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Analysis of cortisol mechanism to predict common genes between PCOS and its co-morbidities

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Abstract

Polycystic ovary syndrome (PCOS) is a multifactorial endocrine disorder and one of the main causes of PCOS is hormonal imbalance due to lifestyle changes. Estrogen, progesterone, testosterone, cortisol and melatonin are the major hormones that regulate the menstrual cycle and other endocrine disorders in women. The hormone cortisol, in particular, can lead to many comorbid conditions related to PCOS. In this proposed work, PubMed articles were mined using R program to retrieve target genes for PCOS. The NCBI Gene/Genome database was used to download PCOS genes, and genes related to cortisol and major comorbid diseases. Then, commonly intersecting genes among PCOS–Cortisol–Comorbids were identified. From the obtained results, the top seven genes were considered as Hub–Bottleneck genes based on the Degree and Betweenness values of the 26 intersecting (merged) genes. Functional annotation and pathway analysis were performed to examine significant pathways of Hub–Bottleneck genes.

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All data were derived from analysis performed by the authors.

Abbreviations

LH:

Luteinizing hormone

HGNC:

HUGO Gene Nomenclature Committee

STRING:

Search tool for the retrieval of interacting genes

PPI:

Protein–protein interaction

MCODE:

Molecular complex detection

MCC:

Maximum clique centrality

GO:

Gene ontology

BP:

Biological processes

CC:

Cellular components

MF:

Molecular functions

KEGG:

Kyoto Encyclopedia of Gene and Genomes

DAVID:

Database for annotation, visualization and integrated discovery

References

  • Abraham Gnanadass S, DivakarPrabhu Y, Valsala Gopalakrishnan A (2021) Association of metabolic and inflammatory markers with polycystic ovarian syndrome (PCOS): an update. Arch Gynecol Obstet 303(3):631–643

    Article  Google Scholar 

  • Ajmal N, Khan SZ, Shaikh R (2019) Polycystic ovary syndrome (PCOS) and genetic predisposition: a review article. Eur J Obstet Gynecol Reprod Biol X 3:100060

    Google Scholar 

  • Alkhuriji AF, Al Omar SY, Babay ZA, El-Khadragy MF, Mansour LA, Alharbi WG, Khalil MI (2020) Association of IL-1β, IL-6, TNF-α, and TGFβ1 gene polymorphisms with recurrent spontaneous abortion in polycystic ovary syndrome. Dis Mark 2020:1–8

    Article  Google Scholar 

  • Bairoch A, Apweiler R, Wu CH, Barker WC, Boeckmann B, Ferro S et al (2005) The universal protein resource (UniProt). Nucleic Acids Res 33(suppl_1):154–159

    Google Scholar 

  • Benjamin JJ, Kuppusamy M, Koshy T, KalburgiNarayana M, Ramaswamy P (2021) Cortisol and polycystic ovarian syndrome—a systematic search and meta-analysis of case–control studies. Gynecol Endocrinol 37(11):961–967

    Article  Google Scholar 

  • Bindea G, Galon J, Mlecnik B (2013) CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics 29(5):661–663

    Article  Google Scholar 

  • Brown GR, Hem V, Katz KS, Ovetsky M, Wallin C, Ermolaeva O et al (2015) Gene: a gene-centered information resource at NCBI. Nucleic Acids Res 43(D1):D36–D42

    Article  Google Scholar 

  • Canese K, Weis S (2013) PubMed: the bibliographic database. NCBI Handb 2(1):13–24

  • Cerychova R, Pavlinkova G (2018) HIF-1, metabolism, and diabetes in the embryonic and adult heart. Front Endocrinol 9:460

    Article  Google Scholar 

  • Chakrabarti J (2013) Serum leptin level in women with polycystic ovary syndrome: correlation with adiposity, insulin, and circulating testosterone. Ann Med Health Sci Res 3(2):191

    Article  Google Scholar 

  • Chen L, Zhang Z, Huang J, Jin M (2018) Association between rs1800795 polymorphism in the interleukin-6 gene and the risk of polycystic ovary syndrome: a meta-analysis. Medicine 97(29):e11558

    Article  Google Scholar 

  • Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(4):1–7

    Google Scholar 

  • Dallel M, Sghaier I, Finan RR, Douma Z, Hachani F, Letaifa DB et al (2019) Circulating leptin concentration, LEP gene variants and haplotypes, and polycystic ovary syndrome in Bahraini and Tunisian Arab women. Gene 694:19–25

    Article  Google Scholar 

  • Dehghan Z, Mohammadi-Yeganeh S, Sameni M, Mirmotalebisohi SA, Zali H, Salehi M (2021) Repurposing new drug candidates and identifying crucial molecules underlying PCOS pathogenesis based on bioinformatics analysis. DARU J Pharm Sci 29(2):353–366

    Article  Google Scholar 

  • Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(9):1–11

    Article  Google Scholar 

  • Deswal R, Narwal V, Dang A, Pundir CS (2020) The prevalence of polycystic ovary syndrome: a brief systematic review. J Hum Reprod Sci 13(4):261

    Article  Google Scholar 

  • Escobar-Morreale HF (2018) Polycystic ovary syndrome: definition, aetiology, diagnosis and treatment. Nat Rev Endocrinol 14(5):270–284

    Article  Google Scholar 

  • Fernandez RC, Moore VM, Van Ryswyk EM, Varcoe TJ, Rodgers RJ, March WA et al (2018) Sleep disturbances in women with polycystic ovary syndrome: prevalence, pathophysiology, impact and management strategies. Nat Sci Sleep 10:45

    Article  Google Scholar 

  • Gilbert EW, Tay CT, Hiam DS, Teede HJ, Moran LJ (2018) Comorbidities and complications of polycystic ovary syndrome: an overview of systematic reviews. Clin Endocrinol 89(6):683–699

    Article  Google Scholar 

  • Goenawan IH, Bryan K, Lynn DJ (2016) DyNet: visualization and analysis of dynamic molecular interaction networks. Bioinformatics 32(17):2713–2715

    Article  Google Scholar 

  • Goussalya D, Jancy MS, Jemi AA, Soundarya R, Varghese S, Nalini AP, Kumar SG (2020) Association of Interleukin 6 and insulin resistance gene polymorphism with polycystic ovarian syndrome: a meta-analysis. Meta Gene 24:100675

    Article  Google Scholar 

  • Hoeger KM, Dokras A, Piltonen T (2021) Update on PCOS: consequences, challenges, and guiding treatment. J Clin Endocrinol Metab 106(3):e1071–e1083

    Article  Google Scholar 

  • Hu D, Jiang J, Lin Z, Zhang C, Moonasar N, Qian S (2021) Identification of key genes and pathways in scleral extracellular matrix remodeling in glaucoma: potential therapeutic agents discovered using bioinformatics analysis. Int J Med Sci 18(7):1554

    Article  Google Scholar 

  • Islam MR, Ahmed ML, Paul BK, Bhuiyan T, Ahmed K, Moni MA (2020) Identification of the core ontologies and signature genes of polycystic ovary syndrome (PCOS): a bioinformatics analysis. Inform Med Unlocked 18:100304

    Article  Google Scholar 

  • Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  Google Scholar 

  • Kawamura S, Maesawa C, Nakamura K, Nakayama K, Morita M, Hiruma Y, &, et al (2011) Predisposition for borderline personality disorder with comorbid major depression is associated with that for polycystic ovary syndrome in female Japanese population. Neuropsychiatr Dis Treat 7:655

    Article  Google Scholar 

  • Kay AM, Simpson CL, Stewart JA (2016) The role of AGE/RAGE signaling in diabetes-mediated vascular calcification. J Diabetes Res 2016:1–8

    Article  Google Scholar 

  • Kebapcilar AG, Tatar MG, Ipekci SH, Gonulalan G, Korkmaz H, Baldane S, Celik C (2014) Cornea in PCOS patients as a possible target of IGF-1 action and insulin resistance. Arch Gynecol Obstet 290(6):1255–1263

    Article  Google Scholar 

  • Khan MJ, Ullah A, Basit S (2019) Genetic basis of polycystic ovary syndrome (PCOS): current perspectives. Appl Clin Genet 12:249

    Article  Google Scholar 

  • Nekoonam S, Naji M, Nashtaei MS, Mortezaee K, Koruji M, Safdarian L, Amidi F (2017) Expression of AKT1 along with AKT2 in granulosa-lutein cells of hyperandrogenic PCOS patients. Arch Gynecol Obstet 295(4):1041–1050

    Article  Google Scholar 

  • Otasek D, Morris JH, Bouças J, Pico AR, Demchak B (2019) Cytoscape automation: empowering workflow-based network analysis. Genome Biol 20(1):1–15

    Article  Google Scholar 

  • Pasquali R, Stener-Victorin E, Yildiz BO, Duleba AJ, Hoeger K, Mason H et al (2011) PCOS forum: research in polycystic ovary syndrome today and tomorrow. Clin Endocrinol 74(4):424–433

    Article  Google Scholar 

  • Patel S (2018) Polycystic ovary syndrome (PCOS), an inflammatory, systemic, lifestyle endocrinopathy. J Steroid Biochem Mol Biol 182:27–36

    Article  Google Scholar 

  • Patel R, Tiwari A, Chouhan S (2020) Poly cystic ovarian syndrome: an updated review. J Appl Pharma Sci Res. https://doi.org/10.31069/japsr.v3i1.2

    Article  Google Scholar 

  • Qi J, Wang W, Zhu Q, He Y, Lu Y, Wang Y et al (2018) Local cortisol elevation contributes to endometrial insulin resistance in polycystic ovary syndrome. J Clin Endocrinol Metab 103(7):2457–2467

    Article  Google Scholar 

  • Team RC (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/

  • Radhakrishnan A, Raju R, Tuladhar N, Subbannayya T, Thomas JK, Goel R et al (2012) A pathway map of prolactin signaling. J Cell Commun Signal 6(3):169–173

    Article  Google Scholar 

  • Rahman F, Mahmud P, Karim R, Hossain T, Islam F (2020) Determination of novel biomarkers and pathways shared by colorectal cancer and endometrial cancer via comprehensive bioinformatics analysis. Inform Med Unlocked 20:100376

    Article  Google Scholar 

  • Rojas J, Chávez M, Olivar L, Rojas M, Morillo J, Mejías J, &, et al (2014) Polycystic ovary syndrome, insulin resistance, and obesity: navigating the pathophysiologic labyrinth. Int J Reprod Med. https://doi.org/10.1155/2014/719050

    Article  Google Scholar 

  • Salas-Pérez F, Ramos-Lopez O, Mansego ML, Milagro FI, Santos JL, Riezu-Boj JI, Martínez JA (2019) DNA methylation in genes of longevity-regulating pathways: association with obesity and metabolic complications. Aging (albany NY) 11(6):1874

    Article  Google Scholar 

  • Salehnia M, Zavareh S (2013) The effects of progesterone on oocyte maturation and embryo development. Int J Fertil Steril 7(2):74

    Google Scholar 

  • Shaikh N, Dadachanji R, Mukherjee S (2014) Genetic markers of polycystic ovary syndrome: emphasis on insulin resistance. Int J Med Genet 2014:1–10

    Article  Google Scholar 

  • Siddappa D, Beaulieu É, Gévry N, Roux PP, Bordignon V, Duggavathi R (2015) Effect of the transient pharmacological inhibition of Mapk3/1 pathway on ovulation in mice. PLoS ONE 10(3):e0119387

    Article  Google Scholar 

  • Soofi A, Taghizadeh M, Tabatabaei SM, Tavirani MR, Shakib H, Namaki S, Alighiarloo NS (2020) Centrality analysis of protein-protein interaction networks and molecular docking prioritize potential drug-targets in type 1 diabetes. Iran J Pharm Res IJPR 19(4):121

    Google Scholar 

  • Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S et al (2021) The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49(D1):D605–D612

    Article  Google Scholar 

  • Thomas GM, Huganir RL (2004) MAPK cascade signalling and synaptic plasticity. Nat Rev Neurosci 5(3):173–183

    Article  Google Scholar 

  • Tsilosani A, Gao C, Zhang W (2022) Aldosterone-regulated sodium transport and blood pressure. Front Physiol 13:770375

    Article  Google Scholar 

  • Velazquez MA, Hadeler KG, Herrmann D, Kues WA, Ulbrich S, Meyer HH et al (2011) In vivo oocyte developmental competence is reduced in lean but not in obese superovulated dairy cows after intraovarian administration of IGF1. Reproduction 142(1):41

    Article  Google Scholar 

  • Wang X, Lin Y (2008) Tumor necrosis factor and cancer, buddies or foes? 1. Acta Pharmacol Sin 29(11):1275–1288

    Article  Google Scholar 

  • Wang J, Zhong J, Chen G, Li M, Wu FX, Pan Y (2014) ClusterViz: a cytoscape APP for cluster analysis of biological network. IEEE/ACM Trans Comput Biol Bioinf 12(4):815–822

    Article  Google Scholar 

  • Witchel SF, Oberfield SE, Peña AS (2019) Polycystic ovary syndrome: pathophysiology, presentation, and treatment with emphasis on adolescent girls. J Endocr Soc 3(8):1545–1573

    Article  Google Scholar 

  • Witchel SF, Teede HJ, Peña AS (2020) Curtailing pcos. Pediatr Res 87(2):353–361

    Article  Google Scholar 

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Contributions

Conceptualization: VVR and JC; data curation: JC and VVR; formal analysis: JC and VVR; methodology: VVR and JC; project administration and supervision: JC; validation: JC; writing–original draft: VVR and JC; writing—review and editing: VVR and JC.

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Correspondence to Jayaprakash Chinnappan.

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Rajalakshmi, V.V., Chinnappan, J. Analysis of cortisol mechanism to predict common genes between PCOS and its co-morbidities. Netw Model Anal Health Inform Bioinforma 12, 36 (2023). https://doi.org/10.1007/s13721-023-00429-y

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  • DOI: https://doi.org/10.1007/s13721-023-00429-y

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