Development and Validation of a Prognostic Model for Cervical Cancer Based on

Background: Methods: Transcriptomic data were obtained from GEO and TCGA databases. Differentially expressed genes (DEGs) were identified from GSE63514 (24 normal controls, 28 CESE samples) and GSE158814 (2 negative controls, 2 CT-infection HeLa cells). Overlapping genes were defined as CT-related genes. TCGA-CESC cohort (n = 252) was split into training and validation sets. Univariate Cox regression was used to screen candidate genes, and then multivariate Cox regression were performed to construct a prognostic signature and calculate RiskScore. LASSO-Cox regression and bootstrap analysis evaluated model stability. Patients were stratified by median RiskScore. A nomogram integrating clinical factors and RiskScore was established, followed by evaluation using calibration curves, receiver operating characteristic, and decision curve analyses. Gene set enrichment analysis, immune infiltration, and drug sensitivity analyses were performed to explore the biological and therapeutic implications of the high- and low- RiskScore group. Results: A total of 31 CT-related CESC genes were identified, among which Conclusion: We established a novel prognostic model for CESC based on CT-related genes in an HPV-positive CESC background, which provides new insights into CT infection-associated immune regulation in CESC and individualized immunotherapy and targeted treatment strategies.