV2
mmu05016:Huntington's
0,079202055 SLC25A31, NDUFS4, CYCT,
disease
043023 CREB1, CYP4A31, NDUFC2,
PLCB2, POLR2A
mmu04330:Notch signaling
0,081200303 NOTCH2, NOTCH4, NUMB,
pathway
679975 LFNG
mmu04514: Cell adhesion
0,093775924 SELP, SIGLEC1, CD80, ITGAV,
molecules (CAMs)
821431 NLGN1, CD2, H2-DMB2
mmu04020:Calcium signaling
0,094298620 IGHG, SLC25A31, ERBB4,
pathway
512485 PDE1C, PLCD4, PLCB2, PTAFR,
CACNA1A
mmu05222: Small cell lung
0,094733504 LAMA3, CYCT, ITGAV, SKP2,
cancer
181432 IKBKB

Supplementary Table 7 - Functional annotation analysis of downregulated differentially expressed
genes in hypothalamus (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized
following 12 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu05210:Colorectal cancer

mmu04916:Melanogenesis

mmu05217:Basal cell carcinoma
mmu04310:Wnt signaling pathway

PValue
Genes
0,0245145411 DVL2,
11056 DVL3,
MSH2,
PDGFRB
0,0361108994 DVL2,
48509 WNT1,
DVL3,
POMC
0,0608645321 DVL2,
83204 WNT1,
DVL3
0,0945999234 DKK2,
20952 DVL2,
WNT1,
DVL3

Supplementary Table 8 - Functional annotation analysis of upregulated differentially expressed genes
in hypothalamus (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized
following 12 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu03320: PPAR
signaling pathway
mmu04060:
Cytokine-cytokine
receptor
interaction
mmu04950:
Maturity onset
diabetes of the
young

PValue
0,0324879268
31281
0,0327089562
67818

Genes
LPL, FABP2, CYP4A14, MMP1B, PCK1
CSF2, INHBA, TNFRSF9, IL22RA1, IL5,
PRLR, MET, TNFRSF14, HGF

0,0595843084 HNF4A, HNF4G, NR5A2
47436

Supplementary Table 9 - Functional annotation analysis of downregulated differentially expressed
genes in cortex (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized following
3 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu04514: Cell adhesion
molecules (CAMs)
mmu00480:Glutathione
metabolismo
mmu00562:Inositol phosphate
metabolismo
mmu04020:Calcium signaling
pathway

PValue
0,0332646588
79409
0,0475189510
45075
0,0521672395
72602
0,0784822409
08623

Genes
H2-K1, CLDN3, H2-D1, CD4,
VCAN, ITGB1, SELE
GSTA2, GSTA3, GPX3, MGST1
PIK3CG, MIOX, PLCD1, PI4KB
GNA14, P2RX1, ADORA2A,
ADRA1B, AVPR1A, PLCD1,
GNAS

Supplementary Table 10 - Functional annotation analysis of upregulated differentially expressed genes
in cortex (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized following 3 hrs
of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu04010:MAPK signaling
pathway

PValue
Genes
0,0692979688 MAP4K3, DUSP4, RASGRF2,
18719 FGF17, HSPB1, HSPA1B, FLNC

Supplementary Table 11 - Functional annotation analysis of downregulated differentially expressed
genes in cortex (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized following
6 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu04514: Cell adhesion molecules
(CAMs)
mmu00983:Drug metabolismo
mmu05416:Viral myocarditis
mmu00980: Metabolism of
xenobiotics by cytochrome P450
mmu00140:Steroid hormone
biosynthesis
mmu00982:Drug metabolismo
mmu04610: Complement and
coagulation cascades
mmu00563:
Glycosylphosphatidylinositol (GPI)anchor biosynthesis
mmu04672: Intestinal immune
network for IgA production
mmu05332: Graft-versus-host
disease

PValue
Genes
0,0086273913 SDC1, H2-Q10, CD80, H2-D1,
23092 CD2, VCAN, MADCAM1,
ITGA4, ITGB1
0,0126226927 CYP3A25, UGT2B5, UPP2,
3448 DPYD, UGT2A3
0,0326061736 BID, H2-Q10, CD80, CYCT,
70026 H2-D1, MYH4
0,0361197691 CYP3A25, UGT2B5,
05627 UGT2A3, CYP2C50, MGST2
0,0532089649 CYP3A25, UGT2B5,
93145 UGT2A3, CYP19A1
0,0536001794 CYP3A25, UGT2B5,
65743 UGT2A3, CYP2C50, MGST2
0,0536001794 F11, FGG, FGA, SERPINA1B,
65743 C3
0,0808295544 PIGB, PIGU, PIGN
93124
0,0824484967
50621
0,0972505016
23575

CCR9, CD80, MADCAM1,
ITGA4
H2-Q10, CD80, H2-D1,
KLRA7

Supplementary Table 12 - Functional annotation analysis of upregulated differentially expressed genes
in cortex (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized following 6 hrs
of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu00052:Galactos
e metabolism

PValue
Genes
0,0349193235 G6PC, GLA, HK3
07812

Supplementary Table 13 - Functional annotation analysis of downregulated differentially expressed
genes in cortex (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized following
9 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu05330:Allograft rejection
mmu04650:Natural killer cell
mediated cytotoxicity
mmu05310:Asthma
mmu04080:Neuroactive ligandreceptor interaction
mmu05320:Autoimmune thyroid
disease
mmu04640:Hematopoietic cell
lineage
mmu04672:Intestinal immune
network for IgA production
mmu05416:Viral myocarditis
mmu04020:Calcium signaling
pathway

PValue
0,0151323158
98876
0,0152433207
62071
0,0167515606
25554
0,0274243255
73514
0,0308333461
82776
0,0498677160
5983
0,0595032913
24067
0,0697171118
10456
0,0959357401
11107

Genes
IGHG, CD80, H2-OB, IGHVJ558, H2-T3
IGHG, H60A, PTPN6, CSF2,
CD247, NFAT5, IGH-VJ558
IGHG, IL3, H2-OB, IGHVJ558
P2RX5, ADRB3, P2RY10,
P2RY6, ADORA2B,
AGTR1B, RXFP1, FPR3,
NMBR, GH
IGHG, CD80, H2-OB, IGHVJ558, H2-T3
IGHG, CSF2, IL3, DNTT,
IGH-VJ558
IGHG, CD80, H2-OB, IGHVJ558
IGHG, CD80, H2-OB, IGHVJ558, H2-T3
P2RX5, IGHG, ADRB3,
ADORA2B, AGTR1B,
CACNA1A, IGH-VJ558

Supplementary Table 14 - Functional annotation analysis of upregulated differentially expressed genes
in cortex (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized following 9 hrs
of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu04060:C
ytokinecytokine
receptor
interaction

PValue
Genes
0,0948137645 CCR9, INHBA, TNFRSF25, CXCL15, TNFRSF14
13206

Supplementary Table 15 - Functional annotation analysis of downregulated differentially expressed
genes in cortex (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized following
12 hrs of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
PValue
Genes
mmu04080:Neuroac 0,0164536250 GALR1, PRLR, TACR3, PRSS2, DRD4,
tive ligand-receptor
84603 VIPR1, PTAFR, GH
interaction

Supplementary Table 16- Functional annotation analysis of upregulated differentially expressed genes
in cortex (GSE6514) intersected datasets using the DAVID tool. Mice were euthanized following 12 hrs
of total sleep deprivation. P < 0,05 was considered statistically significant.

Term
mmu05416:Viral myocarditis
mmu05310:Asthma
mmu05330:Allograft rejection
mmu05320:Autoimmune
thyroid disease

PValue
0,0096939424
63592
0,0105400029
41723
0,0465710277
14215
0,0785341939
61941

Genes
CYCT, MYH2, H2-D1, H2-AA,
H2-DMA, IGH-VJ558
H2-AA, MS4A2, H2-DMA, IGHVJ558
H2-D1, H2-AA, H2-DMA, IGHVJ558
H2-D1, H2-AA, H2-DMA, IGHVJ558

5.2 PRODUTO 2
Patente: Privilégio de Inovação. Número do registro: BR10202002668, título: "Painel genético
para diagnóstico e prognóstico do câncer de mama”, Instituição de registro: INPI - Instituto
Nacional da Propriedade Industrial. Depósito: 24/12/2020

5 CONCLUSÕES
Compilando todos os dados, a hipótese aventada foi de que mudanças na expressão de
genes relacionados ao ritmo circadiano no diabetes mellitus tipo 2 e no câncer podem ser
responsáveis por mudanças metabólicas que podem levar ao desenvolvimento do câncer. Essas
mudanças no metabolismo do tecido resultam, pelo menos em parte, do recrutamento profundo
de tipos de células inflamatórias, particularmente células mielóides, como neutrófilos e
monócitos. A expressão de IGF-1 e USP2 pode estar associada ao sistema imunológico nas
amostras de diabetes e câncer, esses distúrbios podem levar a alterações na imunidade ao
aumentar a infiltração de leucócitos e aumentar a expressão de citocinas pró-inflamatórias.
Essas diversas células comunicam-se entre si por meio de contato direto ou pela produção de
citocinas e quimiocinas e agem de maneira autócrina e parácrina para controlar e moldar o
crescimento tumoral e metástase. Curiosamente, observamos menor expressão da proteína
ARNTL2 em amostras de câncer. Esta proteína desempenha um papel importante no sistema
de ritmo circadiano e está associada ao mecanismo de escape imunológico, níveis de expressão
alterados desses genes podem estar associados à progressão do câncer e ao fenótipo metastático
agressivo.
Essas moléculas biológicas não apenas representam a associação de diabetes mellitus tipo
2 e biomarcadores de ritmo circadiano com câncer de mama, bexiga, fígado, pâncreas, cólon e
reto, mas também têm potencial significativo para serem consideradas como biomarcadores em
nível de sistema que podem ser usados para triagem ou finalidades terapêuticas, podendo levar
a uma medicina de precisão, onde o tratamento é individualizado, garantindo melhores
resultados para o paciente, assim como priorizará a fármaco-economia, selecionando quais
pacientes serão realmente beneficiados com as drogas, economizando recursos públicos e
particulares e evitando que pessoas recebam medicamentos que pouco as beneficiariam e ainda
iriam infligir toxicidade decorrente dos mesmos. Nossos dados estimulam esforços em novos
estudos para alcançar a validação experimental e clínica sobre essas biomoléculas.

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of the original submission. Any addition, deletion or rearrangement of author names in the
authorship list should be made only before the manuscript has been accepted and only if
approved by the journal editor. To request such a change, the Editor must receive the following
from the corresponding author: (a) the reason for the change in author list and (b) written
confirmation (e-mail, letter) from all authors that they agree with the addition, removal or

rearrangement. In the case of addition or removal of authors, this includes confirmation from
the author being added or removed. Only in exceptional circumstances will the editor consider
the addition, deletion or rearrangement of authors after the manuscript has been accepted. While
the editor considers the request, publication of the manuscript will be suspended. If the
manuscript has already been published in an online issue, any requests approved by the editor
will result in a corrigendum. Article transfer service This journal is part of our Article Transfer
Service. This means that if the editor feels your article is more suitable in one of our other
participating journals, then you may be asked to consider transferring the article to one of those.
If you agree, your article will be transferred automatically on your behalf with no need to
reformat. Please note that your article will be reviewed again by the new journal. More
information. Copyright Upon acceptance of an article, authors will be asked to complete a
'Journal Publishing Agreement' (see more information on this). An e-mail will be sent to the
corresponding author confirming receipt of the manuscript together with a 'Journal Publishing
Agreement' form or a link to the online version of this agreement. Subscribers may reproduce
tables of contents or prepare lists of articles including abstracts for internal circulation within
their institutions. Permission of the Publisher is required for resale or distribution outside the
institution and for all other derivative works, including compilations and translations. If
excerpts from other copyrighted works are included, the author(s) must obtain written
permission from the copyright owners and credit the source(s) in the article. Elsevier has
preprinted forms for use by authors in these cases. For gold open access articles: Upon
acceptance of an article, authors will be asked to complete an 'Exclusive License Agreement'
(more information). Permitted third party reuse of gold open access articles is determined by
the author's choice of user license. Author rights As an author you (or your employer or
institution) have certain rights to reuse your work. More information. Elsevier supports
responsible sharing Find out how you can share your research published in Elsevier journals.
Role of the funding source You are requested to identify who provided financial support for the
conduct of the research and/or preparation of the article and to briefly describe the role of the
sponsor(s), if any, in study design; in the collection, analysis and interpretation of data; in the
writing of the report; and in the decision to submit the article for publication. If the funding
source(s) had no such involvement then this should be stated. Open access Please visit our Open
Access page for more information
Elsevier Researcher Academy Researcher Academy is a free e-learning platform designed
to support early and mid-career researchers throughout their research journey. The "Learn"
environment at Researcher Academy offers several interactive modules, webinars,

downloadable guides and resources to guide you through the process of writing for research and
going through peer review. Feel free to use these free resources to improve your submission
and navigate the publication process with ease. Language (usage and editing services) Please
write your text in good English (American or British usage is accepted, but not a mixture of
these). Authors who feel their English language manuscript may require editing to eliminate
possible grammatical or spelling errors and to conform to correct scientific English may wish
to use the English Language Editing service available from Elsevier's Author Services.
Submission Our online submission system guides you stepwise through the process of entering
your article details and uploading your files. The system converts your article files-to a single
PDF file used in the peer-review process. Editable files (e.g., Word, LaTeX) are required to
typeset your article for final publication. All correspondence, including notification of the
Editor's decision and requests for revision, is sent by e-mail. Referees Please submit the names
and institutional e-mail addresses of several potential referees. For more details, visit our
Support site. Note that the editor retains the sole right to decide whether or not the suggested
reviewers are used. PREPARATION NEW SUBMISSIONS Submission to this journal
proceeds totally online and you will be guided stepwise through the creation and uploading of
your files. The system automatically converts your files to a single PDF file, which is used in
the peer-review process. As part of the Your Paper Your Way service, you may choose to submit
your manuscript as a single file to be used in the refereeing process. This can be a PDF file or
a Word document, in any format or layout that can be used by referees to evaluate your
manuscript. It should contain high enough quality figures for refereeing. If you prefer to do so,
you may still provide all or some of the source files at the initial submission. Please note that
individual figure files larger than 10 MB must be uploaded separately. References There are no
strict requirements on reference formatting at submission. References can be in any style or
format as long as the style is consistent. Where applicable, author(s) name(s), journal title/ book
title, chapter title/article title, year of publication, volume number/book chapter and the article
number or pagination must be present. Use of DOI is highly encouraged. The reference style
used by the journal will be applied to the accepted article by Elsevier at the proof stage. Note
that missing data will be highlighted at proof stage for the author to correct. Formatting
requirements There are no strict formatting requirements but all manuscripts must contain the
essential elements needed to convey your manuscript, for example Abstract, Keywords,
Introduction, Materials and Methods, Results, Conclusions, Artwork and Tables with Captions.
If your article includes any Videos and/or other Supplementary material, this should be included
in your initial submission for peer review purposes. Divide the article into clearly defined

sections. Figures and tables embedded in text Please ensure the figures and the tables included
in the single file are placed next to the relevant text in the manuscript, rather than at the bottom
or the top of the file. The corresponding caption should be placed directly below the figure or
table. Peer review This journal operates a single anonymized review process. All contributions
will be initially assessed by the editor for suitability for the journal. Papers deemed suitable are
then typically sent to a minimum of two independent expert reviewers to assess the scientific
quality of the paper. The Editor is responsible for the final decision regarding acceptance or
rejection of articles. The Editor's decision is final. Editors are not involved in decisions about
papers which they have written themselves or have been written by family members or
colleagues or which relate to products or services in which the editor has an interest. Any such
submission is subject to all of the journal's usuais procedures, with peer review handled
independently of the relevant editor and their research groups. More information on types of
peer review.
REVISED SUBMISSIONS Use of word processing software Regardless of the file
format of the original submission, at revision you must provide us with an editable file of the
entire article. Keep the layout of the text as simple as possible. Most formatting codes will be
removed and replaced on processing the article. The electronic text should be prepared in a way
very similar to that of conventional manuscripts (see also the Guide to Publishing with
Elsevier). See also the section on Electronic artwork. To avoid unnecessary errors you are
strongly advised to use the 'spell-check' and 'grammar-check' functions of your word processor.
Article structure Subdivision - numbered sections divide your article into clearly defined and
numbered sections. Subsections should be numbered 1.1 (then 1.1.1, 1.1.2, ...), 1.2, etc. (the
abstract is not included in section numbering). Use this numbering also for internal crossreferencing: do not just refer to 'the text'. Any subsection may be given a brief heading. Each
heading should appear on its own separate line. Introduction State the objectives of the work
and provide an adequate background, avoiding a detailed literature survey or a summary of the
results. Material and methods Provide sufficient details to allow the work to be reproduced by
an independent researcher. Methods that are already published should be summarized, and
indicated by a reference. If quoting directly from a previously published method, use quotation
marks and also cite the source. Any modifications to existing methods should also be described.
Theory/calculation A Theory section should extend, not repeat, the background to the article
already dealt with in the Introduction and lay the foundation for further work. In contrast, a
Calculation section represents a practical development from a theoretical basis. Results Results
should be clear and concise. Discussion This should explore the significance of the results of

the work, not repeat them. A combined Results and Discussion section is often appropriate.
Avoid extensive citations and discussion of published literature. Conclusions The main
conclusions of the study may be presented in a short Conclusions section, which may stand
alone or form a subsection of a Discussion or Results and Discussion section. Appendices If
there is more than one appendix, they should be identified as A, B, etc. Formulae and equations
in appendices should be given separate numbering: Eq. (A.1), Eq. (A.2), etc.; in a subsequent
appendix, Eq. (B.1) and so on. Similarly for tables and figures: Table A.1; Fig. A.1, etc.
Essential title page information • Title. Concise and informative. Titles are often used in
information-retrieval systems. Avoid abbreviations and formulae where possible. • Author
names and affiliations. Please clearly indicate the given name(s) and family name(s) of each
author and check that all names are accurately spelled. You can add your name between
parentheses in your own script behind the English transliteration. Present the authors' affiliation
addresses (where the actual work was done) below the names. Indicate all affiliations with a
lowercase superscript letter immediately after the author's name and in front of the appropriate
address. Provide the full postal address of each affiliation, including the country name and, if
available, the e-mail address of each author.
• Corresponding author. Clearly indicate who will handle correspondence at all stages of
refereeing and publication, also post-publication. This responsibility includes answering any
future queries about Methodology and Materials. Ensure that the e-mail address is given and
that contact details are kept up to date by the corresponding author. • Present/permanent address.
If an author has moved since the work described in the article was done, or was visiting at the
time, a 'Present address' (or 'Permanent address') may be indicated as a footnote to that author's
name. The address at which the author actually did the work must be retained as the main,
affiliation address. Superscript Arabic numerals are used for such footnotes. Highlights
Highlights are mandatory for this journal as they help increase the discoverability of your article
via search engines. They consist of a short collection of bullet points that capture the novel
results of your research as well as new methods that were used during the study (if any). Please
have a look at the examples here: example Highlights. Highlights should be submitted in a
separate editable file in the online submission system. Please use 'Highlights' in the file name
and include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point).
Abstract A concise and factual abstract is required. The abstract should state briefly the purpose
of the research, the principal results and major conclusions. An abstract is often presented
separately from the article, so it must be able to stand alone. For this reason, References should
be avoided, but if essential, then cite the author(s) and year(s). Also, non-standard or uncommon

abbreviations should be avoided, but if essential they must be defined at their first mention in
the abstract itself. The abstract should be no more than 150 words. Graphical abstract Although
a graphical abstract is optional, its use is encouraged as it draws more attention to the online
article. The graphical abstract should summarize the contents of the article in a concise, pictorial
form designed to capture the attention of a wide readership. Graphical abstracts should be
submitted as a separate file in the online submission system. Image size: Please provide an
image with a minimum of 531 × 1328 pixels (h × w) or proportionally more. The image should
be readable at a size of 5 × 13 cm using a regular screen resolution of 96 dpi. Preferred file
types: TIFF, EPS, PDF or MS Office files. You can view Example Graphical Abstracts on our
information site. Authors can make use of Elsevier's Illustration Services to ensure the best
presentation of their images and in accordance with all technical requirements. Keywords
Immediately after the abstract, provide a maximum of 6 keywords, using British spelling and
avoiding general and plural terms and multiple concepts (avoid, for example, 'and', 'of'). Be
sparing with abbreviations: only abbreviations firmly established in the field may be eligible.
These keywords will be used for indexing purposes. Abbreviations Define abbreviations that
are not standard in this field in a footnote to be placed on the first page of the article. Such
abbreviations that are unavoidable in the abstract must be defined at their first mention there,
as well as in the footnote. Ensure consistency of abbreviations throughout the article.
Acknowledgements Collate acknowledgements in a separate section at the end of the article
before the references and do not, therefore, include them on the title page, as a footnote to the
title or otherwise. List here those individuals who provided help during the research (e.g.,
providing language help, writing assistance or proof reading the article, etc.). Formatting of
funding sources List funding sources in this standard way to facilitate compliance to funder's
requirements: Funding: This work was supported by the National Institutes of Health [grant
numbers xxxx, yyyy]; the Bill & Melinda Gates Foundation, Seattle, WA [grant number zzzz];
and the United States Institutes of Peace [grant number aaaa].
It is not necessary to include detailed descriptions on the program or type of grants and
awards. When funding is from a block grant or other resources available to a university, college,
or other research institution, submit the name of the institute or organization that provided the
funding. If no funding has been provided for the research, please include the following sentence:
This research did not receive any specific grant from funding agencies in the public,
commercial, or not-for-profit sectors. Units Follow internationally accepted rules and
conventions: use the international system of units (SI). If other units are mentioned, please give
their equivalent in SI. Math formulae Please submit math equations as editable text and not as

images. Present simple formulae in line with normal text where possible and use the solidus (/)
instead of a horizontal line for small fractional terms, e.g., X/Y. In principle, variables are to be
presented in italics. Powers of e are often more conveniently denoted by exp. Number
consecutively any equations that have to be displayed separately from the text (if referred to
explicitly in the text). Footnotes Footnotes should be used sparingly. Number them
consecutively throughout the article. Many word processors build footnotes into the text, and
this feature may be used. Should this not be the case, indicate the position of footnotes in the
text and present the footnotes themselves separately at the end of the article. Artwork Image
Manipulation Whilst it is accepted that authors sometimes need to manipulate images for clarity,
manipulation for purposes of deception or fraud will be seen as scientific ethical abuse and will
be dealt with accordingly. For graphical images, this journal is applying the following policy:
no specific feature within an image may be enhanced, obscured, moved, removed, or
introduced. Adjustments of brightness, contrast, or color balance are acceptable if and as long
as they do not obscure or eliminate any information present in the original. Nonlinear
adjustments (e.g. changes to gamma settings) must be disclosed in the figure legend. Electronic
artwork General points • Make sure you use uniform lettering and sizing of your original
artwork. • Preferred fonts: Arial (or Helvetica), Times New Roman (or Times), Symbol,
Courier. • Number the illustrations according to their sequence in the text. • Use a logical
naming convention for your artwork files. • Indicate per figure if it is a single, 1.5 or 2-column
fitting image. • For Word submissions only, you may still provide figures and their captions,
and tables within a single file at the revision stage. • Please note that individual figure files
larger than 10 MB must be provided in separate source files. A detailed guide on electronic
artwork is available. You are urged to visit this site; some excerpts from the detailed information
are given here. Formats Regardless of the application used, when your electronic artwork is
finalized, please 'save as' or convert the images to one of the following formats (note the
resolution requirements for line drawings, halftones, and line/halftone combinations given
below): EPS (or PDF): Vector drawings. Embed the font or save the text as 'graphics'. TIFF (or
JPG): Color or grayscale photographs (halftones): always use a minimum of 300 dpi. TIFF (or
JPG): Bitmapped line drawings: use a minimum of 1000 dpi. TIFF (or JPG): Combinations
bitmapped line/half-tone (color or grayscale): a minimum of 500 dpi is required. Please do not:
• Supply files that are optimized for screen use (e.g., GIF, BMP, PICT, WPG); the resolution is
too low. • Supply files that are too low in resolution. • Submit graphics that are
disproportionately large for the content.

Color artwork Please make sure that artwork files are in an acceptable format (TIFF (or
JPEG), EPS (or PDF) or MS Office files) and with the correct resolution. If, together with your
accepted article, you submit usable color figures then Elsevier will ensure, at no additional
charge, that these figures will appear in color online (e.g., ScienceDirect and other sites) in
addition to color reproduction in print. Further information on the preparation of electronic
artwork. Illustration services Elsevier's Author Services offers Illustration Services to authors
preparing to submit a manuscript but concerned about the quality of the images accompanying
their article. Elsevier's expert illustrators can produce scientific, technical and medical-style
images, as well as a full range of charts, tables and graphs. Image 'polishing' is also available,
where our illustrators take your image(s) and improve them to a professional standard. Please
visit the website to find out more. Figure captions Ensure that each illustration has a caption. A
caption should comprise a brief title (not on the figure itself) and a description of the illustration.
Keep text in the illustrations themselves to a minimum but explain all symbols and
abbreviations used. Tables Please submit tables as editable text and not as images. Tables can
be placed either next to the relevant text in the article, or on separate page(s) at the end. Number
tables consecutively in accordance with their appearance in the text and place any table notes
below the table body. Be sparing in the use of tables and ensure that the data presented in them
do not duplicate results described elsewhere in the article. Please avoid using vertical rules and
shading in table cells. References Citation in text Please ensure that every reference cited in the
text is also present in the reference list (and vice versa). Any references cited in the abstract
must be given in full. Unpublished results and personal communications are not recommended
in the reference list, but may be mentioned in the text. If these references are included in the
reference list they should follow the standard reference style of the journal and should include
a substitution of the publication date with either 'Unpublished results' or 'Personal
communication'. Citation of a reference as 'in press' implies that the item has been accepted for
publication. Reference links Increased discoverability of research and high quality peer review
are ensured by online links to the sources cited. In order to allow us to create links to abstracting
and indexing services, such as Scopus, CrossRef and PubMed, please ensure that data provided
in the references are correct. Please note that incorrect surnames, journal/book titles, publication
year and pagination may prevent link creation. When copying references, please be careful as
they may already contain errors. Use of the DOI is highly encouraged. A DOI is guaranteed
never to change, so you can use it as a permanent link to any electronic article. An example of
a citation using DOI for an article not yet in an issue is: VanDecar J.C., Russo R.M., James
D.E., Ambeh W.B., Franke M. (2003). Aseismic continuation of the Lesser Antilles slab

beneath

northeastern

Venezuela.

Journal

Geophysical

Research,

https://doi.org/10.1029/2001JB000884. Please note the format of such citations should be in the
same style as all other references in the paper. Web references As a minimum, the full URL
should be given and the date when the reference was last accessed. Any further information, if
known (DOI, author names, dates, reference to a source publication, etc.), should also be given.
Web references can be listed separately (e.g., after the reference list) under a different heading
if desired, or can be included in the reference list. Data references This journal encourages you
to cite underlying or relevant datasets in your manuscript by citing them in your text and
including a data reference in your Reference List. Data references should include the following
elements: author name(s), dataset title, data repository, version (where available), year, and
global persistent identifier. Add [dataset] immediately before the reference so we can properly
identify it as a data reference. The [dataset] identifier will not appear in your published article.
References in a special issue Please ensure that the words 'this issue' are added to any
references in the list (and any citations in the text) to other articles in the same Special Issue.
Reference management software Most Elsevier journals have their reference template available
in many of the most popular reference management software products. These include all
products that support Citation Style Language styles, such as Mendeley. Using citation plugins from these products, authors only need to select the appropriate journal template when
preparing their article, after which citations and bibliographies will be automatically formatted
in the journal's style. If no template is yet available for this journal, please follow the format of
the sample references and citations as shown in this Guide. If you use reference management
software, please ensure that you remove all field codes before submitting the electronic
manuscript. More information on how to remove field codes from different reference
management software. Users of Mendeley Desktop can easily install the reference style for this
journal by clicking the following link: http://open.mendeley.com/use-citation-style/molecularand-cellular-endocrinology When preparing your manuscript, you will then be able to select
this style using the Mendeley plugins for Microsoft Word or LibreOffice. Reference formatting
There are no strict requirements on reference formatting at submission. References can be in
any style or format as long as the style is consistent. Where applicable, author(s) name(s),
journal title/ book title, chapter title/article title, year of publication, volume number/book
chapter and the article number or pagination must be present. Use of DOI is highly encouraged.
The reference style used by the journal will be applied to the accepted article by Elsevier at the
proof stage. Note that missing data will be highlighted at proof stage for the author to correct.
If you do wish to format the references yourself they should be arranged according to the

following examples: Reference style Text: All citations in the text should refer to: 1. Single
author: the author's name (without initials, unless there is ambiguity) and the year of
publication; 2. Two authors: both authors' names and the year of publication; 3. Three or more
authors: first author's name followed by 'et al.' and the year of publication. Citations may be
made directly (or parenthetically). Groups of references can be listed either first alphabetically,
then chronologically, or vice versa. Examples: 'as demonstrated (Allan, 2000a, 2000b, 1999;
Allan and Jones, 1999) …. Or, as demonstrated (Jones, 1999; Allan, 2000) … Kramer et al.
(2010) have recently shown …' List: References should be arranged first alphabetically and
then further sorted chronologically if necessary. More than one reference from the same
author(s) in the same year must be identified by the letters 'a', 'b', 'c', etc., placed after the year
of publication. Examples: Reference to a journal publication: Van der Geer, J., Hanraads, J.A.J.,
Lupton, R.A., 2010. The art of writing a scientific article. J. Sci. Commun. 163, 51–59.
https://doi.org/10.1016/j.Sc.2010.00372. Reference to a journal publication with an article
number: Van der Geer, J., Hanraads, J.A.J., Lupton, R.A., 2018. The art of writing a scientific
article. Heliyon. 19, e00205. https://doi.org/10.1016/j.heliyon.2018.e00205. Reference to a
book: Strunk Jr., W., White, E.B., 2000. The Elements of Style, fourth ed. Longman, New York.
Reference to a chapter in an edited book: Mettam, G.R., Adams, L.B., 2009. How to prepare an
electronic version of your article, in: Jones, B.S., Smith, R.Z. (Eds.), Introduction to the
Electronic Age. E-Publishing Inc., New York, pp. 281–304. Reference to a website: Cancer
Research UK, 1975. Cancer statistics reports for the UK. http://www.cancerresearchuk.org/
aboutcancer/statistics/cancerstatsreport/ (accessed 13 March 2003). Reference to a dataset:
[dataset] Oguro, M., Imahiro, S., Saito, S., Nakashizuka, T., 2015. Mortality data for Japanese
oak

wilt

disease

and

surrounding

forest

compositions.

Mendeley

Data,

v1.

https://doi.org/10.17632/ xwj98nb39r.1. Journal abbreviations source Journal names should be
abbreviated according to the List of Title Word Abbreviations. Video Elsevier accepts video
material and animation sequences to support and enhance your scientific research. Authors who
have video or animation files that they wish to submit with their article are strongly encouraged
to include links to these within the body of the article. This can be done in the same way as a
figure or table by referring to the video or animation content and noting in the body text where
it should be placed. All submitted files should be properly labeled so that they directly relate to
the video file's content. In order to ensure that your video or animation material is directly
usable, please provide the file in one of our recommended file formats with a preferred
maximum size of 150 MB per file, 1 GB in total. Video and animation files supplied will be
published online in the electronic version of your article in Elsevier Web products, including

ScienceDirect. Please supply 'stills' with your files: you can choose any frame from the video
or animation or make a separate image. These will be used instead of standard icons and will
personalize the link to your video data. For more detailed instructions please visit our video
instruction pages. Note: since video and animation cannot be embedded in the print version of
the journal, please provide text for both the electronic and the print version for the portions of
the article that refer to this content. Data visualization Include interactive data visualizations in
your publication and let your readers interact and engage more closely with your research.
Follow the instructions here to find out about available data visualization options and how to
include them with your article. Supplementary material Supplementary material such as
applications, images and sound clips, can be published with your article to enhance it. Submitted
supplementary items are published exactly as they are received (Excel or PowerPoint files will
appear as such online). Please submit your material together with the article and supply a
concise, descriptive caption for each supplementary file. If you wish to make changes to
supplementary material during any stage of the process, please make sure to provide an updated
file. Do not annotate any corrections on a previous version. Please switch off the 'Track
Changes' option in Microsoft Office files as these will appear in the published version. Research
data This journal encourages and enables you to share data that supports your research
publication where appropriate,and enables you to interlink the data with your published articles.
Research data refers to the results of observations or experimentation that validate research
findings. To facilitate reproducibility and data reuse, this journal also encourages you to share
your software, code, models, algorithms, protocols, methods and other useful materials related
to the project. Below are a number of ways in which you can associate data with your article or
make a statement about the availability of your data when submitting your manuscript. If you
are sharing data in one of these ways, you are encouraged to cite the data in your manuscript
and reference list. Please refer to the "References" section for more information about data
citation. For more information on depositing, sharing and using research data and other relevant
research materials, visit the research data page. Data linking If you have made your research
data available in a data repository, you can link your article directly to the dataset. Elsevier
collaborates with a number of repositories to link articles on ScienceDirect with relevant
repositories, giving readers access to underlying data that gives them a better understanding of
the research described. There are different ways to link your datasets to your article. When
available, you can directly link your dataset to your article by providing the relevant information
in the submission system. For more information, visit the database linking page. For supported
data repositories a repository banner will automatically appear next to your published article on

ScienceDirect. In addition, you can link to relevant data or entities through identifiers within
the text of your manuscript, using the following format: Database: xxxx (e.g., TAIR:
AT1G01020; CCDC: 734053; PDB: 1XFN).
Mendeley Data This journal supports Mendeley Data, enabling you to deposit any
research data (including raw and processed data, video, code, software, algorithms, protocols,
and methods) associated with your manuscript in a free-to-use, open access repository. During
the submission process, after uploading your manuscript, you will have the opportunity to
upload your relevant datasets directly to Mendeley Data. The datasets will be listed and directly
accessible to readers next to your published article online. For more information, visit the
Mendeley Data for journals page. Data in Brief You have the option of converting any or all
parts of your supplementary or additional raw data into a data article published in Data in Brief.
A data article is a new kind of article that ensures that your data are actively reviewed, curated,
formatted, indexed, given a DOI and made publicly available to all upon publication (watch this
video describing the benefits of publishing your data in Data in Brief). You are encouraged to
submit your data article for Data in Brief as an additional item directly alongside the revised
version of your manuscript. If your research article is accepted, your data article will
automatically be transferred over to Data in Brief where it will be editorially reviewed,
published open access and linked to your research article on ScienceDirect. Please note an open
access fee is payable for publication in Data in Brief. Full details can be found on the Data in
Brief website. Please use this template to write your Data in Brief data article. Data statement
to foster transparency, we encourage you to state the availability of your data in your
submission. This may be a requirement of your funding body or institution. If your data is
unavailable to access or unsuitable to post, you will have the opportunity to indicate why during
the submission process, for example by stating that the research data is confidential. The
statement will appear with your published article on ScienceDirect. For more information, visit
the Data Statement page. AFTER ACCEPTANCE Online proof correction to ensure a fast
publication process of the article, we kindly ask authors to provide us with their proof
corrections within two days. Corresponding authors will receive an e-mail with a link to our
online proofing system, allowing annotation and correction of proofs online. The environment
is similar to MS Word: in addition to editing text, you can also comment on figures/tables and
answer questions from the Copy Editor. Web-based proofing provides a faster and less errorprone process by allowing you to directly type your corrections, eliminating the potential
introduction of errors. If preferred, you can still choose to annotate and upload your edits on the
PDF version. All instructions for proofing will be given in the e-mail we send to authors,

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including alternative methods to the online version and PDF. We will do everything possible to
get your article published quickly and accurately. Please use this proof only for checking the
typesetting, editing, completeness and correctness of the text, tables and figures. Significant
changes to the article as accepted for publication will only be considered at this stage with
permission from the editor. It is important to ensure that all corrections are sent back to us in
one communication. Please check carefully before replying, as inclusion of any subsequent
corrections cannot be guaranteed. Proofreading is solely your responsibility. Offprints The
corresponding author will, at no cost, receive a customized Share Link providing 50 days free
access to the final published version of the article on ScienceDirect. The Share Link can be used
for sharing the article via any communication channel, including email and social media. For
an extra charge, paper offprints can be ordered via the offprint order form which is sent once
the article is accepted for publication. Both corresponding and co-authors may order offprints
at any time via Elsevier's Author Services. Corresponding authors who have published their
article gold open access do not receive a Share Link as their final published version of the article
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Mestrado em Ciências Médicas - PPGCM