Department of Gastroenterology, Renmin Hospital of Wuhan University, No 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China.
Sci Rep. 2024 Jul 10;14(1):15949. doi: 10.1038/s41598-024-66574-0.
Metabolic-associated steatohepatitis (MASH) and ulcerative colitis (UC) exhibit a complex interconnection with immune dysfunction, dysbiosis of the gut microbiota, and activation of inflammatory pathways. This study aims to identify and validate critical butyrate metabolism-related shared genes between both UC and MASH. Clinical information and gene expression profiles were sourced from the Gene Expression Omnibus (GEO) database. Shared butyrate metabolism-related differentially expressed genes (sBM-DEGs) between UC and MASH were identified via various bioinformatics methods. Functional enrichment analysis was performed, and UC patients were categorized into subtypes using the consensus clustering algorithm based on sBM-DEGs. Key genes within sBM-DEGs were screened through Random Forest, Support Vector Machines-Recursive Feature Elimination, and Light Gradient Boosting. The diagnostic efficacy of these genes was evaluated using receiver operating characteristic (ROC) analysis on independent datasets. Additionally, the expression levels of characteristic genes were validated across multiple independent datasets and human specimens. Forty-nine shared DEGs between UC and MASH were identified, with enrichment analysis highlighting significant involvement in immune, inflammatory, and metabolic pathways. The intersection of butyrate metabolism-related genes with these DEGs produced 10 sBM-DEGs. These genes facilitated the identification of molecular subtypes of UC patients using an unsupervised clustering approach. ANXA5, CD44, and SLC16A1 were pinpointed as hub genes through machine learning algorithms and feature importance rankings. ROC analysis confirmed their diagnostic efficacy in UC and MASH across various datasets. Additionally, the expression levels of these three hub genes showed significant correlations with immune cells. These findings were validated across independent datasets and human specimens, corroborating the bioinformatics analysis results. Integrated bioinformatics identified three significant biomarkers, ANXA5, CD44, and SLC16A1, as DEGs linked to butyrate metabolism. These findings offer new insights into the role of butyrate metabolism in the pathogenesis of UC and MASH, suggesting its potential as a valuable diagnostic biomarker.
代谢相关性脂肪性肝炎（MASH）和溃疡性结肠炎（UC）与免疫功能障碍、肠道微生物群的失调以及炎症途径的激活之间存在复杂的相互关系。本研究旨在鉴定和验证 UC 和 MASH 之间关键的丁酸代谢相关共享基因。临床信息和基因表达谱来自基因表达综合数据库（GEO）。通过各种生物信息学方法，确定 UC 和 MASH 之间丁酸代谢相关差异表达基因（sBM-DEGs）。基于 sBM-DEGs 使用共识聚类算法对 UC 患者进行分类。通过随机森林、支持向量机-递归特征消除和轻梯度提升筛选 sBM-DEGs 中的关键基因。使用独立数据集的接收者操作特征（ROC）分析评估这些基因的诊断效能。此外，还在多个独立数据集和人类标本中验证了特征基因的表达水平。确定了 49 个 UC 和 MASH 之间的共享 DEG，富集分析突出了它们在免疫、炎症和代谢途径中的显著参与。丁酸代谢相关基因与这些 DEG 的交集产生了 10 个 sBM-DEGs。这些基因通过无监督聚类方法有助于识别 UC 患者的分子亚型。通过机器学习算法和特征重要性排名，确定 ANXA5、CD44 和 SLC16A1 为枢纽基因。ROC 分析在不同数据集上验证了它们在 UC 和 MASH 中的诊断效能。此外，这三个枢纽基因的表达水平与免疫细胞呈显著相关。这些发现通过独立数据集和人类标本得到验证，证实了生物信息学分析结果。综合生物信息学鉴定了三个重要的生物标志物，即 ANXA5、CD44 和 SLC16A1，它们是与丁酸代谢相关的 DEG。这些发现为丁酸代谢在 UC 和 MASH 发病机制中的作用提供了新的见解，表明其作为有价值的诊断生物标志物的潜力。
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