Bioinformatics-Driven Discovery of Signaling Pathways and Genes Influencing Cervical Cancer

Cervical cancer is a threat to women worldwide, with poor early prognosis. Cervical cancer is strongly linked to human papillomavirus (HPV), particularly high-risk HPV16 and HPV18, a prevalent gynecological malignancy. It ranks fourth among women's malignant diseases in terms of morbidity and mortality and there has been a rise in the incidence of cervical cancer among young females in recent decades. The Gene Expression Omnibus database (GEO) was used to screen and download three gene expression profiles (GSE63514, GSE6791, and GSE9750). The study used GEO2R and Venn diagrams to identify differentially expressed genes (DEGs) across three gene expression datasets. GO and KEGG Pathway Enrichment Analysis (PEA) were then performed on the DEGs to understand their biological functions and signaling pathways. Gene set enrichment analysis (GSEA) is also applied to analyze expression profiles. Additionally, a protein–protein interaction (PPI) network is constructed using the DEGs, which underwent a functionality-based enrichment investigation to identify hub genes. The proposed approach has clinical relevance in uncovering potential therapeutic targets and improving our understanding of the prognosis and treatment of the disease.