DataSheet_1_Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma.pdf

BackgroundCuproptosis 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.
MethodsRNA 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).
ResultsWe 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.

研究背景：铜死亡（Cuproptosis）是一种区别于焦亡（pyroptosis）、铁死亡（ferroptosis）与自噬等其他类型的新型程序性细胞死亡形式，是癌症治疗领域极具潜力的全新靶点。此外，免疫相关基因在癌症进展与患者预后中发挥关键作用。因此本研究旨在基于铜死亡与免疫相关基因，构建肺腺癌患者的生存预测模型，以提升患者的个性化治疗水平。

研究方法：本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）与基因表达综合数据库（Gene Expression Omnibus, GEO）中收集肺腺癌（lung adenocarcinoma, LUAD）患者的RNA测序（RNA sequencing, RNA-seq）数据。采用基因集变异分析（gene set variation analysis, GSVA）确定GSE68465队列中的免疫细胞浸润水平，通过加权基因共表达网络分析（weighted gene coexpression network analysis, WGCNA）鉴定免疫相关基因（immune-related genes, IRGs）。此外，通过无监督聚类鉴定铜死亡相关基因（cuproptosis-related genes, CRGs）。采用单因素COX回归分析与最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归分析，构建铜死亡与免疫相关基因（cuproptosis and immune-related genes, CIRGs）的风险预后模型并进行验证。利用多种算法探究风险评分与免疫浸润水平的关联，并基于单细胞测序对模型基因进行分析。最后通过实时定量聚合酶链式反应（quantitative real-time PCR, qRT-PCR）、免疫组化（immunohistochemistry, IHC）与蛋白质印迹（Western blotting, WB）验证特征基因的表达水平。

研究结果：本研究共鉴定出5个致癌驱动基因，分别为CD79B、PEBP1、PTK2B、STXBP1与ZNF671，并构建了比例风险回归模型。研究结果显示，训练集与验证集中的高风险组患者生存率均显著降低。此外，与低风险组相比，高风险组的免疫细胞浸润水平与免疫检查点表达水平均更低。