Identification Of Key Genes In The Progression Of Cervical Cancer By Bioinformatics Analysis

Objective: As a common female reproductive malignancy,cervical cancer is a serious hazard to women’s health.This disease usually develops from normal cervix infected with human papillomavirus(HPV)to cervical intraepithelial neoplasia(CIN)and then to invasive cervical cancer.But the mechanism of this process is still not clear.Therefore,this study aims to identify the key genes in the progression of cervical cancer by bioinformatics analysis,to provide new sights for molecular mechanism as well as potent biomarkers and therapeutic targets.Methods: Gene microarray GSE63514 related to cervical cancer progression was obtained from the GEO database in NCBI.By comparing the data of normal cervical epithelium samples and CIN samples as well as the CIN samples and cervical cancer samples,respectively,the genes differentially expressed(P-value <0.05 and |Log FC| ≥1)in the processes from normal to precancerosis and from precancerosis to cancer were obtained.And the overlapped genes of the two results with same expression trend were identified as the differentially expressed genes(DEGs)in the progression of cervical cancer.By the GO and KEGG pathway enrichment analysis in DAVID,the major biological processes and signaling pathways of DEGs were revealed.Next,with the information of interaction of proteins encoded by these DEGs in STRING database,the protein-protein interaction(PPI)network was constructed by Cytoscape.Using the tool of NetworkAnalyzer,hub proteins in the PPI network were screened.Whereafter,the subnetworks of PPI were extracted by the plug-in MCODE,and GO and KEGG enrichment analyses of the genes involved in each subnetwork were conducted.Then the survival analyses of all DEGs were done to find the genes which were associated with the survival of cervical cancer patients.Finally,On the basis of the above results,the most key gene which was both the hub gene in the PPI network and also linked to the cancer survival was screened.Results: In total,118 up-regulated DEGs and 132 down-regulated DEGs were identified.The up-regulated DEGs were mainly enriched in the biological processes including nuclear division,mitotic nuclear division and organelle fission,and pathways including mismatch repair,pathways in cancer and chemokine signaling pathway;while the down-regulated genes were mainly involved in icosanoid metabolic process,unsaturated fatty acid metabolic process and oxidation-reduction process as well as the pathways of arachidonic acid metabolism and serotonergic synapse.The PPI network was comprised of 123 nodes(including 67 up-regulated genes and 56 down-regulated genes)and 283 edges.After analyzed by NetworkAnalyzer,15 hub genes were obtained: RFC4,SMC4,STAT1,KNTC1,ATAD2,FBXO5,TRIP13,ESR1,CD44,CENPN,ANLN,CDK2,ECT2,KIF14 and POLQ.With the plug-in MCODE,3 subnetworks were extracted,with the up-regulated genes mainly involved in cell cycle,nuclear division,viral process chemokine signaling pathway and PI3K-Akt signaling pathway.The survival analysis of DEGs showed that the high expression level of ANLN,CA9 and PLAUR as well as the low expression level of ANXA9,EPHX2 and PSCA were associated with short survival of cervical patients.Conclusions: Based on bioinformatics analysis,we found that up-regulated DEGs in cervical cancer progression were mainly enriched in cell division process.Among all the DEGs,ANLN is not only a hub gene of PPI network,but also a gene associated with poor survival of cervical cancer patients.Therefore,we concluded that ANLN may play an important role in the progression of cervical cancer and may be used as a new biomarker and therapeutic target.