Biomarker prediction in autism spectrum disorder using a network-based approach

Area of Science:

• Neuroscience
• Genetics
• Bioinformatics

Background:

• Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed in early childhood.
• Early diagnosis and treatment are crucial for individuals with ASD.
• Identifying critical genes and regulatory molecules like miRNAs can improve early diagnosis.

Purpose of the Study:

• To propose a two-step framework for discovering genes and microRNAs (miRNAs) associated with autism.
• To identify key genetic and miRNA biomarkers for potential early diagnostic applications in ASD.

Main Methods:

• The FA_gene algorithm identifies autism-related genes using co-expression and protein-protein interaction networks.
• The DMN_miRNA algorithm detects crucial miRNAs by analyzing mRNA-miRNA interactions within a selected gene set.
• Utilized WGCNA and Set Cover algorithms for network analysis and biomarker identification.

Main Results:

• FA_gene identified 20 key genes, including TP53, TNF, and MAPK3, implicated in autism.
• DMN_miRNA pinpointed five critical miRNAs: hsa-mir-155-5p, hsa-mir-17-5p, hsa-mir-181a-5p, hsa-mir-18a-5p, and hsa-mir-92a-1-5p.
• The significance of identified genes and miRNAs was validated through enrichment analysis and prior research.

Conclusions:

• The FA_gene and DMN_miRNA algorithms offer a robust approach for biomarker discovery in ASD.
• Identified genes and miRNAs represent potential candidates for developing novel diagnostic tests for autism.
• These biomarkers may play roles in neurological functions and disorders, warranting further investigation.