Study on pathogenesis of cervical cancer based on bioinformatics methods

摘要

Aim: The need for new therapeutics in cervical cancer (CC) is highlighted by the general lack of efficacy for most currently available agents for this disease. The aim of this study was to find the pathogenesis of CC. Materials and Methods: To explore the pathogenesis of CC by bioinformatics methods, teh authors obtained the microarray data GSE9750 from Gene Expression Omnibus (GEO) database, and identified the differentially expressed genes (DEGs) among classic cell line samples, normal cervical epithelium samples, and primary CC cell samples by Arraytools software, followed by the protein-protein interaction (PPI) network analysis of the DEGs using String 9.1 software. Additionally, functions of effective genes and dysfunctional pathways were enriched by Gene Ontology and Expressing Analysis Systematic Explorer (GOEASE) and Database for Annotation, Visualization and Integration Discovery (DAVID) online software. Results: The authors attained plenty of up-regulated DEGs (406/120/422) and down-regulated DEGs (746/472/387) by comparing different samples. PPI network construction found that CDK1, CCNA2, MAD2L1, KIF11, RRM2, and PBK might play a vital role in the progression. Total 10 Gene Ontology (GO) terms were enriched and the enriched terms can be generally classified into three groups: cell cycle, non-membrane-bounded organelle and all kinds of enzyme activities. Furthermore, this study showed that signaling pathway, DNA replication, and pathway in cancer may involve in the development of CC. Conclusions: The present data provides a comprehensive bioinformatics analysis of genes and pathways which may be involved in the pathogenesis of CC. However, further experiments are still needed to confirm these results.