Meta-Analysis of RNA-Seq Data Identifies Differentially Expressed Genes in Skeletal Muscle Between Obese and Normal Weight Individuals

Obesity disrupts skeletal muscle metabolism through insulin resistance, oxidative stress, and ectopic fat deposition, yet transcriptomic findings across individual studies remain inconsistent. We performed a meta-analysis of four independent RNA sequencing (RNA-seq) studies of human vastus lateralis muscle, comparing 29 individuals with obesity (body mass index (BMI) ≥ 30 kg/m²) and 23 with normal weight. Differential expression was analyzed using DESeq2, with age and sex included as covariates in studies where individual-level data were available. Study-level results were integrated using the direction-aware inverse normal method (weighted Stouffer). Between-study heterogeneity was assessed by gene-level I² statistics derived from random-effects meta-analysis of log₂ fold changes. Functional annotation was performed with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The weighted Stouffer method identified 2136 differentially expressed genes (DEGs) (adjusted <i>p</i> < 0.05), comprising 1028 upregulated and 1108 downregulated genes, of which 674 (31.6%) were detected only through the meta-analysis. Three genes-<i>PHLDA3</i> (down), <i>CNKSR2</i> (down), and <i>SFRP4</i> (up)-were significant in every individual study and in the combined analysis. Downregulated DEGs were enriched in cytoplasmic translation, ribosomal structure, and oxidative phosphorylation, whereas upregulated DEGs were associated with extracellular matrix organization and the focal adhesion pathway. This RNA-seq meta-analysis of skeletal muscle in obesity identifies robust DEGs and dysregulated pathways, providing candidate targets for future mechanistic and translational research.