Exploring Differentially Expressed Genes to Identify Biomarkers of Cervical Cancer: A Bioinformatics Approach - Suryandari, Dwi Anita; Yunaini, Luluk; Kristanty, Diyah; Prawiningrum, Aisyah (UI Scholars Hub, 2025)
This study explores the molecular landscape of cervical cancer through the identification and analysis of differentially expressed genes (DEGs) from the GSE63514 dataset. A high-confidence protein–protein interaction (PPI) network was constructed using the STRING database (v11.5) and visualized via Cytoscape, identifying 178 nodes and 1,052 edges. Using the CytoHubba plugin, the top 10 hub genes—TOP2A, MKI67, CDK1, BUB1, CCNB1, CCNA2, AURKA, CDC20, PLK1, and RFC4—were highlighted based on degree centrality. These genes are predominantly associated with cell cycle regulation, DNA replication, and mitotic division, and are potentially valuable as biomarkers or therapeutic targets for cervical cancer. Functional enrichment using DAVID and Enrichr tools revealed significant involvement of DEGs in ATP binding, spindle microtubule formation, and protein kinase activity, particularly within the chromosome centromeric region and nucleoplasm. KEGG pathway analysis identified key associations with the cell cycle, DNA replication, p53 signaling, and complement and coagulation cascades. Further heatmap analysis of treatment responders versus non-responders demonstrated distinct gene expression profiles, particularly of immune-related genes like C1QA, C3, and SERPING1, and proliferative markers such as TOP2A and MKI67. These findings underscore the dual role of immune and proliferative pathways in cervical cancer progression and suggest their utility in developing predictive biomarkers and personalized treatment strategies.