Xie Haoran, Wang Junjun, Zhao Qiuyan
Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Sci Rep. 2025 May 13;15(1):16596. doi: 10.1038/s41598-025-86397-x.
Metabolic associated steatohepatitis (MASH) represents a severe subtype of metabolic associated fatty liver disease (MASLD), with an increased risk of progression to cirrhosis and hepatocellular carcinoma. The nomenclature shift from nonalcoholic steatohepatitis (NASH)/nonalcoholic fatty liver disease (NAFLD) to MASH/MASLD, underscores the pivotal role of metabolic factors in disease progression. Diagnosis of MASH currently hinges on liver biopsy, a procedure whose invasive nature limits its clinical utility. This study aims to identify and validate metabolism-related genes (MRGs) markers for the non-invasive diagnosis of MASH.
This study extracted multiple datasets from the GEO database to identify metabolism-related differentially expressed genes (MRDEGs). Protein-Protein Interaction (PPI) network and machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), were applied to screen for signature MRDEGs. The diagnostic performance of these MRDEGs was evaluated using the Receiver Operating Characteristic (ROC) curve and further validated using independent external datasets. Additionally, enrichment analysis was performed to uncover key driver pathways in MASH. The infiltration levels of various immune cell types were assessed using single sample Gene Set Enrichment Analysis (ssGSEA). Finally, Spearman correlation analysis confirmed the association between signature genes and immune cells.
We successfully identified seven signature MRDEGs, including CYP7A1, GCK, AKR1B10, HPRT1, GPD1, FADS2, and ENO3, through PPI network analysis and machine learning algorithms. The gene model displayed exceptional diagnostic performance in the training and validation cohorts, as evidenced by the area under ROC curve (AUC) exceeding 0.9. Further enrichment analysis revealed that signature MEDEGs were primarily involved in multiple biological pathways related to glucose and lipid metabolism. Immune infiltration analysis indicated a significant increase in the infiltration levels of activated CD8 T cells, gamma-delta T cells, natural killer cells, and CD56bright NK cells in patients with MASH.
This study successfully identified seven signature MRDEGs as significant diagnostic biomarkers for MASH. The findings not only offer novel strategies for non-invasive diagnosis of MASH but also highlight the substantial role of immune cell infiltration in the progression of MASH.
代谢相关脂肪性肝炎（MASH）是代谢相关脂肪性肝病（MASLD）的一种严重亚型，进展为肝硬化和肝细胞癌的风险增加。从非酒精性脂肪性肝炎（NASH）/非酒精性脂肪肝（NAFLD）到MASH/MASLD的命名转变，强调了代谢因素在疾病进展中的关键作用。目前MASH的诊断依赖于肝活检，该检查的侵入性限制了其临床应用。本研究旨在识别和验证用于MASH非侵入性诊断的代谢相关基因（MRG）标志物。
本研究从GEO数据库中提取多个数据集，以识别代谢相关差异表达基因（MRDEG）。应用蛋白质-蛋白质相互作用（PPI）网络和机器学习算法，包括最小绝对收缩和选择算子（LASSO）回归、支持向量机-递归特征消除（SVM-RFE）和随机森林（RF），来筛选标志性MRDEG。使用受试者工作特征（ROC）曲线评估这些MRDEG的诊断性能，并使用独立的外部数据集进行进一步验证。此外，进行富集分析以揭示MASH中的关键驱动通路。使用单样本基因集富集分析（ssGSEA）评估各种免疫细胞类型的浸润水平。最后，Spearman相关性分析证实了标志性基因与免疫细胞之间的关联。
通过PPI网络分析和机器学习算法，我们成功识别出7个标志性MRDEG，包括CYP7A1、GCK、AKR1B10、HPRT1、GPD1、FADS2和ENO3。基因模型在训练和验证队列中表现出卓越的诊断性能，ROC曲线下面积（AUC）超过0.9证明了这一点。进一步的富集分析表明，标志性MEDEG主要参与多个与葡萄糖和脂质代谢相关的生物学途径。免疫浸润分析表明，MASH患者中活化的CD8 T细胞、γδ T细胞、自然杀伤细胞和CD56bright NK细胞的浸润水平显著增加。
本研究成功识别出7个标志性MRDEG作为MASH的重要诊断生物标志物。这些发现不仅为MASH的非侵入性诊断提供了新策略，还突出了免疫细胞浸润在MASH进展中的重要作用。
