DataSheet2_Development of a ferroptosis-based model to predict prognosis, tumor microenvironment, and drug response for lung adenocarcinoma with weighted genes co-expression network analysis.CSV

Objective: The goal of this study was to create a risk model based on the ferroptosis gene set that affects lung adenocarcinoma (LUAD) patients’ prognosis and to investigate the potential underlying mechanisms.
Material and Methods: A cohort of 482 LUAD patients from the TCGA database was used to develop the prognostic model. We picked the module genes from the ferroptosis gene set using weighted genes co-expression network analysis (WGCNA). The least absolute shrinkage and selection operator (LASSO) and univariate cox regression were used to screen the hub genes. Finally, the multivariate Cox analysis constructed a risk prediction score model. Three other cohorts of LUAD patients from the GEO database were included to validate the prediction ability of our model. Furthermore, the differentially expressed genes (DEG), immune infiltration, and drug sensitivity were analyzed.
Results: An eight-gene-based prognostic model, including PIR, PEBP1, PPP1R13L, CA9, GLS2, DECR1, OTUB1, and YWHAE, was built. The patients from the TCGA database were classified into the high-RS and low-RS groups. The high-RS group was characterized by poor overall survival (OS) and less immune infiltration. Based on clinical traits, we separated the patients into various subgroups, and RS had remarkable prediction performance in each subgroup. The RS distribution analysis demonstrated that the RS was significantly associated with the stage of the LUAD patients. According to the study of immune cell infiltration in both groups, patients in the high-RS group had a lower abundance of immune cells, and less infiltration was associated with worse survival. Besides, we discovered that the high-RS group might not respond well to immune checkpoint inhibitors when we analyzed the gene expression of immune checkpoints. However, drug sensitivity analysis suggested that high-RS groups were more sensitive to common LUAD agents such as Afatinib, Erlotinib, Gefitinib, and Osimertinib.
Conclusion: We constructed a novel and reliable ferroptosis-related model for LUAD patients, which was associated with prognosis, immune cell infiltration, and drug sensitivity, aiming to shed new light on the cancer biology and precision medicine.

研究目的：本研究旨在构建一种基于铁死亡基因集的风险模型，用于评估肺腺癌（lung adenocarcinoma, LUAD）患者的预后，并探究其潜在的分子作用机制。
材料与方法：本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中纳入482例肺腺癌患者队列，用于构建预后风险模型。采用加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）从铁死亡基因集中筛选模块基因；通过最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）与单变量Cox回归分析筛选核心基因；最终通过多变量Cox回归分析构建风险预测评分模型。此外，从基因表达综合数据库（Gene Expression Omnibus, GEO）中纳入另外3组肺腺癌患者队列，用于验证本模型的预测性能。同时，本研究还对差异表达基因（differentially expressed genes, DEG）、免疫浸润情况以及药物敏感性进行了分析。
研究结果：本研究构建了一套包含PIR、PEBP1、PPP1R13L、CA9、GLS2、DECR1、OTUB1及YWHAE共8个基因的预后模型。将TCGA队列中的患者划分为高风险评分组（Risk Score, RS）与低风险评分组，高RS组患者的总生存期（overall survival, OS）更差，且免疫浸润程度更低。基于临床特征将患者划分为多个亚组后，风险评分在各亚组中均表现出显著的预测性能。风险评分分布分析显示，风险评分与肺腺癌患者的临床分期显著相关。对两组患者的免疫细胞浸润情况进行分析后发现，高RS组的免疫细胞浸润丰度更低，且较低的免疫浸润与较差的生存期相关。此外，通过分析免疫检查点的基因表达情况，本研究发现高RS组患者对免疫检查点抑制剂的响应效果较差。但药物敏感性分析结果显示，高RS组患者对肺腺癌常用治疗药物阿法替尼（Afatinib）、厄洛替尼（Erlotinib）、吉非替尼（Gefitinib）及奥希替尼（Osimertinib）的敏感性更高。
研究结论：本研究构建了一种全新且可靠的肺腺癌铁死亡相关预后模型，该模型与患者预后、免疫细胞浸润及药物敏感性密切相关，可为癌症生物学研究及精准医学领域提供新的研究视角。