Comprehensive analysis of potential biomarkers for the diagnosis and prognosis of Cervical squamous cell carcinoma - based on GEO and TCGA databases.

BACKGROUND

Cervical squamous cell carcinoma (CESC) constitutes a substantial global health burden, especially in resource-limited regions. The identification of reliable biomarkers is critical for developing a clinically applicable nomogram to predict survival outcomes and evaluate immune infiltration in CESC patients.

METHODS

This study integrated RNA-seq data from GEO and TCGA databases to identify key genes associated with CESC through differential expression analysis and machine learning techniques. Prognostic models were constructed and validated, with additional analyses exploring immune cell infiltration and gene function via GSEA and clinical correlation. Finally, key genes were validated via qRT-PCR in CESC tissues.

RESULTS

A total of 112 differentially expressed genes (DEGs) were identified through differential analysis of the GEO and TCGA datasets. , , and emerged as prognostic biomarkers for CESC, showing significant associations with survival, tumor stage, and immune infiltration. may drive tumor progression via the MAPK signaling pathway, could influence immune evasion through NOD-like receptor signaling, and may contribute to tumor invasion by modulating extracellular matrix remodeling. A nomogram integrating these genes demonstrated high predictive accuracy for overall survival (AUC>0.75) and calibration plots. Decision curve analysis (DCA) was performed to assess the nomogram's clinical utility and net benefit for application in clinical practice. Additionally, it was validated by qRT-PCR, showing elevated expression in tumors versus normal tissues (<0.05).

CONCLUSION

, , and are promising biomarkers for CESC prognosis and immune regulation. The nomogram model provides a practical tool for personalized survival prediction, enhancing clinical decision-making for immunotherapy and risk stratification.