Unveiling the molecular landscape of differentially expressed genes in obesity

Lipid-associated disorders such as obesity are major global health challenges, primarily driven by dysregulated lipid metabolism and associated alterations in gene expression and protein interactions. Understanding these molecular mechanisms is essential for identifying new therapeutic targets. This study investigates the molecular landscape of lipid dysregulation through differential gene expression analysis in hyperlipidemic rat models. By integrating multiple datasets and computational tools, we aimed to identify key proteins involved in obesity pathogenesis, thereby contributing to the development of targeted therapeutic strategies for lipid-associated disorders. A comprehensive search was conducted to identify differentially expressed genes associated with lipid disorders by analyzing metadata from various public databases, leading to the curation of four distinct datasets. Gene Ontology (GO) analysis was performed using the G: Profiler server, and protein-protein interaction (PPI) networks were constructed using Cytoscape. Cluster analysis with MCODE identified densely connected subnetworks, while pathway enrichment analysis using KEGG-KASS explored gene involvement in biological pathways. GO analysis revealed critical pathways involved in lipid metabolism, particularly those related to lipid oxidation and homeostasis. Pathway enrichment analysis identified three pivotal genes-Akt1, Nr1h3, and Il6-with Nr1h3 emerging as a prominent target under treatment conditions. Il6 showed significance in both disease and treatment contexts, suggesting its potential as a therapeutic target. These genes were also linked to obesity, fatty liver disease, and atherosclerosis in rat datasets, with supporting evidence from previously published rodent and human studies.