An Integrative Bioinformatics Meta-Analysis of Microarray Data For Identifying Hub Genes As Biomarkers Of Autism Spectrum Disorder (ASD)

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder primarily affecting young children. ASD is a complex disease involving genetic and environmental factors. Environmental risk factors identified include gestational exposure to pollution, pesticides, maternal infections, and inflammation. Genetic mutations account for about 10 – 20% of ASD cases. Based on the Centre for Disease Control (CDC) in the United States, 1 in 68 children are affected with ASD. Recent advancements in genetic technologies have enabled the detection of biomarkers for the early detection of diseases and risk identification. Aim: This meta-analysis aims to determine the gene signatures involved in ASD.  We conducted a meta-analysis to identify the differentially expressed genes (DEGs) in ASD microarray datasets comprising 122 ASD and 89 control peripheral blood mononuclear cell (PBMC) and whole blood samples from two microarray studies. We performed gene ontology, pathway enrichment, and protein-protein interaction (PPI) network analysis to identify associations between autism and altered gene expression levels.  At a false discovery rate (FDR) < 0.05, we identified 1862 DEGs; 1056 genes were upregulated, and 806 genes were downregulated. DEGs revealed that dysregulated genes were significantly enriched in the “Primary immunodeficiency pathway”, “Influenzae A”, “Epstein-Barr virus infection pathway”, and other signalling pathways from the analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Subsequently, protein-protein interactions (PPI) analysis identified SUMO1, SP1, EGR1, EP300, and VHL as hub genes to be the potential biomarkers for ASD. In total, eighteen differentially expressed hub genes could potentially be used as potential biomarkers for the diagnosis of ASD.