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