Zhao Zhenyu, He Boxue, Cai Qidong, Zhang Pengfei, Peng Xiong, Zhang Yuqian, Xie Hui, Wang Xiang
Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.
Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.
PeerJ. 2020 Sep 24;8:e10008. doi: 10.7717/peerj.10008. eCollection 2020.
The highest rate of cancer-related deaths worldwide is from lung adenocarcinoma (LUAD) annually. Metabolism was associated with tumorigenesis and cancer development. Metabolic-related genes may be important biomarkers and metabolic therapeutic targets for LUAD.
In this study, the gleaned cohort included LUAD RNA-SEQ data from the Cancer Genome Atlas (TCGA) and corresponding clinical data ( = 445). The training cohort was utilized to model construction, and data from the Gene Expression Omnibus (GEO, GSE30219 cohort, = 83; GEO, GSE72094, = 393) were regarded as a testing cohort and utilized for validation. First, we used a lasso-penalized Cox regression analysis to build a new metabolic-related signature for predicting the prognosis of LUAD patients. Next, we verified the metabolic gene model by survival analysis, C-index, receiver operating characteristic (ROC) analysis. Univariate and multivariate Cox regression analyses were utilized to verify the gene signature as an independent prognostic factor. Finally, we constructed a nomogram and performed gene set enrichment analysis to facilitate subsequent clinical applications and molecular mechanism analysis.
Patients with higher risk scores showed significantly associated with poorer survival. We also verified the signature can work as an independent prognostic factor for LUAD survival. The nomogram showed better clinical application performance for LUAD patient prognostic prediction. Finally, KEGG and GO pathways enrichment analyses suggested several especially enriched pathways, which may be helpful for us investigative the underlying mechanisms.
全球每年与癌症相关的死亡中，肺癌腺癌（LUAD）的死亡率最高。代谢与肿瘤发生和癌症发展相关。代谢相关基因可能是LUAD重要的生物标志物和代谢治疗靶点。
在本研究中，收集的队列包括来自癌症基因组图谱（TCGA）的LUAD RNA-SEQ数据及相应临床数据（n = 445）。训练队列用于模型构建，来自基因表达综合数据库（GEO，GSE30219队列，n = 83；GEO，GSE72094，n = 393）的数据作为测试队列用于验证。首先，我们使用套索惩罚Cox回归分析构建一个新的代谢相关特征，用于预测LUAD患者的预后。接下来，我们通过生存分析、C指数、受试者工作特征（ROC）分析验证代谢基因模型。单因素和多因素Cox回归分析用于验证基因特征作为独立预后因素。最后，我们构建了列线图并进行基因集富集分析，以促进后续临床应用和分子机制分析。
风险评分较高的患者生存情况明显较差。我们还验证了该特征可作为LUAD生存的独立预后因素。列线图在LUAD患者预后预测中显示出更好的临床应用性能。最后，KEGG和GO通路富集分析表明了几个特别富集的通路，这可能有助于我们研究潜在机制。
