Identification of Hub Genes and Pathways in Cervical Cancer by Statistical and Bioinformatics Analysis

Abstract

In the field of bioinformatics cervical cancer is the fourth common malignant tumor of women reproductive system. The goal of this research was to evaluate Hub genes and pathways in cervical cancer by statistical and bioinformatics analysis. Several statistical methods like student’s t test, Welch t test, F test, Likelihood ratio test, Hochberg and Benjamini test used to find out differentially express genes. Altogether 603 differentially express genes are identified, from constructing protein-protein interaction network of differentially express genes; the top ten Hub genes with relatively high degree of connectivity (over 58 in PPI network) are identified. These top ten Hub genes are - KIF2C, RAD21, MAD2LI, TOP2A, BIRC5, KIF11, MCM5, PCNA, MCM4, and SMC3. Then we have applied the KEGG pathway enrichment analysis of these Hub genes and we have found six hub genes (KIF2C, RAD21, MAD2LI, TOP2A, BIRC5, KIF11) are significantly enriched in cell cycle, and four hub genes (MCM5, PCNA, MCM4, SMC3) are significantly enriched DNA replication. These results might hold promise for finding potential therapeutic targets of cervical cancer.