肺腺癌(LUAD)是一种致死率较高的恶性肿瘤。免疫治疗已成为癌症治疗的突破口，能够提高患者生存率和预后，因此有必要找到新的免疫相关标志物，然而目前肺腺癌的免疫相关标志物研究还不足，因此需要找到新的免疫相关生物标志物来帮助治疗肺腺癌患者。本研究采用生物信息学方法和机器学习方法筛选出可靠的免疫相关标志物，构建预后模型，预测肺腺癌患者的总体生存率(OS)，从而促进免疫治疗在肺腺癌中的临床应用。实验数据来自癌症基因组图谱(TCGA)数据库，包括535个肺腺癌样本和59个正常对照样本。首先，采用生物信息学方法和支持向量机递归特征消除算法筛选出中心基因，然后利用多因素Cox回归分析构建肺腺癌免疫预后模型和预测OS率的标尺图。最后，利用ceRNA分析Hub基因在肺腺癌中的调节机制。ADM2、CDH17、DKK1、PTX3和AC145343.1这五个基因被筛选为潜在的免疫相关基因，其中ADM2和AC145343.1在肺腺癌患者中预后良好(HR<1)且是新标志物，而其余三个基因与肺腺癌患者预后不良(HR>1)有关。此外，实验结果表明，低风险组的患者OS率比高风险组更好(P<0.001)。本文提出了一种免疫预后模型，预测肺腺癌患者的OS率，并展示了五个免疫基因与免疫相关细胞浸润水平之间的相关性。它为肺腺癌患者的免疫治疗提供了新的标志物和额外的思路。©2023年，作者(们)独家许可Springer-Verlag GmbH Germany，它属于Springer Nature的一部分。

Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients.In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA.Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001).In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.