Identification of Potential Diagnostic and Prognostic Biomarkers for Cervical Cancer

AbstractObjectiveTo explore potential diagnostic and prognostic markers of cervical cancer by using GEO and TCGA databases.MethodsExpression matrices related to cervical cancer were downloaded from the GEO database. Gene expression and clinical-pathological data from TCGA and GTEx were obtained from the UCSC Xena database. Differentially expressed genes (DEGs) between normal and tumor tissue samples were identified using the limma package in R. DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the ClusterProfiler package. The Cox proportional hazard regression model was used to screen significant genes. ROC curve and multivariate Cox regression analysis were used to evaluate the prognostic value of multiple clinical features.ResultsIn this study, 42 total DEGs were found, including 33 up-regulated genes and 9 down-regulated genes. GO analysis revealed that DEGs were involved in biological processes such as chromosomal segregation, nuclear division, and organelle fission. KEGG pathway analysis implicated Toll-like receptor and mismatch repair signaling pathways. 6 significant genes were identified by COX (p < 0.05) and CA9, GINS2, and SPP1 combined biomarkers divided cervical cancer patients into a high-risk group and a low-risk group. Moreover, the low-risk survival rate was significantly higher than the high-risk survival rate. Finally, multivariate Cox regression analysis showed that the combined biomarkers of CA9, GINS2, and SPP1 are independent predictors of the prognosis of cervical cancer patients.ConclusionThe GEO and TCGA databases screened out the combined biomarkers of CA9, GINS2, and SPP1, which are independent prognostic predictors of cervical cancer.