PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0322387 - e0322387

Published: April 30, 2025

Cervical cancer (CC) ranks as the fourth most common malignancy affecting women globally, with research highlighting a rising incidence among younger age groups. Disulfidptosis, newly identified form of regulated cell death, has been implicated in pathogenesis numerous diseases. This study employs bioinformatics analyses to explore expression profiles and functional roles disulfidptosis-related genes (DRGs) context cervical cancer. Differential analysis gene matrix CC was performed identify differentially expressed genes. The overlap between these then determined. Key hub were using multiple machine learning approaches, including LASSO regression, support vector machines (SVM), random forest (RF). These subsequently used construct predictive model, which validated external datasets ensure robustness reliability. In this study, 11 overlapping identified, four genes-BRK1, NDUFA11, RAC1, NDUFS1-were extracted techniques. diagnostic performance datasets, model constructed based on their expression. demonstrated an exceptionally high area under curve (AUC) 0.997. Moreover, AUC values exceeding 0.85 for two independent validation further confirmed model's accuracy stability. Notably, NDUFA11 BRK1 showed significant associations patient survival, prognostic importance squamous carcinoma. Using CMAP DGIdb databases, Metformin Coenzyme-I potential targeted therapies NDUFS1 respectively, offering new therapeutic avenues patients. uncovered strong association disulfidptosis developed assess risk findings offer novel insights into identifying biomarkers targets CC, paving way improved treatment strategies.

Language: Английский