Aged
Aged, 80 and over
Binding Sites
Brain Ischemia/diagnosis/*genetics/metabolism
Case-Control Studies
Computational Biology
Databases, Genetic
Female
Gene Expression Profiling/methods
Gene Expression Regulation
Gene Regulatory Networks
Genome-Wide Association Study
Humans
Inflammation/genetics/metabolism
Male
Middle Aged
Oligonucleotide Array Sequence Analysis
PAX2 Transcription Factor/genetics/metabolism
Stroke/diagnosis/*genetics
*Transcriptome
BACKGROUND: Genome-wide expression studies (GWES), using microarray platforms, have allowed a deeper understanding of the molecular factors involved in the pathophysiology of ischemic stroke (IS), one of the main global causes of mortality and disability. METHODS: In the current work, we carried out a meta-analysis of available GWES for IS. Bioinformatics and computational biology analyses were applied to identify enriched functional categories and convergence with other genomic datasets for IS. RESULTS: Three primary datasets were included and in the meta-analyses for GWES and IS, 41 differentially expressed (DE) genes were identified using a random effects model. Thirteen of these genes were downregulated and 28 were upregulated. An analysis of functional categories found a significant enrichment for the Gene Ontology Term "Inflammatory Response" and for binding sites for the PAX2 transcription factor. CONCLUSIONS: The list of DE genes identified in this meta-analysis of GWES for IS is useful for future genetic and molecular studies, which would allow the identification of novel mechanisms involved in the pathophysiology of IS. Several of the DE genes found in this meta-analysis have known functional roles related to mechanisms involved in the pathophysiology of IS. It is recognized the role of the inflammatory response in the pathophysiology of IS.BACKGROUND: Genome-wide expression studies (GWES), using microarray platforms, have allowed a deeper understanding of the molecular factors involved in the pathophysiology of ischemic stroke (IS), one of the main global causes of mortality and disability. METHODS: In the current work, we carried out a meta-analysis of available GWES for IS. Bioinformatics and computational biology analyses were applied to identify enriched functional categories and convergence with other genomic datasets for IS. RESULTS: Three primary datasets were included and in the meta-analyses for GWES and IS, 41 differentially expressed (DE) genes were identified using a random effects model. Thirteen of these genes were downregulated and 28 were upregulated. An analysis of functional categories found a significant enrichment for the Gene Ontology Term "Inflammatory Response" and for binding sites for the PAX2 transcription factor. CONCLUSIONS: The list of DE genes identified in this meta-analysis of GWES for IS is useful for future genetic and molecular studies, which would allow the identification of novel mechanisms involved in the pathophysiology of IS. Several of the DE genes found in this meta-analysis have known functional roles related to mechanisms involved in the pathophysiology of IS. It is recognized the role of the inflammatory response in the pathophysiology of IS.