Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at late stages, limiting treatment options and survival rates. Pyroptosis-related gene signatures hold promise as PDAC prognostic markers, but limited gene pools and small sample sizes hinder their utility. We aimed to enhance PDAC prognosis with a comprehensive multi-algorithm analysis. Using R, we employed natural language processing and latent Dirichlet allocation on PubMed publications to identify pyroptosis-related genes. We collected PDAC transcriptome data (n = 1273) from various databases, conducted a meta-analysis, and performed differential gene expression analysis on tumour and non-cancerous tissues. Cox and LASSO algorithms were used for survival modelling, resulting in a pyroptosis-related gene expression-based prognostic index. Laboratory and external validations were conducted. Bibliometric analysis revealed that pyroptosis publications focus on signalling pathways, disease correlation, and prognosis. We identified 357 pyroptosis-related genes, validating the significance of BHLHE40, IL18, BIRC3, and APOL1. Elevated expression of these genes strongly correlated with poor PDAC prognosis and guided treatment strategies. Our accessible nomogram model aids in PDAC prognosis and treatment decisions. We established an improved gene signature for pyroptosis-related genes, offering a novel model and nomogram for enhanced PDAC prognosis.
胰腺导管腺癌（PDAC）通常在晚期才被诊断，这限制了治疗选择并降低了生存率。焦亡相关基因特征有望成为PDAC的预后标志物，但有限的基因库和小样本量限制了其应用。本研究旨在通过综合多算法分析提升PDAC的预后评估能力。我们使用R语言，对PubMed文献进行自然语言处理和潜在狄利克雷分配分析，以识别焦亡相关基因。收集来自多个数据库的PDAC转录组数据（n = 1273），进行荟萃分析，并对肿瘤组织与非癌组织进行差异基因表达分析。采用Cox和LASSO算法构建生存模型，最终建立基于焦亡相关基因表达的预后指数。研究进行了实验室和外部验证。文献计量分析显示，焦亡相关研究主要聚焦于信号通路、疾病关联及预后。我们识别出357个焦亡相关基因，并验证了BHLHE40、IL18、BIRC3和APOL1基因的重要性。这些基因的高表达与PDAC不良预后密切相关，并为治疗策略提供了指导。我们构建的便捷列线图模型有助于PDAC的预后评估和治疗决策。本研究建立了改进的焦亡相关基因特征，为提升PDAC预后提供了新的模型和列线图工具。
Multi-Algorithm Analysis Reveals Pyroptosis-Linked Genes as Pancreatic Cancer Biomarkers
肿瘤(癌症)患者之家