A nomogram model integrating transcriptomics and clinical features for predicting the risk of cervical...

To identify molecular and clinical predictors of cervical lesion progression and construct an integrated nomogram model for personalized risk assessment in patients with LSIL.

Transcriptomic data from the GEO database were analyzed for differentially expressed genes (DEGs). GO and KEGG enrichment analyses identified biological functions and pathways, while STRING network screened candidate core genes. Immunohistochemistry was performed on patient samples to evaluate the expression of key proteins associated with progression. A prediction model using Cox regression and a random forest algorithm was developed based on clinical data and immunohistochemical markers, with model discrimination and potential clinical net benefit assessed by ROC and decision curve analysis (DCA).

We identified 221 upregulated and 161 downregulated DEGs. Key progression-related genes included CDKN2A, CALML5, GINS2, KCNH1, MCM2, MKI67, and ESR1. Immunohistochemical validation in 155 patients showed that Eag1, p16INK4a, and Ki-67 correlated with poor outcomes. Colposcopic lesion involvement across cervical quadrants showed an inverse association with progression, while bacterial vaginosis was a risk factor. The prediction model showed favorable discrimination (AUC: 0.914/0.753 for 1-year and 0.941/0.769 for 2-year progression). DCA suggested potential clinical net benefit across selected threshold probabilities. External validation is needed to confirm the generalizability and clinical utility of the model.

This study identifies candidate biomarkers and clinical factors associated with cervical lesion progression and provides a preliminary prediction model for risk stratification.