Investigation into the molecular mechanism of obesity: an integrated approach of multi-omics analysis, machine...

Obesity has emerged as a major global public health challenge and poses a significant threat to human health. Despite extensive research, the mechanisms underlying its pathological progression remain elusive.

To systematically identify pivotal targets and underlying mechanisms affecting the pathological progression of obesity through integrated strategies.

Transcriptomic and single-cell RNA sequencing (scRNA-seq) datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify obesity-associated genes. Moreover, functional enrichment analysis was conducted to elucidate potential mechanisms, and immune cell infiltration was assessed using the CIBERSORT algorithm. Then, macrophage-related genes were screened and subjected to degree centrality assessment, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and random forest to identify hub genes. Furthermore, scRNA-seq was employed to systematically characterize key cell types and their gene expression profiles in the context of obesity. Finally, immunofluorescence (IF) and ELISA techniques were used to validate the expression of specific genes in the adipose tissue of obese mice.

A total of 535 differentially expressed genes (DEGs) were identified, highlighting their significant role in the modulation of immune responses and inflammation. WGCNA was conducted to identify gene modules strongly correlated with obesity, and integration with differential expression analysis yielded 425 co-expressed DEGs. Pathway enrichment and immune cell infiltration analyses revealed that these genes were closely associated with the expression of macrophages. A total of 81 macrophage-related genes were further screened, and through protein-protein interaction (PPI) analysis combined with two machine learning algorithms, two hub genes (TREM2 and CXCR4) were ultimately identified. The Human Protein Atlas database and single-cell transcriptome analyses validated that TREM2 is specifically expressed in macrophages. Lastly, animal experiments verified the expression pattern of TREM2 in the adipose tissue of obese mouse models.

This study identified TREM2 as a key effector in the regulation of obesity-related pathophysiological processes, with specific expression in macrophages. These findings collectively position TREM2 as a potential diagnostic biomarker for obesity.