Transcriptomics, identification, and testing of manganese metabolism-related genes in cervical squamous cell carcinoma.

Background: Cervical squamous cell carcinoma (CSCC) remains a leading cause of cancer-related mortality in women, and few effective prognostic biomarkers have been identified. Although manganese metabolism (MAM) has been implicated in tumorigenesis and immune regulation, its prognostic relevance in CSCC has not been systematically explored, and existing prognostic models for CSCC have largely ignored MAM-related genes. Methods: CSCC transcriptomic and clinical datasets were obtained from public databases. MAM-related differentially expressed genes (DEGs) were identified by intersecting DEGs, weighted gene coexpression network analysis key module genes, and a curated MAM gene set. Prognostic genes were screened using univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox regression analyses to construct a risk model. The model's performance was assessed Results: A four-gene prognostic signature ( Conclusion: This study presents the first MAM-related prognostic signature specifically tailored to CSCC, filling a gap in existing prognostic models that did not consider the role of MAM. The four-gene risk model offers a robust tool for patient stratification, with potential implications for guiding personalized immunotherapy and targeted therapy. These findings provide a novel framework for incorporating metabolic perspectives into CSCC prognosis and warrant further clinical validation.