Identification of Key Pathways and Genes Related to Immunotherapy Resistance of LUAD Based on WGCNA Analysis

基于 WGCNA 分析识别 LUAD 免疫治疗耐药性相关关键通路和基因

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作者:Weina Yu, Fengsen Liu, Qingyang Lei, Peng Wu, Li Yang, Yi Zhang

Abstract

Immunotherapy resistance is a major barrier in the application of immune checkpoint inhibitors (ICI) in lung adenocarcinoma (LUAD) patients. Although recent studies have found several mechanisms and potential genes responsible for immunotherapy resistance, ways to solve this problem are still lacking. Tumor immune dysfunction and exclusion (TIDE) algorithm is a newly developed method to calculate potential regulators and indicators of ICI resistance. In this article, we combined TIDE and weighted gene co-expression network analysis (WGCNA) to screen potential modules and hub genes that are highly associated with immunotherapy resistance using the Cancer Genome Atlas (TCGA) dataset of LUAD patients. We identified 45 gene co-expression modules, and the pink module was most correlated with TIDE score and other immunosuppressive features. After considering the potential factors in immunotherapy resistance, we found that the pink module was also highly related to cancer stemness. Further analysis showed enriched immunosuppressive cells in the extracellular matrix (ECM), immunotherapy resistance indicators, and common cancer-related signaling pathways in the pink module. Seven hub genes in the pink module were shown to be significantly upregulated in tumor tissues compared with normal lung tissue, and were related to poor survival of LUAD patients. Among them, THY1 was the gene most associated with TIDE score, a gene highly related to suppressive immune states, and was shown to be strongly expressed in late-stage patients. Immunohistochemistry (IHC) results demonstrated that THY1 level was higher in the progressive disease (PD) group of LUAD patients receiving a PD-1 monoclonal antibody (mAb) and positively correlated with SOX9. Collectively, we identified that THY1 could be a critical biomarker in predicting ICI efficiency and a potential target for avoiding tumor immunotherapy resistance.

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