A Metabolism-Related Gene Prognostic Index for Prediction of Response to Immunotherapy in Lung Adenocarcinoma

免疫疗法 基因 免疫系统 生物 癌症研究 腺癌 肺癌 生存分析 计算生物学 肿瘤科 医学 癌症 免疫学 内科学 遗传学
作者
Bo Tang,Lanlin Hu,Tao Jiang,Yunchang Li,Huasheng Xu,Hang Zhou,Lan Mei,Ke Xu,Jun Yin,Chunxia Su,Caicun Zhou,Chuan Xu
出处
期刊:International Journal of Molecular Sciences [Multidisciplinary Digital Publishing Institute]
卷期号:23 (20): 12143-12143 被引量:6
标识
DOI:10.3390/ijms232012143
摘要

Immunotherapy, such as immune checkpoint inhibitors (ICIs), is a validated strategy for treating lung adenocarcinoma (LUAD) patients. One of the main challenges in ICIs treatment is the lack of efficient biomarkers for predicting response or resistance. Metabolic reprogramming has been proven to remodel the tumor microenvironment, altering the response to ICIs. We constructed a prognostic model as metabolism-related gene (MRG) of four genes by using weighted gene co-expression network analysis (WGCNA), the nonnegative matrix factorization (NMF), and Cox regression analysis of a LUAD dataset (n = 500) from The Cancer Genome Atlas (TCGA), which was validated with three Gene Expression Omnibus (GEO) datasets (n = 442, n = 226 and n = 127). The MRG was constructed based on BIRC5, PLK1, CDKN3, and CYP4B1 genes. MRG-high patients had a worse survival probability than MRG-low patients. Furthermore, the MRG-high subgroup was more associated with cell cycle-related pathways; high infiltration of activated memory CD4+T cells, M0 macrophages, and neutrophils; and showed better response to ICIs. Contrarily, the MRG-low subgroup was associated with fatty acid metabolism, high infiltration of dendric cells, and resting mast cells, and showed poor response to ICIs. MRG is a promising prognostic index for predicting survival and response to ICIs and other therapeutic agents in LUAD, which might provide insights on strategies with ICIs alone or combined with other agents.
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