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Current Bioinformatics

Author(s): Feng Pang, Dong Shi and Lin Yuan*

DOI: 10.2174/1574893618666230428095114

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Screening and Identification of Key Genes for Cervical Cancer, Ovarian Cancer and Endometrial Cancer by Combinational Bioinformatic Analysis

Page: [647 - 657] Pages: 11

  • * (Excluding Mailing and Handling)

Abstract

Introduction: Cervical cancer, ovarian cancer and endometrial cancer are the top three cancers in women. With the rapid development of gene chip and high-throughput sequencing technology, it has been widely used to study genomic functional omics data and identify markers for disease diagnosis and treatment. At the same time, more and more public databases containing genetic data have appeared. The result of the bioinformatic analysis can provide a diagnosis of new perspectives on cell origin and differences.

Methods: In this paper, three datasets about cervical cancer, ovarian cancer and endometrial cancer from GEO were used to dig out common DEGs (differentially expressed genes) among cervical cancer/ovarian cancer/endometrial cancer. DEGs contain 400 up-regulation genes and 157 down-regulation genes.

Results: The results of GO (gene ontology) functional enrichment analysis show that the BP (biological process) changes of DEGs are mainly in cell division, mitotic nuclear division, sister chromatid cohesion, and DNA replication. The CC (cell component) function enrichments of DEGs were mainly in the nucleoplasm, nucleus, condensed chromosome kinetochore, chromosome, centromeric region. The MF (molecular function) function enrichments of DEGs were mainly in protein binding. The results of the KEGG pathway analysis showed that the upregulation DEGs were mainly enriched in retinoblastoma gene in the cell cycle, cellular senescence, oocyte meiosis, and pathways in cancer, while the downregulation DEGs enriched in thiamine metabolism, protein processing in endoplasmic reticulum. Similarly, the function of the most significant module was enriched in cell division, condensed chromosome kinetochore, and microtubule motor activity.

Conclusion: In the result, 4 of the top 10 hub genes (CCNA2, CCNB1, CDC6 and CDK1) will provide help for future biomedical experimental research.

Graphical Abstract

[1]
Vitale SG, Capriglione S, Zito G, et al. Management of endometrial, ovarian and cervical cancer in the elderly: Current approach to a challenging condition. Arch Gynecol Obstet 2019; 299(2): 299-315.
[http://dx.doi.org/10.1007/s00404-018-5006-z] [PMID: 30542793]
[2]
Cohen PA, Jhingran A, Oaknin A, Denny L. Cervical cancer. Lancet 2019; 393(10167): 169-82.
[http://dx.doi.org/10.1016/S0140-6736(18)32470-X] [PMID: 30638582]
[3]
Small W Jr, Bacon MA, Bajaj A, et al. Cervical cancer: A global health crisis. Cancer 2017; 123(13): 2404-12.
[http://dx.doi.org/10.1002/cncr.30667] [PMID: 28464289]
[4]
Stewart C, Ralyea C, Lockwood S. Ovarian cancer: An integrated review. Semin Oncol Nurs 35(2): 151-6.
[http://dx.doi.org/10.1016/j.soncn.2019.02.001]
[5]
Lheureux S, Gourley C, Vergote I, Oza AM. Epithelial ovarian cancer. Lancet 2019; 393(10177): 1240-53.
[http://dx.doi.org/10.1016/S0140-6736(18)32552-2] [PMID: 30910306]
[6]
Singh R, Som A. Identification of common candidate genes and pathways for progression of ovarian, cervical and endometrial cancers. Meta Gene 2020; 23: 100634.
[http://dx.doi.org/10.1016/j.mgene.2019.100634]
[7]
Yang L, Zeng W, Sun H, et al. Bioinformatical analysis of gene expression omnibus database associates TAF7/CCNB1, TAF7/CCNA2, and GTF2E2/CDC20 pathways with glioblastoma development and prognosis. World Neurosurg 2020; 138: e492-514.
[http://dx.doi.org/10.1016/j.wneu.2020.02.159] [PMID: 32147549]
[8]
Yuan L, Huang DS. A network-guided association mapping approach from DNA methylation to disease. Sci Rep 2019; 9(1): 5601.
[http://dx.doi.org/10.1038/s41598-019-42010-6] [PMID: 30944378]
[9]
Yuan L, Zhao J, Sun T, Shen Z. A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs. BMC Bioinformatics 2021; 22(1): 332.
[http://dx.doi.org/10.1186/s12859-021-04256-8] [PMID: 34134612]
[10]
Zhou L, Zhu W, Wang G, Cao X, Zhang X, Chen Q. Investigation of microRNA expression signatures in HCC via microRNA Gene Chip and bioinformatics analysis. Pathol Res Pract 2020; 216(6): 152982.
[http://dx.doi.org/10.1016/j.prp.2020.152982] [PMID: 32360250]
[11]
Yuan L, Guo LH, Yuan CA, et al. Integration of multi-omics data for gene regulatory network inference and application to breast cancer. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16(3): 782-91.
[http://dx.doi.org/10.1109/TCBB.2018.2866836] [PMID: 30137012]
[12]
Wattam AR, Davis JJ, Assaf R, et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 2017; 45(D1): D535-42.
[http://dx.doi.org/10.1093/nar/gkw1017] [PMID: 27899627]
[13]
Scheps KG, Hasenahuer MA, Parisi G, Targovnik HM, Fornasari MS. Curating the gnomAD database: Report of novel variants in the globin‐coding genes and bioinformatics analysis. Hum Mutat 2020; 41(1): 81-102.
[http://dx.doi.org/10.1002/humu.23925] [PMID: 31553106]
[14]
Zheng C-H, Yuan L, Sha W, Sun Z-L. Gene differential coexpression analysis based on biweight correlation and maximum clique. BMC Bioinformatics 2014; S15: S3.
[http://dx.doi.org/10.1186/1471-2105-15-S15-S3]
[15]
Yuan L, Yuan C-A, Huang D-S. FAACOSE: A fast adaptive ant colony optimization algorithm for detecting SNP epistasis. Complexity 2017; 1: 1-10.
[http://dx.doi.org/10.1155/2017/5024867]
[16]
Kang X, Bai L, Qi X, Wang J. Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis. Medicine 2020; 99(50): e23563.
[http://dx.doi.org/10.1097/MD.0000000000023563] [PMID: 33327311]
[17]
Yuan L, Zheng C-H, Xia J-F, Huang D-S. Module based differential coexpression analysis method for type 2 diabetes. Biomed Res Int 2015; 2015: 836929.
[http://dx.doi.org/10.1155/2015/836929]
[18]
Dong S, Ding Z, Zhang H, Chen Q. Identification of prognostic biomarkers and drugs targeting them in colon adenocarcinoma: A bioinformatic analysis. Integr Cancer Ther 2019; 18.
[http://dx.doi.org/10.1177/1534735419864434] [PMID: 31370719]
[19]
Weiderpass E, Hashim D, Labrèche F. Malignant tumors of the female reproductive system. In: Occupational Cancers. Berlin: Springer 2020; pp. 439-53.
[20]
Orfanelli T, Jeong JM, Doulaveris G, Holcomb K, Witkin SS. Involvement of autophagy in cervical, endometrial and ovarian cancer. Int J Cancer 2014; 135(3): 519-28.
[http://dx.doi.org/10.1002/ijc.28524] [PMID: 24122662]
[21]
Husseinzadeh N, Husseinzadeh HD. mTOR inhibitors and their clinical application in cervical, endometrial and ovarian cancers: A critical review. Gynecol Oncol 2014; 133(2): 375-81.
[http://dx.doi.org/10.1016/j.ygyno.2014.02.017] [PMID: 24556063]
[22]
Lin DI, Shah N, Tse JY, et al. Molecular profiling of mesonephric and mesonephric-like carcinomas of cervical, endometrial and ovarian origin. Gynecol Oncol Rep 2020; 34: 100652.
[http://dx.doi.org/10.1016/j.gore.2020.100652] [PMID: 33024807]
[23]
Lupi LA, Cucielo MS, Silveira HS, et al. The role of Toll-like receptor 4 signaling pathway in ovarian, cervical, and endometrial cancers. Life Sci 2020; 247: 117435.
[http://dx.doi.org/10.1016/j.lfs.2020.117435] [PMID: 32081661]
[24]
Yan X, Guo ZX, Liu XP, et al. Four novel biomarkers for bladder cancer identified by weighted gene coexpression network analysis. J Cell Physiol 2019; 234(10): 19073-87.
[http://dx.doi.org/10.1002/jcp.28546] [PMID: 30927274]
[25]
Feng H, Gu ZY, Li Q, Liu QH, Yang XY, Zhang JJ. Identification of significant genes with poor prognosis in ovarian cancer via bioinformatical analysis. J Ovarian Res 2019; 12(1): 35.
[http://dx.doi.org/10.1186/s13048-019-0508-2] [PMID: 31010415]
[26]
Javerzat J-P, Adrien B, Marta T-P, et al. The CDK Pef1 and protein phosphatase 4 oppose each other for regulating cohesin binding to fission yeast chromosomes. Elife 2019; 9: e50556.
[http://dx.doi.org/10.7554/eLife.50556] [PMID: 31895039]
[27]
Tanaka K, Yonekawa T, Kawasaki Y, et al. Fission yeast Eso1p is required for establishing sister chromatid cohesion during S phase. Mol Cell Biol 2000; 20(10): 3459-69.
[http://dx.doi.org/10.1128/MCB.20.10.3459-3469.2000] [PMID: 10779336]
[28]
Macheret M, Halazonetis TD. DNA replication stress as a hallmark of cancer. Annu Rev Pathol 2015; 10(1): 425-48.
[http://dx.doi.org/10.1146/annurev-pathol-012414-040424] [PMID: 25621662]
[29]
Campisi J. Aging, cellular senescence, and cancer. Annu Rev Physiol 2013; 75(1): 685-705.
[http://dx.doi.org/10.1146/annurev-physiol-030212-183653] [PMID: 23140366]
[30]
Schmitt CA. Cellular senescence and cancer treatment. Biochimica et Biophysica Acta 2007; 1775(1): 5-20.
[31]
Wynford-Thomas D. Cellular senescence and cancer. J Pathol 1999; 187(1): 100-11.
[http://dx.doi.org/10.1002/(SICI)1096-9896(199901)187:1<100::AID-PATH236>3.0.CO;2-T] [PMID: 10341711]
[32]
Khan H, Reale M, Ullah H, et al. Anti-cancer effects of polyphenols via targeting p53 signaling pathway: updates and future directions. Biotechnol Adv 2020; 38: 107385.
[http://dx.doi.org/10.1016/j.biotechadv.2019.04.007] [PMID: 31004736]
[33]
Wei GH, Wang X. lncRNA MEG3 inhibit proliferation and metastasis of gastric cancer via p53 signaling pathway. Eur Rev Med Pharmacol Sci 2017; 21(17): 3850-6.
[PMID: 28975980]
[34]
Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 2016; 54: 1.30.1-1.30.33..
[http://dx.doi.org/10.1002/cpbi.5]
[35]
Majumdar G, Raghow R. Trichostatin A induces a unique set of microRNAs including miR-129-5p that blocks cyclin-dependent kinase 6 expression and proliferation in H9c2 cardiac myocytes. Mol Cell Biochem 2016; 415(1-2): 39-49.
[http://dx.doi.org/10.1007/s11010-016-2675-4] [PMID: 26946427]
[36]
Cheng J, Lu X, Wang J, Zhang H, Duan P, Li C. Interactome analysis of gene expression profiles of cervical cancer reveals dysregulated mitotic gene clusters. Am J Transl Res 2017; 9(6): 3048-59.
[PMID: 28670392]
[37]
Ruan JS, Zhou H, Yang L, Wang L, Jiang ZS, Wang SM. CCNA2 facilitates epithelial-to-mesenchymal transition via the integrin αvβ3 signaling in NSCLC. Int J Clin Exp Pathol 2017; 10(8): 8324-33.
[PMID: 31966683]
[38]
Bai X, Wang W, Zhao P, et al. LncRNA CRNDE acts as an oncogene in cervical cancer through sponging miR-183 to regulate CCNB1 expression. Carcinogenesis 2020; 41(1): 111-21.
[http://dx.doi.org/10.1093/carcin/bgz166] [PMID: 31605132]
[39]
Cai H, Xiang YB, Qu S, et al. Association of genetic polymorphisms in cell-cycle control genes and susceptibility to endometrial cancer among Chinese women. Am J Epidemiol 2011; 173(11): 1263-71.
[http://dx.doi.org/10.1093/aje/kwr002] [PMID: 21454826]
[40]
Deng Y, Jiang L, Wang Y, et al. High expression of CDC6 is associated with accelerated cell proliferation and poor prognosis of epithelial ovarian cancer. Pathol Res Pract 2016; 212(4): 239-46.
[http://dx.doi.org/10.1016/j.prp.2015.09.014] [PMID: 26920249]
[41]
Wang C, Shao S, Deng L, Wang S, Zhang Y. RETRACTED ARTICLE: LncRNA SNHG12 regulates the radiosensitivity of cervical cancer through the miR-148a/CDK1 pathway. Cancer Cell Int 2020; 20(1): 554.
[http://dx.doi.org/10.1186/s12935-020-01654-5] [PMID: 33292254]
[42]
Xi Q, Huang M, Wang Y, et al. The expression of CDK1 is associated with proliferation and can be a prognostic factor in epithelial ovarian cancer. Tumour Biol 2015; 36(7): 4939-48.
[http://dx.doi.org/10.1007/s13277-015-3141-8] [PMID: 25910705]