铜凋亡是一种新型的程序性细胞死亡，与其他类型如易燃凋亡、铁凋亡和自噬有所不同。它是癌症治疗的一个有希望的新靶点。此外，免疫相关基因在癌症进展和患者预后中起着关键作用。因此，我们的研究旨在基于铜凋亡和免疫相关基因为肺腺癌患者构建一个生存预测模型。该模型可以用于提高个体化治疗水平。我们从癌症基因组图谱（TCGA）和基因表达杂志（GEO）数据库中收集了肺腺癌（LUAD）患者的RNA测序（RNA-seq）数据。通过基因集变异分析（GSVA）确定了GSE68465队列中的免疫细胞浸润水平，并使用加权基因共表达网络分析（WGCNA）鉴定了免疫相关基因（IRGs）。此外，使用无监督聚类鉴定了与铜凋亡相关的基因（CRGs）。进行单变量COX回归分析和最小绝对收缩选择操作（LASSO）回归分析，开发了铜凋亡和免疫相关基因（CIRGs）的风险预测模型，并进行了验证。使用各种算法探索了风险评分与免疫浸润水平之间的关系，并基于单细胞测序对模型基因进行了分析。最后，通过定量实时荧光PCR（qRT-PCR）、免疫组化（IHC）和免疫印迹（WB）确认了特征基因的表达。我们鉴定了5个致癌驱动基因，分别为CD79B、PEBP1、PTK2B、STXBP1和ZNF671，并开发了比例风险回归模型。研究结果表明，在高风险组中，训练集和验证集的生存率显著降低。此外，与低风险组相比，高风险组显示较低的免疫细胞浸润水平和免疫检查点的表达。版权所有 © 2023张、曲、马、李、魏、王、曾、聂、张、尹、周和杨。

Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients.RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB).We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.Copyright © 2023 Zhang, Qu, Ma, Li, Wei, Wang, Zeng, Nie, Zhang, Yin, Zhou and Yang.