Identification of candidate biomarkers for NAFLD through bioinformatics analysis and machine learning based on circulating insulin degradation-associated genes

Non-alcoholic fatty liver disease (NAFLD) has become as a metabolic disorder posing a significant threat to public health, with no presently available effective treatment. Circulating insulin degradation constitutes a pivotal process regulating insulin concentration and biological activity in the bloodstream, and its capacity is closely associated with hyperinsulinaemia and hepatic lipid accumulation. Hepatic lipid accumulation represents a key pathophysiological mechanism in NAFLD. Therefore, targeting the circulating insulin degradation pathway may represent a significant therapeutic opportunity for NAFLD. This study employed a multi-omics strategy, incorporating pertinent datasets from the Gene Expression Omnibus (GEO) collection, to investigate the function of circulating insulin degradation in NAFLD. We employed systems biology informatics approaches, including weighted gene co-expression network analysis (WGCNA) and machine learning models, to identify four hub biomarkers: MYO7A, AGTR1, IL1RN, and IGFBP2. We applied Shapley Additive Explanations (SHAP) to interpret the contribution of each gene to the machine learning model. The expression patterns and potential relevance of these hub genes were further assessed in external datasets, cellular models, and animal models. Overall, this hypothesis-generating study identified four candidate genes potentially associated with NAFLD and provided additional insights into the molecular mechanisms underlying disease progression.