Fang Hai-Ya, Ji Li-Mei, Hong Cui-Hua
Department of Obstetrics and Gynecology, Jinhua Municipal Central Hospital, Jinhua, 321000, China.
Department of Obstetrics and Gynecology, Wenzhou Central Hospital, No.252 East Baili Road, Wenzhou, 325100, China.
Discov Oncol. 2025 Mar 20;16(1):368. doi: 10.1007/s12672-025-02109-x.
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.
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.
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.
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.
宫颈癌（CC）是一种影响全球女性的主要恶性肿瘤。然而，谷氨酰胺在CC中作用的精确机制仍未得到充分理解。本研究通过分析与谷氨酰胺代谢相关的基因，系统评估了CC患者的生存结局、免疫格局和药物敏感性特征。
样本的转录组数据来自TCGA、GTEx和GEO数据库。通过单变量、多变量和最小绝对收缩和选择算子（LASSO）回归分析确定预后基因。通过分析受试者工作特征（ROC）曲线评估模型的预测准确性。开发了一个综合列线图，并使用校准和决策曲线分析（DCA）曲线评估其准确性。采用Kaplan-Meier（K-M）生存曲线估计总生存期。通过单样本基因集富集分析（ssGSEA）和CIBERSORT分析风险评分与免疫浸润之间的关系。进行功能富集分析以及构建miRNA和转录因子网络，以探索特征基因的潜在分子机制。
本研究确定了四个与谷氨酰胺代谢相关的特征基因，即UCP2、LEPR、TFRC和RNaseH2A。我们成功开发了一个具有强大预测性能的预后模型。在训练集中，1年、3年和5年生存的AUC值分别为0.702、0.719和0.721。在验证集中，这些时间点的AUC值分别为0.715、0.696和0.739。分类为低风险的患者的生存率明显高于高风险患者（P < 0.05）。此外，结合临床数据和风险评分的列线图在广泛的阈值概率范围内提供了更好的临床净效益。功能富集分析表明，这些特征基因与细胞周期和细胞内氧水平的调节密切相关。此外，基因特征与大多数免疫细胞类型的浸润水平呈显著负相关。
这种新的特征对CC患者的预后生存概率和免疫浸润具有强大的预测能力，为推进CC的精准治疗策略提供了新的视角。
