Background Lung cancer is the leading cause of cancer-related deaths worldwide. Lung adenocarcinoma (LUAD) is one of the main subtypes of lung cancer. Hundreds of metabolic genes are altered consistently in LUAD; however, their prognostic role remains to be explored. This study aimed to establish a molecular signature that can predict the prognosis in patients with LUAD based on metabolic gene expression. Methods The transcriptome expression profiles and corresponding clinical information of LUAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) between LUAD and paired non-tumor samples were identified by the Wilcoxon rank sum test. Univariate Cox regression analysis and the lasso Cox regression model were used to construct the best-prognosis molecular signature. A nomogram was established comprising the prognostic model for predicting overall survival. To validate the prognostic ability of the molecular signature and the nomogram, the Kaplan–Meier survival analysis, Cox proportional hazards model, and receiver operating characteristic analysis were used. Results The six-gene molecular signature (PFKP, PKM, TPI1, LDHA, PTGES, and TYMS) from the DEGs was constructed to predict the prognosis. The molecular signature demonstrated a robust independent prognostic ability in the training and validation sets. The nomogram including the prognostic model had a greater predictive accuracy than previous systems. Furthermore, a gene set enrichment analysis revealed several significantly enriched metabolic pathways, which suggests a correlation of the molecular signature with metabolic systems and may help explain the underlying mechanisms. Conclusions Our study identified a novel six-gene metabolic signature for LUAD prognosis prediction. The molecular signature could reflect the dysregulated metabolic microenvironment, provide potential biomarkers for predicting prognosis, and indicate potential novel metabolic molecular-targeted therapies.

中文翻译：

---

鉴定预测肺腺癌患者总生存期的六基因代谢特征

背景 肺癌是全球癌症相关死亡的主要原因。肺腺癌（LUAD）是肺癌的主要亚型之一。数百个代谢基因在 LUAD 中不断改变；然而，它们的预后作用仍有待探索。本研究旨在建立一种分子特征，可以根据代谢基因表达预测 LUAD 患者的预后。方法从癌症基因组图谱和基因表达综合数据库中获得LUAD的转录组表达谱和相应的临床信息。通过 Wilcoxon 秩和检验鉴定 LUAD 和配对非肿瘤样本之间的差异表达基因 (DEG)。单变量 Cox 回归分析和 lasso Cox 回归模型用于构建最佳预后分子特征。建立了一个列线图，包括预测总生存期的预后模型。为了验证分子特征和列线图的预后能力，使用了 Kaplan-Meier 生存分析、Cox 比例风险模型和接受者操作特征分析。结果构建了来自DEGs的六基因分子特征（PFKP、PKM、TPI1、LDHA、PTGES和TYMS）来预测预后。分子特征在训练和验证集中表现出强大的独立预后能力。包括预后模型的列线图比以前的系统具有更高的预测准确性。此外，基因集富集分析揭示了几个显着富集的代谢途径，这表明分子特征与代谢系统存在相关性，并可能有助于解释潜在机制。结论 我们的研究确定了一种用于 LUAD 预后预测的新型六基因代谢特征。分子特征可以反映失调的代谢微环境，为预测预后提供潜在的生物标志物，并表明潜在的新型代谢分子靶向疗法。