Department of Medical Oncology, Laboratory of Basic Medical Sciences, Qilu Hospital of Shandong University, Jinan, China.
Department of Respiratory Medicine, Tai'an City Central Hospital, Tai'an, China.
Sci Rep. 2024 Oct 22;14(1):24861. doi: 10.1038/s41598-024-75650-4.
Pyroptosis plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the pyroptosis-related gene (PRG) expression pattern and to identify promising pyroptosis-related biomarkers to improve the prognosis of LUAD. The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the 44 differentially expressed PRGs (DEPRGs). Then, consensus clustering revealed pyroptosis-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic PRGs. After overlapping DEGs and the Lasso regression analysis-based prognostic genes, the predictive risk model was established and validated. Correlation analysis between PRGs and clinicopathological variables was also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels, the risk score, and clinicopathological variables in the predictive risk model. A total of 52 genes from the PubMed were identified as PRGs, and 44 of the 52 genes were pooled as DEPRGs. The most significant GO term was "collagen trimer" (P = 2.46E-13), and KEGG analysis results indicated that 44 DEPRGs were significantly enriched in Salmonella infection (P < 0.001). Then, consensus clustering analysis divided LUAD patients into two clusters, and a total of 79 DEGs were identified according to these cluster subtypes. Subsequently, univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators and we also performed Lasso regression analysis and screened 23 DEGs. After overlapping 23 DEGs and 12 genes, only 4 (KLRG2, MAPK4, C6 and SFRP5) of 12 genes were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model effectively predicted the prognosis (P < 0.001). Combined with the correlation analysis results between the 4 genes and clinicopathological variables, C6 and KLRG2 were screened as prognostic genes. In this study, we constructed a predictive risk model and identified two pyroptosis subtype-related gene expression patterns to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful for accurately characterizing the LUAD and developing personalized treatment.
细胞焦亡在肺腺癌（LUAD）中发挥着重要作用。本研究旨在探索细胞焦亡相关基因（PRG）的表达模式，并确定有前途的细胞焦亡相关生物标志物，以改善 LUAD 的预后。从癌症基因组图谱（TCGA）下载 LUAD 患者的基因表达谱和临床信息，并从基因表达综合数据库中提取验证队列信息。使用 limma 包分析基因表达数据，并使用 R 软件中的 ggplot2 包和 pheatmap 包进行可视化。还对 44 个差异表达的 PRG（DEPRG）进行了功能富集分析。然后，共识聚类揭示了与细胞焦亡相关的肿瘤亚型，并根据这些亚型筛选差异表达基因（DEGs）。接下来，使用单变量 Cox 和多变量 Cox 回归分析来识别独立的预后 PRG。重叠 DEGs 和基于 Lasso 回归分析的预后基因后，建立并验证了预测风险模型。还探索了 PRG 与临床病理变量之间的相关性分析。最后，使用 TIMER 和 TISIDB 数据库进一步探索预测风险模型中免疫细胞浸润水平、风险评分与临床病理变量之间的相关性分析。总共从 PubMed 中确定了 52 个基因作为 PRG，其中 52 个基因中的 44 个被归为 DEPRG。最显著的 GO 术语是“胶原三聚体”（P=2.46E-13），KEGG 分析结果表明，44 个 DEPRG 显著富集于沙门氏菌感染（P<0.001）。然后，共识聚类分析将 LUAD 患者分为两个亚群，根据这些聚类亚型共鉴定出 79 个差异表达基因。随后，使用单变量和多变量 Cox 回归分析鉴定出 12 个可作为独立预后指标的基因，我们还进行了 Lasso 回归分析并筛选出 23 个差异表达基因。重叠 23 个 DEGs 和 12 个基因后，仅从 12 个基因中选择了 4 个（KLRG2、MAPK4、C6 和 SFRP5）用于进一步探索预后模式。生存分析结果表明，该风险模型能够有效预测预后（P<0.001）。结合 4 个基因与临床病理变量之间的相关性分析结果，筛选出 C6 和 KLRG2 作为预后基因。本研究构建了预测风险模型，并鉴定了两种与细胞焦亡相关的亚型基因表达模式，以改善 LUAD 的预后。了解 LUAD 的亚型有助于准确描述 LUAD 并制定个性化治疗方案。
