Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning.

Journal: Scientific reports

PMID: 40360670

Abstract

BACKGROUND: 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.

Authors

Haoran Xie

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, Hong Kong, Special Administrative Region of China.
Junjun Wang

Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qiuyan Zhao

Department of Nursing, School of Clinical Nursing, Gansu Health Vocational College, Gansu, China.

Keywords

Biomarkers Computational Biology Fatty Liver Gene Expression Profiling Humans Machine Learning Non-alcoholic Fatty Liver Disease Protein Interaction Maps ROC Curve

External Resources

View on PubMed Access via DOI PubMed (40360670)

Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning.

Abstract

Authors

Keywords

External Resources

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