computational identification of hpv-related molecular drivers in cervical cancer

Background objective: cervical cancer is the fourth most prevalent cancer among women, accounting for about 8% of all female cancer-related deaths. human papillomavirus (hpv), a sexually transmitted infection, plays a significant role in its development. this study aims to investigate hpv’s role in cervical cancer pathogenesis using a bioinformatics approach.materials methods: this computational study utilized transcriptomic data related to cervical cancer and host-pathogen protein-protein interactions (hp-ppis) of hpv. gene expression data were sourced from the tcga database, while hpv hp-ppis were retrieved from hpidb v.3.0. differentially expressed genes/proteins (degs/deps) were identified using the edger library, applying a threshold of absolute log fold change greater than 2 and p-value less than 0.05. degs/deps were filtered based on hp-ppis to identify interactions with hpv proteins. a protein-protein interaction (ppi) network was constructed to identify hub genes/proteins relevant to cervical cancer, and pathway enrichment analysis was conducted using enrichr software.results: the analysis identified 661 interactions between degs/deps and hpv proteins. notably, the ve6 and ve7 genes in hpv showed the highest degree of interaction with human genes. key human genes such as myc, actb, jun, hdac1, ccnd1, and hspa5 were significant in the interaction network. pathway enrichment analysis indicated involvement in pathways like epstein-barr virus infection and viral carcinogenesis.conclusion: this research highlights essential hub genes and pathways in cervical cancer influenced by hpv, emphasizing the need for further in vitro and in vivo validation.