An innovative glutamine metabolism-related gene signature for predicting prognosis and immune landscape in cervical cancer

Background Cervical cancer (CC) is a major global malignancy affecting women. However, the precise mechanisms underlying glutamine's role in CC remain inadequately understood. This study systematically assessed the survival outcomes, immune landscape, and drug sensitivity profiles with CC patients by analyzing genes associated with glutamine metabolism. Methods Transcriptomic data for the samples were sourced from the TCGA, GTEx, and GEO databases. Prognostic genes were identified through univariate, multivariate, and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses. The predictive accuracy of the model was assessed through the analysis of receiver operating characteristic (ROC) curves. A comprehensive nomogram was developed and evaluated for accuracy using calibration and Decision Curve Analysis (DCA) curves. Kaplan-Meier (K-M) survival curves were employed to estimate overall survival. The relationship between risk scores and immune infiltration was analyzed through Single-sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT. Functional enrichment analysis and the construction of miRNA and transcription factors networks were conducted to explore the potential molecular mechanisms of the signature genes. Results This investigation identified four signature genes associated with glutamine metabolism, UCP2, LEPR, TFRC, and RNaseH2A. We successfully developed a prognostic model with strong predictive performance. In the training set, the AUC values for 1-, 3-, and 5-year survival were 0.702, 0.719, and 0.721, respectively. In the validation set, the AUC values for these time points were 0.715, 0.696, and 0.739, respectively. Patients categorized as low-risk had notably improved survival rates than those identified as high-risk (P < 0.05). Additionally, a nomogram that combines clinical data and risk scores offered improved clinical net benefits over a broad range of threshold probabilities. Functional enrichment analysis revealed that these signature genes are strongly linked to the regulation of the cell cycle and intracellular oxygen levels. Furthermore, the gene signature displayed a significant negative correlation with the infiltration levels of most immune cell types. Conclusion This novel signature demonstrates robust predictive capability for prognostic survival probabilities and immune infiltration in CC patients, providing a fresh perspective for advancing precision treatment strategies in CC.