Table_2_RETRACTED: Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles.docx

Background: Lung cancer is one of the most common types of cancer, and it has a poor prognosis. It is urgent to identify prognostic biomarkers to guide therapy. Methods: The immune gene expression profiles for patients with lung adenocarcinomas (LUADs) were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The relationships between the expression of 45 immune checkpoint genes (ICGs) and prognosis were analyzed. Additionally, the correlations between the expression of 45 biomarkers and immunotherapy biomarkers, including tumor mutation burden (TMB), mismatch repair defects, neoantigens, and others, were identified. Ultimately, prognostic ICGs were combined to determine immune subgroups, and the prognostic differences between these subgroups were identified in LUAD. Results: A total of 11 and nine ICGs closely related to prognosis were obtained from the GEO and TCGA databases, respectively. CD200R1 expression had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68, and GZMB, indicating that it may cause the disordered expression of adaptive immune resistance pathway genes. Multivariable Cox regression was used to construct a signature composed of four prognostic ICGs (IDO1, CD274, CTLA4, and CD200R1): Risk Score = −0.002*IDO1+0.031*CD274−0.069*CTLA4−0.517*CD200R1. The median Risk Score was used to classify the samples for the high- and low-risk groups. We observed significant differences between groups in the training, testing, and external validation cohorts. Conclusion: Our research provides a method of integrating ICG expression profiles and clinical prognosis information to predict lung cancer prognosis, which will provide a unique reference for gene immunotherapy for LUAD.背景：肺癌是最为常见的癌症类型之一，预后不良，亟需鉴定可指导治疗的预后生物标志物。 方法：从癌症基因组图谱（The Cancer Genome Atlas, TCGA）与基因表达综合数据库（Gene Expression Omnibus, GEO）获取肺腺癌（lung adenocarcinomas, LUAD）患者的免疫基因表达谱。分析45个免疫检查点基因（immune checkpoint genes, ICGs）的表达水平与患者预后的关联；同时鉴定这45个生物标志物的表达与免疫治疗生物标志物（包括肿瘤突变负荷（tumor mutation burden, TMB）、错配修复缺陷、新抗原等）之间的相关性。最终整合预后相关免疫检查点基因以划分免疫亚型，并在肺腺癌队列中鉴定各亚型间的预后差异。 结果：分别从GEO与TCGA数据库中筛选得到11个和9个与预后密切相关的免疫检查点基因。CD200R1的表达水平与肿瘤突变负荷及新抗原呈显著负相关；同时CD200R1与CD8A、CD68及GZMB的表达呈显著正相关，提示其可能介导适应性免疫抵抗通路基因的异常表达。采用多变量Cox回归构建由4个预后相关免疫检查点基因（IDO1、CD274、CTLA4及CD200R1）组成的预后评分模型：风险评分（Risk Score）= −0.002*IDO1 + 0.031*CD274 − 0.069*CTLA4 − 0.517*CD200R1。以风险评分的中位数作为截断值，将样本划分为高风险组与低风险组，在训练集、测试集及外部验证队列中均观察到两组间存在显著预后差异。 结论：本研究建立了一种整合免疫检查点基因表达谱与临床预后信息的肺癌预后预测方法，可为肺腺癌的基因免疫治疗提供独特的参考依据。