Prediction of Cervical Cancer Progression Leveraging HPV16 Integration-Related Genes

Cervical cancer (CC) remains a significant global health burden among women, particularly in cases of advanced or recurrent disease. Current clinical parameters exhibit suboptimal accuracy in predicting disease progression. Given that HPV integration is a well-established oncogenic driver in cervical carcinogenesis, there is growing interest in leveraging HPV-related molecular signatures to improve risk stratification and guide personalized treatment strategies.

Our study design employed HPV16-postive samples from TCGA-CESC as the training set (n = 95) and a local cervical cancer cohort (n = 118) for independent validation. From differentially expressed genes (DEGs) identified in HPV16-integrated HaCaT cells, we developed a prognostic 9-gene signature through a rigorous two-stage selection process: feature reduction using LASSO regression along with 10-fold cross-validation, followed by stepwise Cox regression. The risk score's predictive performance was systematically evaluated through Kaplan-Meier survival analysis, time-dependent ROC curves, ROC over time profiling, calibration plots, and nomogram construction. Mechanistic investigations included functional enrichment analysis, mutational profiling, and drug sensitivity prediction.

The 9-gene signature (LCP1, CXCL11, NEK6, MCAM, PRRX2, NPL, PGLYRP3, SPRR3 and MMP1) demonstrated superior predictive accuracy compared to conventional clinical parameters. Mechanistic investigations revealed that the signature genes collectively influence tumor progression through two key pathways: modulation of tumor immune microenvironment and regulation of oncogenic mutation patterns. These findings were consistently supported by both functional enrichment analysis and comprehensive mutational profiling. Furthermore, pharmacological inhibition of NRF2 signaling may overcome cisplatin resistance in high-risk patients with NFE2L2-mutant tumors. While the signature shows significant clinical potential, further independent validation is required before it can be adopted into routine clinical practice.

We developed a robust nine-gene prognostic model for predicting Progression-Free Survival (PFS) in CC, which provides novel insights into HPV-associated oncogenesis and facilitates risk stratification and therapeutic decision-making in CC management.