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BackgroundThe exact mechanisms driving colorectal cancer (CRC) are yet to be fully elucidated. This study aims to confirm the reliability of a prognostic model for colon adenocarcinoma (COAD) by analyzing the varied expression levels of Glycolysis & Pyroptosis-Related Differentially Expressed Genes (G&PRDEGs) in COAD using bioinformatics tools.MethodsWe retrieved gene expression data and clinical details for COAD patients from the Cancer Genome Atlas (TCGA) database. These data were analyzed to categorize the samples into pyroptosis-positive and pyroptosis-negative groups based on their expression of G&PRDEGs. A prognostic model for COAD was then developed using LASSO Cox regression analysis, focusing on these differentially expressed genes (DEGs). Kaplan-Meier curves were plotted to assess the differences in survival between the two groups. Furthermore, we conducted multivariate Cox regression analyses to evaluate the influence of clinical parameters and model-derived risk scores. Analyses of pathway enrichment were performed using R software, alongside single-sample gene-set enrichment analysis (ssGSEA) to explore the role of immune cells and functions associated with G&PRDEGs.ResultsA predictive model was developed using 53 G&PRDEGs that were expressed differentially. An examination of survival rates revealed that the high-risk groups exhibited a noticeably diminished overall survival (OS) in comparison to the low-risk groups in the TCGA database (P P ConclusionThe COAD prognosis model, developed using G&PRDEGs, exhibits predictive capability for the prognosis of COAD sufferers and offers utility in prognostic analysis for COAD sufferers.背景 结直肠癌（colorectal cancer, CRC）的确切致病机制尚未完全阐明。本研究拟通过生物信息学手段，分析结肠腺癌（colon adenocarcinoma, COAD）样本中糖酵解与焦亡相关差异表达基因（Glycolysis & Pyroptosis-Related Differentially Expressed Genes, G&PRDEGs）的表达差异，以此验证一款结肠腺癌预后模型的可靠性。方法 我们从癌症基因组图谱（Cancer Genome Atlas, TCGA）数据库中检索获取结肠腺癌患者的基因表达数据与临床信息。基于G&PRDEGs的表达水平，将样本划分为焦亡阳性组与焦亡阴性组。随后以该类差异表达基因为研究对象，采用LASSO Cox回归分析构建结肠腺癌预后预测模型。绘制Kaplan-Meier曲线以评估两组患者的生存差异。此外，我们开展多因素Cox回归分析，以评估临床参数与模型风险评分的影响。通过R软件进行通路富集分析，并采用单样本基因集富集分析（single-sample gene-set enrichment analysis, ssGSEA）探究G&PRDEGs相关的免疫细胞与免疫功能的调控作用。结果 本研究利用53个差异表达的G&PRDEGs构建了预后预测模型。生存率分析结果显示，在TCGA数据库中，高风险组患者的总生存期（overall survival, OS）较低风险组显著缩短（P P）。结论 基于G&PRDEGs构建的结肠腺癌预后模型，对结肠腺癌患者的预后具有良好的预测价值，可用于该类患者的预后评估与分析。