Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers

Abstract

Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA–mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.

Biomarker MicroRNAs for Diagnosis of Oral Squamous Cell Carcinoma Identified Based on Gene Expression Data and MicroRNA-mRNA Network Analysis

“…Since many key biological processes including the development, differentiation, and cell cycles are regulated by microRNAs, the abnormal expression of microRNAs is often associated with the initialization and progression of many diseases [ 6 , 7 ]. Thus miRNAs usually could serve as suitable biomarkers for many diseases, such as neurodevelopmental disorders [ 8 ], cancer, and cardiovascular disease [ 9 – 11 ].…”

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

“…Functional enrichment analysis results show that 439 GO terms and 128 KEGG pathways are significantly associated with the target genes of the hub miRNAs. Moreover, several significantly enriched GO biological processes and KEGG pathways, including Cell cycle arrest (GO: 0007050), Regulation of immune response (GO: 0050776) ( Gupta et al, 2014 ) ( Ander et al, 2015 ), Nervous system development (GO:0007399) ( Hu et al, 2017 ), NF-kappa B signaling pathway (hsa04064) ( Malik et al, 2011 ), Long-term depression (hsa04730) ( Monday et al, 2018 ), Wnt signaling pathway (hsa04310) ( Shen et al, 2016 ), and gastric cancer (hsa05226) ( Wasilewska and Klukowski, 2015 ) are closely associated with the progression and development of ASD ( Figure 4B ). As for the identified hub miRNA sponges, functional enrichment analysis results indicate that they are significantly enriched in SMAD binding (GO: 0046332).…”

“…By binding with messenger RNAs (mRNAs) at the post-transcriptional level, miRNAs as tiny non-coding RNA molecules (∼22 nucleotides) can induce repression or translational inhabitation of mRNAs ( Ambros, 2004 ). Previous studies ( Ander et al, 2015 ; Hu et al, 2017 ; Shen et al, 2016 ) have elucidated that miRNAs participate in several biological processes that are closely associated with ASD, including synaptic plasticity and neuronal development ( Hu et al, 2017 ), immune response ( Ander et al, 2015 ), and signaling pathways ( Shen et al, 2016 ). These studies have also indicated that miRNAs and their corresponding targets could help to uncover ASD pathogenesis.…”

Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges

“…Without the knowledge, we cannot build the precision models, and we even cannot identify the risk factors and the pathogens at the systems level. The building of knowledge base for CCA can help us to connect the reported factors and to build the systems biological model for the investigation of heterogeneity of CCA and to identify the key players or the drivers in different steps of the CCA evolution 91 .…”