A Meta-analysis on Molecular Gene Expression Profiles in Coronary Artery Disease

Coronary artery disease (CAD) is a leading global cause of morbidity and mortality, with complex pathophysiology involving genetic, environmental, and molecular factors. Recent advancements in transcriptomics and genomic technologies have enabled the identification of differentially expressed genes (DEGs), non-coding RNAs, and regulatory networks critical to CAD progression. The aim of this study was to identify consistent DEGs, regulatory networks, and molecular pathways associated with CAD through a systematic review and meta-analysis of global transcriptomic studies from 2015 to 2025. A systematic literature search was conducted in PubMed, Scopus, Web of Science, and Embase (2015–2025) per Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Keywords included “CAD,” “gene expression,” “transcriptomics,” and “microRNA.” Of 641 studies screened, eight met the inclusion criteria for meta-analysis. Selected studies involved RNA sequencing (RNA-seq) or microarray-based transcriptomic comparisons between CAD patients and healthy controls, reporting DEGs, microRNA-messenger RNA interactions, or gene networks. Two independent reviewers performed data extraction and quality assessment using the Newcastle-Ottawa Scale. Risk of bias was evaluated through funnel plots, Egger’s and Begg’s tests, with sensitivity analyses confirming robustness. Eight studies were included in the meta-analysis, with sample sizes ranging from 16 to over 26,000 across diverse global cohorts. Transcriptomic profiling used microarrays, RNA-Seq, and polymerase chain reaction, mainly from peripheral blood. The pooled odds ratio was 1.74 (95% confidence interval: 0.85–3.56; P = 0.13) with high heterogeneity (I 2 = 97%). Key DEGs included interleukin-8, early growth response 1, CXCL1, and miR-182, indicating roles in immune activation. PHACTR1 rs9349379G conferred a 44% increased CAD risk, while polygenic scores explained 22% of disease variance. Quality assessment found 94% of studies high quality. The findings underscore the complex molecular basis of CAD, driven by inflammation, immune dysregulation, and genetic susceptibility. Integrating multi-omics biomarkers offers promise for enhanced risk prediction and personalized therapeutic strategies.