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A model of twenty-three metabolic-related genes predicting overall survival for lung adenocarcinoma
PeerJ ( IF 2.7 ) Pub Date : 2020-09-24 , DOI: 10.7717/peerj.10008
Zhenyu Zhao 1, 2 , Boxue He 1, 2 , Qidong Cai 1, 2 , Pengfei Zhang 1, 2 , Xiong Peng 1, 2 , Yuqian Zhang 1, 2 , Hui Xie 1, 2 , Xiang Wang 1, 2
Affiliation  

Background 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. Materials and Methods In this study, the gleaned cohort included LUAD RNA-SEQ data from the Cancer Genome Atlas (TCGA) and corresponding clinical data (n = 445). The training cohort was utilized to model construction, and data from the Gene Expression Omnibus (GEO, GSE30219 cohort, n = 83; GEO, GSE72094, n = 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. Result 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.

中文翻译:

预测肺腺癌总生存期的 23 个代谢相关基因模型

背景 全世界每年与癌症相关的死亡率最高的是肺腺癌 (LUAD)。代谢与肿瘤发生和癌症发展有关。代谢相关基因可能是 LUAD 的重要生物标志物和代谢治疗靶点。材料和方法 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 数据和相应的临床数据 (n = 445)。训练队列用于模型构建,来自基因表达综合体(GEO,GSE30219 队列,n = 83;GEO,GSE72094,n = 393)的数据被视为测试队列并用于验证。首先,我们使用 lasso-penalized Cox 回归分析来构建新的代谢相关特征来预测 LUAD 患者的预后。下一个,我们通过生存分析、C指数、接受者操作特征(ROC)分析验证了代谢基因模型。单变量和多变量 Cox 回归分析用于验证基因特征是否为独立的预后因素。最后,我们构建了列线图并进行了基因集富集分析,以方便后续的临床应用和分子机制分析。结果 风险评分较高的患者与较差的生存率显着相关。我们还验证了签名可以作为 LUAD 生存的独立预后因素。列线图显示了更好的 LUAD 患者预后预测临床应用性能。最后,KEGG 和 GO 通路富集分析提出了几个特别丰富的通路,
更新日期:2020-09-24
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