肺癌是人类最常见的癌症之一，肺腺癌（LUAD）已成为肺癌最常见的组织学类型。免疫逃逸促进 LUAD 从早期进展到转移性晚期，是改善针对免疫检测点的免疫治疗临床结果的主要障碍之一。我们的研究旨在探讨肺腺癌中异常表达的免疫逃逸相关基因，为预测肺腺癌的预后和靶向提供帮助。LUAD患者的RNA数据和相关临床细节均来自癌症基因组图谱（TCGA）数据库。通过加权基因共表达网络分析（WGCNA），筛选出3112个关键基因，并与前期研究中获得的182个免疫逃逸基因进行交叉，鉴定出免疫逃逸相关基因（IERG）。通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析，系统地探讨了IERGs在LUAD中的作用，并用于丰富IERGs的相关通路。采用最小绝对收缩和选择算子（LASSO）算法和多元Cox回归分析来识别关键预后基因，并构建预后风险模型。使用表达数据估计恶性肿瘤组织中的基质细胞和免疫细胞（ESTIMATE）和微环境细胞群（MCP）计数器方法（可以准确评估八个免疫细胞群和两个基质细胞群的数量）来分析肿瘤免疫高风险亚组和低风险亚组的状态。利用人类蛋白质图谱（HPA）数据库确定肺癌样本中差异表达基因的蛋白质表达水平。构建列线图，并通过基因表达综合（GEO）数据集GSE72094和GSE30219验证预后风险模型。获得20个差异表达的IERG。对这20个IERGs的GO分析表明，它们主要与免疫系统过程、免疫反应的调节以及介导信号通路和凋亡信号通路中的干扰素-γ富集有关；同时，KEGG分析显示，IERGs与坏死性凋亡、抗原加工和呈递、程序性细胞死亡配体1（PD-L1）表达和肿瘤中程序性细胞死亡1（PD-1）通路、细胞因子-细胞因子受体相互作用、T辅助细胞相关。细胞 1 (Th1) 和 Th2 分化以及肿瘤坏死因子信号通路。利用LASSO和Cox回归分析，我们构建了一个可以预测LUAD患者预后的四基因模型，并通过验证队列对该模型进行了验证。 HPA数据库免疫组化结果显示，AHSA1和CEP55在正常肺组织中低表达，而在肺癌组织中高表达。我们构建了基于IERG的LUAD预后预测模型。在已确定的基因中，CEP55 和 AHSA1 可能是潜在的预后和治疗靶标，降低其表达可能代表 LUAD.2024 转化癌症研究治疗的一种新方法。版权所有。

Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted.RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219.Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue.We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.2024 Translational Cancer Research. All rights reserved.