PCOSKBR2: a database of genes, diseases, pathways, and networks associated with polycystic ovary syndrome


  • 1.

    Wang, F. et al. Alternative splicing of the androgen receptor in polycystic ovary syndrome. Proc. Natl. Acad. Sci. USA 112, 4743–4748 (2015).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 2.

    Fauser, B. C. J. M. et al. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Hum. Reprod. 19, 41–47 (2004).


    Google Scholar
     

  • 3.

    Azziz, R. Diagnostic criteria for polycystic ovary syndrome: a reappraisal. Fertil. Steril. https://doi.org/10.1016/j.fertnstert.2005.01.085 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • 4.

    Azziz, R. et al. The androgen excess and PCOS Society criteria for the polycystic ovary syndrome: the complete task force report. Fertil. Steril. 91, 456–488 (2009).

    PubMed 

    Google Scholar
     

  • 5.

    Yedulapuram, S. H., Gunda, M., Moola, N. R. & Kadarla, R. K. An overview on polycystic ovarian syndrome. Asian J. Pharm. Res. Dev. 7, 72–80 (1970).


    Google Scholar
     

  • 6.

    Gilbert, E. W., Tay, C. T., Hiam, D. S., Teede, H. J. & Moran, L. J. Comorbidities and complications of polycystic ovary syndrome: an overview of systematic reviews. Clin. Endocrinol. 89, 683–699 (2018).


    Google Scholar
     

  • 7.

    Kazemi, M. et al. Comprehensive evaluation of type 2 diabetes and cardiovascular disease risk profiles in reproductive-age women with polycystic ovary syndrome: a large canadian cohort. J. Obstet. Gynaecol. Canada 41, 1453–1460 (2019).


    Google Scholar
     

  • 8.

    Kakoly, N. S., Moran, L. J., Teede, H. J. & Joham, A. E. Cardiometabolic risks in PCOS: a review of the current state of knowledge. Exp. Rev. Endocrinol. Metab. 14, 23–33 (2019).

    CAS 

    Google Scholar
     

  • 9.

    Dokras, A., Clifton, S., Futterweit, W. & Wild, R. Increased prevalence of anxiety symptoms in women with polycystic ovary syndrome: Systematic review and meta-analysis. Fertil. Steril. 97, 225-230.e2 (2012).

    PubMed 

    Google Scholar
     

  • 10.

    Chen, S. F., Yang, Y. C., Hsu, C. Y. & Shen, Y. C. Risk of bipolar disorder in patients with polycystic ovary syndrome: a nationwide population-based cohort study. J. Affect. Disord. 263, 458–462 (2020).

    CAS 
    PubMed 

    Google Scholar
     

  • 11.

    Thannickal, A. et al. Eating, sleeping and sexual function disorders in women with polycystic ovary syndrome (PCOS): a systematic review and meta-analysis. Clin. Endocrinol. (Oxf) 92, 338–349 (2020).


    Google Scholar
     

  • 12.

    Barthelmess, E. K. & Naz, R. K. Polycystic ovary syndrome: current status and future perspective. Frontiers Biosci. Elite 6E, 104–119 (2014).


    Google Scholar
     

  • 13.

    Joseph, S., Barai, R. S., Bhujbalrao, R. & Idicula-Thomas, S. PCOSKB: A knowledgebase on genes, diseases, ontology terms and biochemical pathways associated with polycystic ovary syndrome. Nucleic Acids Res. https://doi.org/10.1093/nar/gkv1146 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • 14.

    Afiqah-Aleng, N., Harun, S., A-Rahman, M. R. A., Nor Muhammad, N. A. & Mohamed-Hussein, Z. A. PCOSBase: a manually curated database of polycystic ovarian syndrome. Database https://doi.org/10.1093/database/bax098 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 15.

    Maniraja, J. M., Vetrivel, U., Munuswamy, D. & Melanathuru, V. PCOSDB: PolyCystic ovary syndrome DataBase for manually curated genes associated with the disease. Bioinformation 12, 4–8 (2016).


    Google Scholar
     

  • 16.

    Bardou, P., Mariette, J., Escudié, F., Djemiel, C. & Klopp, C. Jvenn: an interactive Venn diagram viewer. BMC Bioinform. 15, 293 (2014).


    Google Scholar
     

  • 17.

    Scicchitano, P. et al. Cardiovascular risk in women with PCOS. Int. J. Endocrinol. Metab. 10, 611–618 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 18.

    Kulshreshtha, B., Singh, S. & Arora, A. Family background of Diabetes Mellitus, obesity and hypertension affects the phenotype and first symptom of patients with PCOS. Gynecol. Endocrinol. 29, 1040–1044 (2013).

    PubMed 

    Google Scholar
     

  • 19.

    Sirmans, S. M., Parish, R. C., Blake, S. & Wang, X. Epidemiology and comorbidities of polycystic ovary syndrome in an indigent population. J. Investig. Med. 62, 868–874 (2014).

    PubMed 

    Google Scholar
     

  • 20.

    Elting, M. W., Korsen, T. J., Bezemer, P. D. & Schoemaker, J. Prevalence of diabetes mellitus, hypertension and cardiac complaints in a follow-up study of a Dutch PCOS population. Hum. Reprod. 16, 556–560 (2001).

    CAS 
    PubMed 

    Google Scholar
     

  • 21.

    Rasgon, N. L. et al. Depression in women with polycystic ovary syndrome: clinical and biochemical correlates. J. Affect. Disord. 74, 299–304 (2003).

    PubMed 

    Google Scholar
     

  • 22.

    Rodriguez-Paris, D. et al. Psychiatric disorders in women with polycystic ovary syndrome. Psychiatr. Pol. 53, 955–966 (2019).

    PubMed 

    Google Scholar
     

  • 23.

    Rassi, A. et al. Prevalence of psychiatric disorders in patients with polycystic ovary syndrome. Compr. Psychiatry 51, 599–602 (2010).

    PubMed 

    Google Scholar
     

  • 24.

    Hung, J. H. et al. Risk of psychiatric disorders following polycystic ovary syndrome: a nationwide population-based cohort study. PLoS One 9, e97041 (2014).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 25.

    Annagür, B. B., Kerimoglu, ÖS., Tazegül, A., Gündüz, Ş & Gençoglu, B. B. Psychiatric comorbidity in women with polycystic ovary syndrome. J. Obstet. Gynaecol. Res. 41, 1229–1233 (2015).

    PubMed 

    Google Scholar
     

  • 26.

    Brutocao, C. et al. Psychiatric disorders in women with polycystic ovary syndrome: a systematic review and meta-analysis. Endocrine 62, 318–325 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 27.

    Cheung, B. M. Y. The hypertension-diabetes continuum. J. Cardiovasc. Pharmacol. 55, 333–339 (2010).

    CAS 
    PubMed 

    Google Scholar
     

  • 28.

    Zhao, L. et al. Estrogen receptor 1 gene polymorphisms are associated with metabolic syndrome in postmenopausal women in China 11 Medical and Health Sciences 1103 Clinical Sciences. BMC Endocr. Disord. 18, 65 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 29.

    Jiao, X. et al. Variant alleles of the ESR1, PPARG, HMGA2, and MTHFR genes are associated with polycystic ovary syndrome risk in a Chinese population: A case-control study. Front. Endocrinol. (Lausanne) 9, 504 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 30.

    Jakimiuk, A. J., Weitsman, S. R., Yen, H. W., Bogusiewicz, M. & Magoffin, D. A. Estrogen receptor α and β expression in theca and granulosa cells from women with polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 87, 5532–5538 (2002).

    CAS 
    PubMed 

    Google Scholar
     

  • 31.

    Wong, W. T., Tian, X. Y. & Huang, Y. Endothelial dysfunction in diabetes and hypertension: cross talk in RAS, BMP4, and ROS-dependent COX-2-derived prostanoids. J. Cardiovasc. Pharmacol. 61, 204–214 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • 32.

    Schmidt, J. et al. Differential expression of inflammation-related genes in the ovarian stroma and granulosa cells of PCOS women. Mol. Hum. Reprod. 20, 49–58 (2014).

    CAS 
    PubMed 

    Google Scholar
     

  • 33.

    Supriya, R. et al. Adipokines demonstrate the interacting influence of central obesity with other cardiometabolic risk factors of metabolic syndrome in Hong Kong Chinese adults. PLoS One 13, e0201585 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 34.

    Gacka, M. & Adamiec, R. Mutations of peroxisome proliferator-activated receptor gamma (PPARgamma): clinical implications. Postepy Hig. Med. Dosw. (Online) 58, 483–489 (2004).


    Google Scholar
     

  • 35.

    Tsatsakis, A. M., Zafiropoulos, A., Tzatzarakis, M. N., Tzanakakis, G. N. & Kafatos, A. Relation of PON1 and CYP1A1 genetic polymorphisms to clinical findings in a cross-sectional study of a Greek rural population professionally exposed to pesticides. Toxicol. Lett. 186, 66–72 (2009).

    CAS 
    PubMed 

    Google Scholar
     

  • 36.

    Wang, Y. et al. Evidence for association between paraoxonase 1 gene polymorphisms and polycystic ovarian syndrome in south-west Chinese women. Eur. J. Endocrinol. 166, 877–885 (2012).

    CAS 
    PubMed 

    Google Scholar
     

  • 37.

    Ohashi, K., Ouchi, N. & Matsuzawa, Y. Adiponectin and Hypertension. Am. J. Hypertens. 24, 263–269 (2011).

    CAS 
    PubMed 

    Google Scholar
     

  • 38.

    Davis, S. K. et al. Association of adiponectin with type 2 diabetes and hypertension in African American men and women: The Jackson Heart Study. BMC Cardiovasc. Disord. 15, 13 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 39.

    Mirza, S. S., Shafique, K., Shaikh, A. R., Khan, N. A. & Anwar Qureshi, M. Association between circulating adiponectin levels and polycystic ovarian syndrome. J. Ovarian Res. 7, 18 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 40.

    Cooney, L. G. & Dokras, A. Depression and anxiety in polycystic ovary syndrome: etiology and treatment. Current Psychiatry Rep. 19, 83 (2017).


    Google Scholar
     

  • 41.

    Meczekalski, B., Pérez-Roncero, G. R., López-Baena, M. T., Chedraui, P. & Pérez-López, F. R. The polycystic ovary syndrome and gynecological cancer risk. Gynecol. Endocrinol. 36, 289–293 (2020).

    CAS 
    PubMed 

    Google Scholar
     

  • 42.

    Feng, Y. et al. Effects of androgen and leptin on behavioral and cellular responses in female rats. Horm. Behav. 60, 427–438 (2011).

    CAS 
    PubMed 

    Google Scholar
     

  • 43.

    Kamalanathan, S., Sahoo, J. & Sathyapalan, T. Pregnancy in polycystic ovary syndrome. Indian J. Endocrinol. Metab. 17, 37 (2013).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 44.

    Hartanti, M. D. et al. Could perturbed fetal development of the ovary contribute to the development of polycystic ovary syndrome in later life?. PLoS One 15, e0229351 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 45.

    Scalici, E. et al. Circulating microRNAs in follicular fluid, powerful tools to explore in vitro fertilization process. Sci. Rep. 6, 1–10 (2016).


    Google Scholar
     

  • 46.

    He, T. et al. MicroRNA-141 and MicroRNA-200c are overexpressed in granulosa cells of polycystic ovary syndrome patients. Front. Med. 5, 299 (2018).


    Google Scholar
     

  • 47.

    Tesfaye, D. et al. Potential role of microRNAs in mammalian female fertility. Reprod. Fertil. Dev. 29, 8–23 (2017).

    CAS 

    Google Scholar
     

  • 48.

    Lykoudi, A. et al. Dysregulated placental microRNAs in early and Late onset Preeclampsia. Placenta 61, 24–32 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 49.

    Lu, J., Wang, Z., Cao, J., Chen, Y. & Dong, Y. A novel and compact review on the role of oxidative stress in female reproduction. Reprod. Biol. Endocrinol. 16, 80 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 50.

    Lundberg, F. E., Iliadou, A. N., Rodriguez-Wallberg, K., Gemzell-Danielsson, K. & Johansson, A. L. V. The risk of breast and gynecological cancer in women with a diagnosis of infertility: a nationwide population-based study. Eur. J. Epidemiol. 34, 499–507 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 51.

    Yin, W., Falconer, H., Yin, L., Xu, L. & Ye, W. Association between polycystic ovary syndrome and cancer risk. JAMA Oncol. 5, 106–107 (2019).

    PubMed 

    Google Scholar
     

  • 52.

    Wolf, W. M., Wattick, R. A., Kinkade, O. N. & Olfert, M. D. Geographical prevalence of polycystic ovary syndrome as determined by region and race/ethnicity. Int. J. Environ. Res. Public Health 15, 2589 (2018).

    PubMed Central 

    Google Scholar
     

  • 53.

    Khan, M. J., Ullah, A. & Basit, S. Genetic basis of polycystic ovary syndrome (PCOS): current perspectives. Appl. Clin. Genet. 12, 249–260 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 54.

    Choudhary, A., Jain, S. & Chaudhari, P. Prevalence and symptomatology of polycystic ovarian syndrome in Indian women: is there a rising incidence?. Int. J. Reprod. Contracept. Obstet. Gynecol. 6, 4971 (2017).


    Google Scholar
     

  • 55.

    Belenkaia, L. V., Lazareva, L. M., Walker, W., Lizneva, D. V. & Suturina, L. V. Criteria, phenotypes and prevalence of polycystic ovary syndrome. Minerva Ginecol. 71, 211–225 (2019).

    PubMed 

    Google Scholar
     

  • 56.

    Nouraldein, M., Hamad, M., Abdelgadir, M. A., Omer, M. & Hussein, M. Prevalence of Stein-Leventhal syndrome. EC EC Gynaecol. 9, 52–55 (2020).


    Google Scholar
     

  • 57.

    Canese, K. & Weis, S. PubMed: The bibliographic database. NCBI Handb. (2013)

  • 58.

    Lipscomb, C. E. Medical Subject headings (MeSH). Bull. Med. Libr. Assoc. 88, 265–266 (2000).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 59.

    Brown, G. R. et al. Gene: a gene-centered information resource at NCBI. Nucleic Acids Res. 43, D36-42 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 60.

    Sherry, S. T. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. https://doi.org/10.1093/nar/29.1.308 (2001).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 61.

    Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx1098 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 62.

    Bateman, A. et al. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).


    Google Scholar
     

  • 63.

    Rose, P. W. et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw1000 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • 64.

    Gene Ontology Consortium. Gene ontology consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).


    Google Scholar
     

  • 65.

    Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).

    CAS 
    PubMed 

    Google Scholar
     

  • 66.

    Hamosh, A., Scott, A. F., Amberger, J., Valle, D. & McKusick, V. A. Online mendelian inheritance in man (OMIM). Hum. Mutat. 15, 57–61 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • 67.

    Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx1132 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • 68.

    Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1131 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • 69.

    Piñero, J. et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. https://doi.org/10.1093/nar/gkz1021 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • 70.

    NIH, N. MedGen. NIH (2016).

  • 71.

    Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. https://doi.org/10.1093/nar/gkt1113 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • 72.

    Davis, A. P. et al. The comparative toxicogenomics database: update 2019. Nucleic Acids Res. 47, D948–D954 (2019).

    CAS 
    PubMed 

    Google Scholar
     

  • 73.

    England, G. The 100,000 genomes project protocol v3 genomics England. Genomics Engl. Protoc. https://doi.org/10.6084/m9.figshare.4530893.v2 (2017).

    Article 

    Google Scholar
     

  • 74.

    Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. https://doi.org/10.1093/nar/gkt1229 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • 75.

    Flint, J. GWAS. Curr. Biol. https://doi.org/10.1016/j.cub.2013.01.040 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • 76.

    Bravo, À, Piñero, J., Queralt-Rosinach, N., Rautschka, M. & Furlong, L. I. Extraction of relations between genes and diseases from text and large-scale data analysis: Implications for translational research. BMC Bioinform. 16, 55 (2015).


    Google Scholar
     

  • 77.

    Bundschus, M., Dejori, M., Stetter, M., Tresp, V. & Kriegel, H. P. Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinform. 9, 207 (2008).


    Google Scholar
     

  • 78.

    Rani, J., Shah, A. R. & Ramachandran, S. pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts. J. Biosci. 40, 671–682 (2015).

    PubMed 

    Google Scholar
     

  • 79.

    The Lancet. ICD-11. The Lancet (Elesiver, Asterdam, 2019). https://doi.org/10.1016/S0140-6736(19)31205-X.

  • 80.

    Heat Map Chart|Basic Charts|AnyChart Documentation. Available at: https://docs.anychart.com/Basic_Charts/Heat_Map_Chart. (Accessed: 5th May 2020)

  • 81.

    Rubio-Perez, C. et al. Genetic and functional characterization of disease associations explains comorbidity. Sci. Rep. 7, 1–14 (2017).

    CAS 

    Google Scholar
     

  • 82.

    Carson, M. B., Liu, C., Lu, Y., Jia, C. & Lu, H. A disease similarity matrix based on the uniqueness of shared genes. BMC Med. Genom. 10, 26 (2017).


    Google Scholar
     

  • 83.

    Sun, K., Gonçalves, J. P., Larminie, C. & Pržulj, N. Predicting disease associations via biological network analysis. BMC Bioinform. 15, 304 (2014).


    Google Scholar
     

  • 84.

    Menche, J. et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347(80), 347–841 (2015).


    Google Scholar
     

  • 85.

    vis.js. Available at: https://visjs.org/. (Accessed: 9th May 2020)

  • 86.

    Goh, K. . Il. et al. The human disease network. Proc. Natl. Acad. Sci. USA 104, 8685–8690 (2007).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 87.

    miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database – PubMed. Available at: https://pubmed.ncbi.nlm.nih.gov/31647101/. (Accessed: 4th August 2020)

  • 88.

    Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 1695(5), 1–9 (2006).


    Google Scholar
     

  • 89.

    Rakshit, H., Rathi, N. & Roy, D. Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson’s Disease. PLoS ONE 9, e103047 (2014).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     



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