Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

Yun-Xia Zhang, Yan-Li Zhao
2016 Computational and Mathematical Methods in Medicine  
Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and to understand the pathogenic process of CC.Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was
more » ... igned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed.Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes,VIMhad the highest weight value. Moreover, candidate genesMMP1,CDC45, andCATwere, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism.Conclusion. Candidate pathogenic genes includingMMP1,CDC45,CAT, andVIMmight be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.
doi:10.1155/2016/3186051 pmid:27034707 pmcid:PMC4789371 fatcat:xrtl2m5h65bnvn2pwdbqssuu6a