Coronary artery disease is the leading cause of mortality and morbidity worldwide and shows significant biomarker diversity. Despite considerable progress in microarray and proteomics technologies, and therapeutic strategies, the genetic basis of this disease remain poorly understood. High-throughput experiments such as genome-wide association studies and microarray gene expression profiling offer valuable information to uncover potential biomarkers associated with biochemical pathways and underlying mechanisms. Meta-analysis and gene enrichment analysis are known to provide powerful tool to discover novel biomarkers and their associated pathways using available genomic data on differentially expressed genes, measured under diverse conditions. Although meta-analysis alone has shown to predict biomarker genes of cardiovascular diseases, its efficacy in linking differentially expressed genes to coronary artery disease pathways along with other functional enrichment tools is unclear. Specifically, identifying differentially expressed genes using meta-analysis is often complicated by several factors such as the differences in microarray technique, study focus, data quality, gene nomenclature, species, intervention, and the statistical models used for analysis. Therefore, in this work, we developed a combined meta-analysis of multiple microarray data with quality control analysis, gene ontology and pathways functional enrichment analysis to identify potential differentially expressed genes of coronary artery disease and their cellular pathways. Gene expression data from four different studies were analyzed individually and using meta-analysis of combined data, which results in five differentially expressed genes that are thought to involve in coronary artery disease. This framework demonstrated the significance of microarray data heterogeneity and selection of statistical measures in identifying potential biomarkers associated with coronary artery disease, and can help elucidating the molecular mechanisms underlying this disease.

Microarray Data; Differentially Expressed Genes; Gene Enrichment; Bioinformatics Analysis; Metaanalysis; Coronary Artery Disease