Identification and Validation of Biomarkers in Metabolic Dysfunction-Associated Steatohepatitis Using Machine Learning and Bioinformatics

被引：0
作者：
Zhang, Yu-Ying [1 ]
Li, Jin-E [1 ]
Zeng, Hai-Xia [1 ]
Liu, Shuang [1 ]
Luo, Yun-Fei [1 ]
Yu, Peng [1 ,2 ,3 ]
Liu, Jian-Ping [1 ,2 ,3 ]
机构：
[1] Nanchang Univ, Affiliated Hosp 2, Jiangxi Med Coll, Dept Endocrinol & Metab, Nanchang, Jiangxi, Peoples R China
[2] Inst Study Endocrinol & Metab Jiangxi Prov, Nanchang, Jiangxi, Peoples R China
[3] Branch Natl Clin Res Ctr Metab Dis, Nanchang, Jiangxi, Peoples R China
来源：
MOLECULAR GENETICS & GENOMIC MEDICINE | 2025年 / 13卷 / 02期
基金：
中国国家自然科学基金;
关键词：
biomarkers; immune cell infiltration; machine learning algorithms; malic enzyme 1; metabolic dysfunction-associated steatohepatitis; FATTY LIVER-DISEASE; MALIC ENZYME; PACKAGE; EXPRESSION; NADPH;
D O I：
10.1002/mgg3.70063
中图分类号：
Q3 [遗传学];
学科分类号：
071007 ; 090102 ;
摘要：
Background The incidence of metabolic dysfunction-associated steatohepatitis (MASH) is increasing annually. MASH can progress to cirrhosis and hepatocellular carcinoma. However, the early diagnosis of MASH is challenging. Aim To screen prospective biomarkers for MASH and verify their effectiveness through in vitro and in vivo experiments. Methods Microarray datasets (GSE89632, GSE48452, and GSE63067) from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs) between patients with MASH and healthy controls. Machine learning methods such as support vector machine recursive feature elimination and least absolute shrinkage and selection operator were utilized to identify optimum feature genes (OFGs). OFGs were validated using the GSE66676 dataset. CIBERSORT was utilized to illustrate the variations in immune cell abundance between patients with MASH and healthy controls. The correlation between OFGs and immune cell populations was evaluated. The OFGs were validated at both transcriptional and protein levels. Results Initially, 37 DEGs were identified in patients with MASH compared with healthy controls. In the enrichment analysis, the DEGs were mainly related to inflammatory responses and immune signal-related pathways. Subsequently, using machine learning algorithms, five genes (FMO1, PEG10, TP53I3, ME1, and TRHDE) were identified as OFGs. The candidate biomarkers were validated in the testing dataset and through experiments with animal and cell models. The malic enzyme (ME1) gene (HGNC:6983) expression was significantly upregulated in MASH samples compared to controls (0.4353 +/- 0.2262 vs. -0.06968 +/- 0.3222, p = 0.00076). Immune infiltration analysis revealed a negative correlation between ME1 expression and plasma cells (R = -0.77, p = 0.0033). Conclusion This study found that ME1 plays a regulatory role in early MASH, which may affect disease progression by mediating plasma cells and T cells gamma delta to regulate immune microenvironment. This finding provides a new idea for the early diagnosis, monitoring and potential therapeutic intervention of MASH.
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页数：13
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