Table3_A Pyroptosis-Related Signature Predicts Overall Survival and Immunotherapy Responses in Lung Adenocarcinoma.XLSX

Background: Lung adenocarcinoma (LUAD) is a highly malignant cancer with a bleak prognosis. Pyroptosis is crucial in LUAD. The present study investigated the prognostic value of a pyroptosis-related signature in LUAD.
Methods: LUAD’s genomic data were downloaded from TCGA and GEO databases. K-means clustering was used to classify the data based on pyroptosis-related genes (PRGs). The features of tumor microenvironment were compared between the two subtypes. Differentially expressed genes (DEGs) were identified between the two subtypes, and functional enrichment and module analysis were carried out. LASSO Cox regression was used to build a prognostic model. Its prognostic value was assessed.
Results: In LUAD, genetic and transcriptional changes in PRGs were found. A total of 30 PRGs were found to be differentially expressed in LUAD tissues. Based on PRGs, LUAD patients were divided into two subgroups. Subtype 1 has a higher overall survival rate than subtype 2. The tumor microenvironment characteristics of the two subtypes differed significantly. Compared to subtype 1, subtype 2 had strong immunological infiltration. Between the two groups, 719 DEGs were discovered. WGCNA used these DEGs to build a co-expression network. The network modules were analyzed. A prognostic model based on seven genes was developed, including FOSL1, KRT6A, GPR133, TMPRSS2, PRDM16, SFTPB, and SFTA3. The developed model was linked to overall survival and response to immunotherapy in patients with LUAD.
Conclusion: In LUAD, a pyroptosis-related signature was developed to predict overall survival and treatment responses to immunotherapy.

背景：肺腺癌（Lung adenocarcinoma, LUAD）是一类预后凶险的高度恶性肿瘤，焦亡（pyroptosis）在LUAD的病理进程中具有关键调控作用。本研究探讨了焦亡相关基因（pyroptosis-related genes, PRGs）特征在LUAD中的预后价值。
方法：从TCGA与GEO数据库下载LUAD的基因组数据；基于PRGs采用K-means聚类对数据进行亚型分型；比较两种亚型的肿瘤微环境特征；鉴定两种亚型间的差异表达基因（Differentially expressed genes, DEGs），并开展功能富集分析与模块分析；采用LASSO Cox回归构建预后模型，并评估其预后效能。
结果：在LUAD中发现了PRGs的遗传与转录组学改变；共计30个PRGs在LUAD组织中呈差异表达。基于PRGs将LUAD患者划分为两个亚组，1亚型患者的总生存期显著优于2亚型。两种亚型的肿瘤微环境特征存在显著差异：与1亚型相比，2亚型的免疫浸润水平更高。两组间共鉴定出719个DEGs，通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）构建共表达网络并解析其网络模块。最终构建了包含FOSL1、KRT6A、GPR133、TMPRSS2、PRDM16、SFTPB及SFTA3共7个基因的预后模型，该模型与LUAD患者的总生存期及免疫治疗应答情况密切相关。
结论：本研究构建了一种焦亡相关特征标签，可用于预测LUAD患者的总生存期与免疫治疗应答效果。