A Multi-Dataset Meta-Analysis Identifies Core mRNA and lncRNA Networks Associated with Autism Spectrum Disorder

Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder accompanied by multifaceted genetic and molecular dysregulation. There is a need to identify reproducible gene expression patterns and regulatory networks by integrative meta-analysis methods. Methods Six autonomous ASD-related transcriptomic datasets were obtained from the Gene Expression Omnibus and consolidated analytically. Quality control, log2-transformation, and outlier removal were done before differential expression analysis between each dataset through the limma framework. Robust Rank Aggregation (RRA) was used to identify mRNAs and lncRNAs with consistent dysregulation. DAVID database was used for functional enrichment analysis. STRING was used to build protein-protein interaction (PPI) networks and MCODE and CytoHubba were used to identify hub genes. To create reproducible lncRNA-mRNA regulation networks among datasets, Pearson correlation was conducted. Results ASD-associated mRNAs and lncRNAs with consistent directionality across datasets were identified. Upregulated mRNAs were primarily involved in transcriptional regulation, chromatin remodeling, RNA processing, and intracellular signaling whereas downregulated mRNAs were enriched in synaptic signaling, neuronal communication, and ion-transport pathways. Densely interconnected modules enriched for translation initiation and RNA-binding functions, with EIF family members and heterogeneous nuclear ribonucleoproteins emerging as key hub genes. A reproducible regulatory network involving five lncRNAs and nineteen hub mRNAs was identified, with MALAT1 acting as a central regulatory hub. Conclusions This integrative transcriptomics analysis shows that coordinated transcriptional regulation and synaptic signaling dysregulation occur in ASD and identifies key hub genes and lncRNA-mediated regulatory networks. The results may bring new insights into the ASD molecular pathobiology and possible peripheral biomarkers and treatment targets.