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

Abstract

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 via Kaplan–Meier survival analysis, time-dependent receiver operating characteristic curves, and external validation. Associations with immune infiltration, drug sensitivity, and clinical features were further investigated. Experimental validation was performed using real-time quantitative polymerase chain reaction, western blotting, and immunofluorescence staining in CSCC and normal cervical cells. Results A four-gene prognostic signature (PARP1, SLC20A1, GALNTL6, and TAGLN) was established. Patients were stratified into high- and low-risk groups, and survival was significantly worse in the high-risk group (p = 0.0034). The risk model demonstrated good predictive performance (1-/2-/3-year area under the curve = 0.72/0.76/0.83), and its performance was externally validated in an independent cohort. Notably, risk stratification was associated with distinct immune infiltration patterns (including natural killer cells and regulatory T cells), focal adhesion pathway enrichment, and differential drug sensitivity. A nomogram incorporating the risk score, race, and lymphovascular invasion exhibited improved prognostic stratification. 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.