Systems-Level Identification Of Metabolic Dysfunction-Associated Steatotic Liver Disease Targets: A Bioinformatics Approach
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Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD, affects about 30% of adults worldwide and remains without effective pharmacotherapy. Its complex pathogenesis, driven by metabolic dysregulation, oxidative stress, inflammation, and hepatocellular injury, necessitates integrative approaches for target discovery.
This study systematically identified and characterized MASLD-associated genes and pathways through multi-database integration (MalaCards, KEGG, OMIM) and enrichment analysis using DAVID v6.8. After deduplication, 236 unique genes were obtained. Gene Ontology and KEGG analyses revealed strong enrichment in mitochondrial energy metabolism, notably oxidative phosphorylation (p <0.0001) and ATP synthesis-coupled electron transport (p <0.0001). Key molecular functions included NADH dehydrogenase and oxidoreductase activities, while enriched cellular components were the respirasome and mitochondrial inner membrane. The most enriched KEGG pathway was non-alcoholic fatty liver disease (p <0.0001), followed by diabetic cardiomyopathy and oxidative stress-related pathways.
Overall, this integrative bioinformatics study highlights mitochondrial dysfunction and lipid metabolic imbalance as central features of MASLD. The identified 236 genes and their pathways offer promising targets for drug discovery, particularly those modulating mitochondrial and lipid homeostasis networks.
Keywords:
MASLD NAFLD Bioinformatics Pathway Enrichment Systems Biology Mitochondrial Dysfunction Drug Target Identification Computational BiologyFull Article
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Copyright © 2026 by the author(s). This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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How to Cite
Hui Yi, T., Adam, S., Hmidan, W. (2026). Systems-Level Identification Of Metabolic Dysfunction-Associated Steatotic Liver Disease Targets: A Bioinformatics Approach. BiomedicaSphere, 1(1), 29-42. https://doi.org/10.59324/bmsj.2026.1(1).04