A model of twenty-three metabolic-related genes predicting overall survival for lung adenocarcinoma.

Abstract

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.

• Authors: Zhenyu Zhao, Boxue He, Qidong Cai, Pengfei Zhang, Xiong Peng, Yuqian Zhang, Hui Xie, Xiang Wang
• Journal: PeerJ (PeerJ) Vol. 8 Pg. e10008 ( 2020) ISSN: 2167-8359 [Print] United States
• PMID: 33024640 (Publication Type: Journal Article)
• Copyright: ©2020 Zhao et al.