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From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism
Ecmel Mehmetbeyoglu1,2, Abdulkerim Duman3, Serpil Taheri2,4
1Department of Cancer and Genetics, Cardiff University, Cardiff CF14 4XN, UK.
Insights
Researchers identified specific microRNAs (miRNAs) significantly downregulated in children with Autism Spectrum Disorder (ASD). MiR-126-3p shows potential as a reliable biomarker for earlier and more accurate ASD diagnosis.
Area of Science:
- Biochemistry and Molecular Biology
- Neuroscience
- Genetics
Background:
- Autism Spectrum Disorder (ASD) impacts communication and social interaction, presenting significant societal and scientific challenges.
- Current ASD diagnostic methods are subjective and time-consuming, highlighting the need for objective biomarkers.
- ASD prevalence is increasing, with estimates at 1 in 36 children in 2020.
Purpose of the Study:
- To identify novel, reliable diagnostic biomarkers for Autism Spectrum Disorder (ASD).
- To explore the potential of microRNAs (miRNAs) as non-invasive, cost-effective diagnostic tools for ASD.
- To investigate the diagnostic utility of specific downregulated miRNAs in ASD patients.
Main Methods:
- Serum samples from 45 children with ASD and 21 controls were analyzed for microRNA (miRNA) expression.
- Quantitative analysis of 372 microRNAs (miRNAs) was performed.
- Machine learning models, including K-nearest neighbors (KNN), were applied for diagnostic classification.
Main Results:
- Six specific miRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p, and miR-499a-5p) were significantly downregulated in all ASD patients.
- miR-126-3p demonstrated particular potential as a diagnostic biomarker for ASD.
- Machine learning models showed promise in accurately diagnosing ASD using miRNA profiles.
Conclusions:
- Specific downregulated miRNAs in serum can serve as potential biomarkers for Autism Spectrum Disorder (ASD).
- miR-126-3p is a promising candidate biomarker for early and accurate ASD diagnosis.
- Integrating miRNA analysis with machine learning offers a powerful approach for objective ASD diagnostics.
Abstract:
Autism Spectrum Disorder (ASD) poses significant challenges to society and science due to its impact on communication, social interaction, and repetitive behavior patterns in affected children. The Autism and Developmental Disabilities Monitoring (ADDM) Network continuously monitors ASD prevalence and characteristics. In 2020, ASD prevalence was estimated at 1 in 36 children, with higher rates than previous estimates. This study focuses on ongoing ASD research conducted by Erciyes University. Serum samples from 45 ASD patients and 21 unrelated control participants were analyzed to assess the expression of 372 microRNAs (miRNAs). Six miRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p, and miR-499a-5p) exhibited significant downregulation in all ASD patients compared to healthy controls. The current study endeavors to identify dependable diagnostic biomarkers for ASD, addressing the pressing need for non-invasive, accurate, and cost-effective diagnostic tools, as current methods are subjective and time-intensive. A pivotal discovery in this study is the potential diagnostic value of miR-126-3p, offering the promise of earlier and more accurate ASD diagnoses, potentially leading to improved intervention outcomes. Leveraging machine learning, such as the K-nearest neighbors (KNN) model, presents a promising avenue for precise ASD diagnosis using miRNA biomarkers.
