Identification of Key Biomarkers Related to Lipid Metabolism in Acute Pancreatitis and Their Regulatory Mechanisms Based on Bioinformatics and Machine Learning.

Background

Acute pancreatitis (AP) is characterized by the abnormal activation of pancreatic enzymes due to various causes, leading to local pancreatic inflammation. This can trigger systemic inflammatory response syndrome and multi-organ dysfunction. Hyperlipidemia, mainly resulting from lipid metabolism disorders and elevated triglyceride levels, is a major etiological factor in AP. This study aims to investigate the role of lipid metabolism-related genes in the pathogenesis of AP and to propose novel strategies for its prevention and treatment.

Methods

We obtained AP-related datasets GSE3644, GSE65146, and GSE121038 from the GEO database. Differentially expressed genes (DEGs) were identified using DEG analysis and gene set enrichment analysis (GSEA). To identify core lipid metabolism genes in AP, we performed least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis. Gene and protein interactions were predicted using GeneMANIA and AlphaFold. Finally, biomarker expression levels were quantified using Real-Time quantitative Polymerase Chain Reaction (RT-qPCR) in an AP mouse model.

Results

Seven lipid metabolism-related genes were identified as key biomarkers in AP: Amacr, Cyp39a1, Echs1, Gpd2, Osbpl9, Acsl4, and Mcee. The biological roles of these genes mainly involve fatty acid metabolism, cholesterol metabolism, lipid transport across cellular membranes, and mitochondrial function.

Conclusions

Amacr, Cyp39a1, Echs1, Gpd2, Osbpl9, Acsl4, and Mcee are characteristic biomarkers of lipid metabolism abnormalities in AP. These findings are crucial for a deeper understanding of lipid metabolism pathways in AP and for the early implementation of preventive clinical measures, such as the control of blood lipid levels.