Volume 33, Issue 11 (February 2023)
Studies in Medical Sciences 2023, 33(11): 768-785 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Tabatabaei S N, Minuchehr Z. COMPUTATIONAL BIOLOGY APPROACHES AND BIOINFORMATICS TO IDENTIFY KEY GENES IN POLYCYSTIC OVARY SYNDROME: A SYSTEMATIC REVIEW. Studies in Medical Sciences 2023; 33 (11) :768-785
URL: http://umj.umsu.ac.ir/article-1-5832-en.html
URL: http://umj.umsu.ac.ir/article-1-5832-en.html
Associate Professor of Department of Systems Biotechnology, Industry and Environment Biotechnology Research Institute, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran (Corresponding Author) , dminuchehr@gmail.com
Abstract: (1135 Views)
Background & Aims: Polycystic ovary syndrome (PCOS) is the most common female endocrine disease and often causes infertility in the women of reproductive age. This syndrome includes different genes and proteins, multiple pathways, and complex processes of hormone secretion. Therefore, a single factor cannot explain the pathogenesis of PCOS. Using computational biology and omicses including genomics, transcriptomics, proteomics, and metabolomics, can provide faster and more effective methods for studying the pathogenesis of complex diseases such as PCOS.
Materials & Methods: In this study, to find the related studies, PubMed, Google Scholar, and Science Direct databases were searched without time constraints for 3 years using the keywords of "Polycystic Ovary Syndrome, Computational Biology, protein-protein interaction, Network Biology, and Pathways analysis".
Results: Various databases have been designed and made available to the public to repository data, Protein-Protein interactions, networks, and biological pathways related to humans. Three databases PCOSBase, PCOSKB, and PCOSDB have been explicitly developed for polycystic ovary syndrome.
Conclusion: Since the molecular mechanisms of polycystic ovary syndrome are still not completely understood, to realize this syndrome, besides experimental results using omics platforms, computational biology, and bioinformatics tools, it is necessary to identify the interaction between proteins and the pathways involved in it.
Materials & Methods: In this study, to find the related studies, PubMed, Google Scholar, and Science Direct databases were searched without time constraints for 3 years using the keywords of "Polycystic Ovary Syndrome, Computational Biology, protein-protein interaction, Network Biology, and Pathways analysis".
Results: Various databases have been designed and made available to the public to repository data, Protein-Protein interactions, networks, and biological pathways related to humans. Three databases PCOSBase, PCOSKB, and PCOSDB have been explicitly developed for polycystic ovary syndrome.
Conclusion: Since the molecular mechanisms of polycystic ovary syndrome are still not completely understood, to realize this syndrome, besides experimental results using omics platforms, computational biology, and bioinformatics tools, it is necessary to identify the interaction between proteins and the pathways involved in it.
Keywords: Computational Biology, Network Biology, Pathway Analysis, Polycystic Ovarian Syndrome, Protein-Protein Interaction Analysis
Type of Study: Review article |
Subject:
ژنتیک
References
1. Cunha A, Póvoa AM. Infertility management in women with polycystic ovary syndrome: a review. Porto Biomed J 2021a;6(1):e116. Available from: http://dx.doi.org/10.1097/j.pbj.0000000000000116. [DOI:10.1097/j.pbj.0000000000000116] [PMID] [PMCID]
2. Pourhoseini SA, Babazadeh R, Abedian Z, Mazloum SR. Frequency of the Phenotypes of Polycystic Ovarian Syndrome in Iranian Adolescents (Mashhad) based on Rotterdam Criteria in 2018. Iranian J Obstetr Gynecol Infertility 2021;24(2):14-22. [Google Scholar]
3. Rajska A, Buszewska-Forajta M, Rachoń D, Markuszewski MJ. Metabolomic insight into polycystic ovary syndrome-An overview. Int J Mol Sci 2020;21(14):4853. [DOI:10.3390/ijms21144853] [PMID] [PMCID]
4. Nautiyal H, Imam SS, Alshehri S, Ghoneim MM, Afzal M, Alzarea SI, et al. Polycystic Ovarian Syndrome: A Complex Disease with a Genetics Approach. Biomedicines 2022;10(3):540. [DOI:10.3390/biomedicines10030540] [PMID] [PMCID]
5. Abdolahian S, Ramezani Tehrani F, Nahidi F, Ghodsi D, Jafari M, Alavi Majd H. Relationship between body mass index and the clinical predictors of polycystic ovary syndrome in adolescent girls. Iranian J Obstetr Gynecol Infertility 2021;24(10):40-7. [URL]
6. Zare Bidaki F, Chaichian S, Arbabi Bidgoli S. The role of oral Curcumin Nanomicell on improvement of menstrual disorders, depression and anxiety in women with Polycystic Ovarian Syndrome. Iranian J Obstetr Gynecol Infertility 2021;24(1):55-66. [Google Scholar]
7. Egea RR, Puchalt NG, Escrivá MM, Varghese AC. OMICS: current and future perspectives in reproductive medicine and technology. J Hum Reprod Sci 2014;7(2):73. [DOI:10.4103/0974-1208.138857] [PMID] [PMCID]
8. Minuchehr Z, Kheitan S. Bioinformatics in a Nutshell. Bioinf Biocomp Res 2016;1:34-41. [Google Scholar]
9. Afiqah-Aleng N, Mohamed-Hussein Z-A. Computational systems analysis on polycystic ovarian syndrome (PCOS). Polycystic Ovarian Syndrome 2020:125-48. [DOI:10.5772/intechopen.89490]
10. Barabasi A-L, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet 2004;5(2):101-13. [DOI:10.1038/nrg1272] [PMID]
11. Barabási A-L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet 2011;12(1):56-68. [DOI:10.1038/nrg2918] [PMID] [PMCID]
12. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature 2009;461(7261):218-23. [DOI:10.1038/nature08454] [PMID]
13. Wachi S, Yoneda K, Wu R. Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics 2005;21(23):4205-8. [DOI:10.1093/bioinformatics/bti688] [PMID] [PMCID]
14. Oti M, Snel B, Huynen MA, Brunner HG. Predicting disease genes using protein-protein interactions. J Med Genet 2006;43(8):691-8. [DOI:10.1136/jmg.2006.041376] [PMID] [PMCID]
15. Liu W, Wu A, Pellegrini M, Wang X. Integrative analysis of human protein, function and disease networks. Sci Rep 2015;5(1):1-11. [DOI:10.1038/srep14344] [PMID] [PMCID]
16. Navlakha S, Kingsford C. The power of protein interaction networks for associating genes with diseases. Bioinformatics 2010;26(8):1057-63. [DOI:10.1093/bioinformatics/btq076] [PMID] [PMCID]
17. Adamcsek B, Palla G, Farkas IJ, Derényi I, Vicsek T. CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 2006;22(8):1021-3. [DOI:10.1093/bioinformatics/btl039] [PMID]
18. Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 2012;9(5):471-2. [DOI:10.1038/nmeth.1938] [PMID] [PMCID]
19. Liu G, Wong L, Chua HN. Complex discovery from weighted PPI networks. Bioinformatics 2009;25(15):1891-7. [DOI:10.1093/bioinformatics/btp311] [PMID]
20. Palla G, Derényi I, Farkas I, Vicsek T. Uncovering the overlapping community structure of complex networks in nature and society. Nature 2005;435(7043):814-8. [DOI:10.1038/nature03607] [PMID]
21. Altaf-Ul-Amin M, Shinbo Y, Mihara K, Kurokawa K, Kanaya S. Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinf 2006;7(1):1-13. [DOI:10.1186/1471-2105-7-207] [PMID] [PMCID]
22. Bozlul KM, Wakamatsu N, Altaf-Ul-Amin M. DPClusOST: a software tool for general purpose graph clustering. J Compu Aided Chem 2017;18:76-93. [DOI:10.2751/jcac.18.76]
23. Li M, Chen J-e, Wang J-x, Hu B, Chen G. Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC Bioinf 2008;9(1):1-16.
https://doi.org/10.1186/s12859-016-1414-x [DOI:10.1186/1471-2105-9-398] [PMID] [PMCID]
24. Li X-L, Foo C-S, Tan S-H, Ng S-K. Interaction graph mining for protein complexes using local clique merging. Genome Inform 2005;16(2):260-9. [Google Scholar]
25. King AD, Pržulj N, Jurisica I. Protein complex prediction via cost-based clustering. Bioinformatics 2004;20(17):3013-20. [DOI:10.1093/bioinformatics/bth351] [PMID]
26. Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 2002;30(7):1575-84. [DOI:10.1093/nar/30.7.1575] [PMID] [PMCID]
27. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinf 2003;4(1):1-27. [DOI:10.1186/1471-2105-4-1] [PMID] [PMCID]
28. Anvar MS, Minuchehr Z, Shahlaei M, Kheitan S. Gastric cancer biomarkers; A systems biology approach. Biochem Biophys Rep 2018;13:141-6. [DOI:10.1016/j.bbrep.2018.01.001] [PMID] [PMCID]
29. García-Campos MA, Espinal-Enríquez J, Hernández-Lemus E. Pathway analysis: state of the art. Front Physiol 2015;6:383. [DOI:10.3389/fphys.2015.00383] [PMID] [PMCID]
30. Torres-Ayuso P, Sahoo S, Ashton G, An E, Simms N, Galvin M, et al. Signaling pathway screening platforms are an efficient approach to identify therapeutic targets in cancers that lack known driver mutations: A case report for a cancer of unknown primary origin. NPJ Genomic Med 2018;3(1):1-7. [DOI:10.1038/s41525-018-0055-6] [PMID] [PMCID]
31. Varala K, Marshall-Colón A, Cirrone J, Brooks MD, Pasquino AV, Léran S, et al. Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants. Proc Natl Acad Sci U S A 2018;115(25):6494-9. Available from: http://dx.doi.org/10.1073/pnas.1721487115. [DOI:10.1073/pnas.1721487115] [PMID] [PMCID]
32. Doong SJ, Gupta A, Prather KL. Layered dynamic regulation for improving metabolic pathway productivity in Escherichia coli. Proc Natl Acad Sci U S A 2018;115(12):2964-9. [DOI:10.1073/pnas.1716920115] [PMID] [PMCID]
33. Kitano H. Computational systems biology. Nature 2002;420(6912):206-10. [DOI:10.1038/nature01254] [PMID]
34. Kolesnikov N, Hastings E, Keays M, Melnichuk O, Tang YA, Williams E, et al. ArrayExpress update-simplifying data submissions. Nucleic Acids Res 2015;43(D1):D1113-D6. [DOI:10.1093/nar/gku1057] [PMID] [PMCID]
35. Clough E, Barrett T. The gene expression omnibus database. Statistical genomics: Springer; 2016. p. 93-110. [DOI:10.1007/978-1-4939-3578-9_5] [PMID] [PMCID]
36. Ikeo K, Ishi-i J, Tamura T, Gojobori T, Tateno Y. CIBEX: center for information biology gene expression database. C R Biol 2003;326(10-11):1079-82. [DOI:10.1016/j.crvi.2003.09.034] [PMID]
37. Chen Q, Zheng B, Du S, Lin Y. Explore the potential molecular mechanism of polycystic ovarian syndrome by protein-protein interaction network analysis. Taiwan J Obstet Gynecol 2021;60(5):807-15. [DOI:10.1016/j.tjog.2021.07.005] [PMID]
38. Zou J, Li Y, Liao N, Liu J, Zhang Q, Luo M, et al. Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis. J Ovarian Res 2022;15(1):1-16. [DOI:10.1186/s13048-022-00962-w] [PMID] [PMCID]
39. Pei C-Z, Jin L, Baek K-H. Pathogenetic analysis of polycystic ovary syndrome from the perspective of omics. Biomed Pharmacother 2021;142:112031. [DOI:10.1016/j.biopha.2021.112031] [PMID]
40. Chen Z-J, Zhao H, He L, Shi Y, Qin Y, Shi Y, et al. Genome-wide association study identifies susceptibility loci for polycystic ovary syndrome on chromosome 2p16. 3, 2p21 and 9q33. 3. Nat Genet 2011;43(1):55-9. [DOI:10.1038/ng.732] [PMID]
41. Shi Y, Zhao H, Shi Y, Cao Y, Yang D, Li Z, et al. Genome-wide association study identifies eight new risk loci for polycystic ovary syndrome. Nat Genet 2012;44(9):1020-5. [DOI:10.1038/ng.2384] [PMID]
42. Hayes MG, Urbanek M, Ehrmann DA, Armstrong LL, Lee JY, Sisk R, et al. Genome-wide association of polycystic ovary syndrome implicates alterations in gonadotropin secretion in European ancestry populations. Nat Comm 2015;6(1):1-13. [DOI:10.1038/ncomms8502] [PMID] [PMCID]
43. Day FR, Hinds DA, Tung JY, Stolk L, Styrkarsdottir U, Saxena R, et al. Causal mechanisms and balancing selection inferred from genetic associations with polycystic ovary syndrome. Nat Comm 2015;6(1):1-7. [DOI:10.1038/ncomms9464] [PMID] [PMCID]
44. Lan C-W, Chen M-J, Tai K-Y, Yu DC, Yang Y-C, Jan P-S, et al. Functional microarray analysis of differentially expressed genes in granulosa cells from women with polycystic ovary syndrome related to MAPK/ERK signaling. Sci Rep 2015;5(1):1-10. [DOI:10.1038/srep14994] [PMID] [PMCID]
45. Ambekar AS, Kelkar DS, Pinto SM, Sharma R, Hinduja I, Zaveri K, et al. Proteomics of follicular fluid from women with polycystic ovary syndrome suggests molecular defects in follicular development. J Clin Endocrin Metabol 2015;100(2):744-53. [DOI:10.1210/jc.2014-2086] [PMID] [PMCID]
46. Dong F, Deng D, Chen H, Cheng W, Li Q, Luo R, et al. Serum metabolomics study of polycystic ovary syndrome based on UPLC-QTOF-MS coupled with a pattern recognition approach. Anal Bioanal Chem 2015;407(16):4683-95. [DOI:10.1007/s00216-015-8670-x] [PMID]
47. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Nat Acad Sci 2001;98(8):4569-74. [DOI:10.1073/pnas.061034498] [PMID] [PMCID]
48. Fossum E, Friedel CC, Rajagopala SV, Titz B, Baiker A, Schmidt T, et al. Evolutionarily conserved herpesviral protein interaction networks. PLoS Pathogens 2009;5(9):e1000570. [DOI:10.1371/journal.ppat.1000570] [PMID] [PMCID]
49. Peregrin-Alvarez JM, Xiong X, Su C, Parkinson J. The modular organization of protein interactions in Escherichia coli. PLoS Comput Biol 2009;5(10):e1000523. [DOI:10.1371/journal.pcbi.1000523] [PMID] [PMCID]
50. Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, Ignatchenko A, et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006;440(7084):637-43. [DOI:10.1038/nature04670] [PMID]
51. Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, et al. A map of the interactome network of the metazoan C. elegans. Science 2004;303(5657):540-3. [DOI:10.1126/science.1091403] [PMID] [PMCID]
52. Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, et al. A protein interaction map of Drosophila melanogaster. Science 2003;302(5651):1727-36. [DOI:10.1126/science.1090289] [PMID]
53. Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 2005;437(7062):1173-8. [DOI:10.1038/nature04209] [PMID]
54. Kori M, Gov E, Arga KY. Molecular signatures of ovarian diseases: Insights from network medicine perspective. Syst Biol Reprod Med 2016;62(4):266-82. [DOI:10.1080/19396368.2016.1197982] [PMID]
55. Ramly B, Afiqah-Aleng N, Mohamed-Hussein Z-A. Protein-protein interaction network analysis reveals several diseases highly associated with polycystic ovarian syndrome. Int J Mol Sci 2019;20(12):2959. [DOI:10.3390/ijms20122959] [PMID] [PMCID]
56. Alaimo S, Marceca GP, Ferro A, Pulvirenti A. Detecting disease specific pathway substructures through an integrated systems biology approach. Noncoding RNA 2017;3(2):20. [DOI:10.3390/ncrna3020020] [PMID] [PMCID]
57. Alonso-López D, Campos-Laborie FJ, Gutiérrez MA, Lambourne L, Calderwood MA, Vidal M, et al. APID database: redefining protein-protein interaction experimental evidences and binary interactomes. Database 2019;2019. [DOI:10.1093/database/baz005] [PMID] [PMCID]
58. Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006;34(suppl_1):D535-D9. [DOI:10.1093/nar/gkj109] [PMID] [PMCID]
59. Xenarios I, Fernandez E, Salwinski L, Duan XJ, Thompson MJ, Marcotte EM, et al. DIP: the database of interacting proteins: 2001 update. Nucleic Acids Res 2001;29(1):239-41. [DOI:10.1093/nar/29.1.239] [PMID] [PMCID]
60. Franz M, Rodriguez H, Lopes C, Zuberi K, Montojo J, Bader GD, et al. GeneMANIA update 2018. Nucleic Acids Res 2018;46(W1):W60-W4. [DOI:10.1093/nar/gky311] [PMID] [PMCID]
61. Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH. HIPPIE v2. 0: enhancing meaningfulness and reliability of protein-protein interaction networks Nucleic Acids Res 2016:gkw985. [DOI:10.1093/nar/gkw985] [PMID] [PMCID]
62. Hoffmann R, Valencia A. A gene network for navigating the literature. Nature genetics 2004;36(7):664-. [DOI:10.1038/ng0704-664] [PMID]
63. Keshava Prasad T, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, et al. Human protein reference database-2009 update. Nucleic Acids Res 2009;37(suppl_1):D767-D72. [DOI:10.1093/nar/gkn892] [PMID] [PMCID]
64. Brown KR, Jurisica I. Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biol 2007;8(5):1-11. [DOI:10.1186/gb-2007-8-5-r95] [PMID] [PMCID]
65. Breuer K, Foroushani AK, Laird MR, Chen C, Sribnaia A, Lo R, et al. InnateDB: systems biology of innate immunity and beyond-recent updates and continuing curation. Nucleic Acids Res 2013;41(D1):D1228-D33. [DOI:10.1093/nar/gks1147] [PMID] [PMCID]
66. Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, et al. The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 2014;42(D1):D358-D63. [DOI:10.1093/nar/gkt1115] [PMID] [PMCID]
67. Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 2012;40(D1):D857-D61. [DOI:10.1093/nar/gkr930] [PMID] [PMCID]
68. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2016:gkw937. [DOI:10.1093/nar/gkw937] [PMID] [PMCID]
69. Launay G, Salza R, Multedo D, Thierry-Mieg N, Ricard-Blum S. MatrixDB, the extracellular matrix interaction database: updated content, a new navigator and expanded functionalities. Nucleic Acids Res 2015;43(D1):D321-D7. [DOI:10.1093/nar/gku1091] [PMID] [PMCID]
70. Han K, Park B, Kim H, Hong J, Park J. HPID: the human protein interaction database. Bioinformatics 2004;20(15):2466-70. [DOI:10.1093/bioinformatics/bth253] [PMID]
71. Orchard S, Kerrien S, Abbani S, Aranda B, Bhate J, Bidwell S, et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat Methods 2012;9(4):345-50. [DOI:10.1038/nmeth.1931] [PMID] [PMCID]
72. Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, et al. The MIPS mammalian protein-protein interaction database. Bioinformatics 2005;21(6):832-4. [DOI:10.1093/bioinformatics/bti115] [PMID]
73. Nishimura D. BioCarta. Biotech Software & Internet Report. Comput Software J Sci 2001;2(3):117-20. [DOI:10.1089/152791601750294344]
74. Trupp M, Altman T, Fulcher CA, Caspi R, Krummenacker M, Paley S, et al. Beyond the genome (BTG) is a (PGDB) pathway genome database: HumanCyc. Genome Biol 2010;11(1):1-. [DOI:10.1186/gb-2010-11-s1-o12] [PMCID]
75. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 2017;45(D1):D353-D61. [DOI:10.1093/nar/gkw1092] [PMID] [PMCID]
76. Mi H, Thomas P. PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Protein networks and pathway analysis: Springer; 2009. p. 123-40. [DOI:10.1007/978-1-60761-175-2_7] [PMID] [PMCID]
77. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The reactome pathway knowledgebase. Nucleic Acids Res 2018;46(D1):D649-D55. [DOI:10.1093/nar/gkx1132] [PMID] [PMCID]
78. Pico AR, Kelder T, Van Iersel MP, Hanspers K, Conklin BR, Evelo C. WikiPathways: pathway editing for the people. PLoS Biol 2008;6(7):e184. [DOI:10.1371/journal.pbio.0060184] [PMID] [PMCID]
79. Afiqah-Aleng N, Harun S, Nor Muhammad NA, Mohamed-Hussein Z-A. PCOSBase: a manually curated database of polycystic ovarian syndrome. Database 2017;2017. [DOI:10.1093/database/bax098] [PMID] [PMCID]
80. Joseph S, Barai RS, Bhujbalrao R, Idicula-Thomas S. PCOSKB: A KnowledgeBase on genes, diseases, ontology terms and biochemical pathways associated with PolyCystic Ovary Syndrome. Nucleic Acids Res 2016;44(D1):D1032-D5. [DOI:10.1093/nar/gkv1146] [PMID] [PMCID]
81. Maniraja J, Vetrivel U, Munuswamy D, Melanathuru V. PCOSDB: PolyCystic ovary syndrome DataBase for manually curated genes associated with the disease. Bioinformation 2016;12:4-8. [DOI:10.6026/97320630012004] [PMID] [PMCID]
82. Geronikolou SA, Pavlopoulou A, Cokkinos DV, Bacopoulou F, Chrousos GP. Polycystic οvary syndrome revisited: an interactions network approach. Eur J Clin Invest 2021;51(9):e13578. [DOI:10.1111/eci.13578] [PMID]
83. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 2016:gkw943. [DOI:10.1093/nar/gkw943] [PMID] [PMCID]
84. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 2014;42(D1):D1001-D6. [DOI:10.1093/nar/gkt1229] [PMID] [PMCID]
85. Stenson PD, Mort M, Ball EV, Evans K, Hayden M, Heywood S, et al. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum Genetics 2017;136(6):665-77. [DOI:10.1007/s00439-017-1779-6] [PMID] [PMCID]
86. Agrawal S, Dimitrova N, Nathan P, Udayakumar K, Lakshmi SS, Sriram S, et al. T2D-Db: an integrated platform to study the molecular basis of Type 2 diabetes. BMC Genomics 2008;9(1):1-12. [DOI:10.1186/1471-2164-9-320] [PMID] [PMCID]
87. Bai Z, Han G, Xie B, Wang J, Song F, Peng X, et al. AlzBase: an integrative database for gene dysregulation in Alzheimer's disease. Mol Neurobiol 2016;53(1):310-9. [DOI:10.1007/s12035-014-9011-3] [PMID]
88. Hutter C, Zenklusen JC. The cancer genome atlas: creating lasting value beyond its data. Cell 2018;173(2):283-5. [DOI:10.1016/j.cell.2018.03.042] [PMID]
89. Taghvaei S, Sabouni F, Minuchehr Z. Evidence of Omics, Immune Infiltration, and Pharmacogenomic for SENP1 in the Pan-Cancer Cohort. Front Pharmacol 2021;12. [DOI:10.3389/fphar.2021.700454] [PMID] [PMCID]
90. Saberi M, Minuchehr Z, Shahlaei M, Kheitan S. An in silico method to identify key proteins involved in the development of gastric cancer. Res Med 2017;41(3):199-209. [Google Scholar]
91. Islam MR, Ahmed ML, Paul BK, Bhuiyan T, Ahmed K, Moni MA. Identification of the core ontologies and signature genes of polycystic ovary syndrome (PCOS): A bioinformatics analysis. Inform Med Unlocked 2020;18:100304. [DOI:10.1016/j.imu.2020.100304]
92. Liang Q-Q, Wang D-J. Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Polycystic Ovary Syndrome. J Bioinf Syst Biol 2022;5:78-92. [DOI:10.26502/jbsb.5107036]
93. Devarbhavi P, Telang L, Vastrad B, Tengli A, Vastrad C, Kotturshetti I. Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules. Reprod Biol Endocrinol 2021;19(1):1-39. [DOI:10.1186/s12958-021-00706-3] [PMID] [PMCID]
94. Yang D, Li N, Ma A, Dai F, Zheng Y, Hu X, et al. Identification of potential biomarkers of polycystic ovary syndrome via integrated bioinformatics analysis. Reprod Sci 2021;28(5):1353-61. [DOI:10.1007/s43032-020-00352-x] [PMID]
95. Hossain MA, Al Amin M, Hasan MI, Sohel M, Ahammed MA, Mahmud SH, et al. Bioinformatics and system biology approaches to identify molecular pathogenesis of polycystic ovarian syndrome, type 2 diabetes, obesity, and cardiovascular disease that are linked to the progression of female infertility. Inf Med Unlocked 2022;30:100960. [DOI:10.1016/j.imu.2022.100960]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
