Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

摘要: Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and understand the pathogenic process CC. Methods. A network CC extracted based on known (seed genes) differentially expressed (DEGs) between normal controls. Subsequently, cluster analysis performed identify subnetworks ClusterONE. Each gene assigned weight value, then were obtained distribution. Eventually, pathway enrichment for performed. Results. In this work, total 330 DEGs identified From network, 2 intensely connected clusters extracted, 52 detected under values greater than 0.10. Among these genes, VIM had highest value. Moreover, MMP1, CDC45, CAT were, respectively, enriched cancer, cell cycle, methane metabolism. Conclusion. Candidate including CAT, might be involved pathogenesis We believe that results can provide theoretical guidelines future clinical application.