Comparative proteome analysis of dried blood spots for high-risk group screening in children with autism spectrum disorder

Abstract

Objective

Reliable biomarkers that assist in the diagnosis of autism spectrum disorder (ASD) are limited. This study aimed to identify proteins that can differentiate children with ASD from controls.

Methods

A total of 30 children with ASD and 30 control children participated in the study. Psychological tests and questionnaires to assess cognitive function, adaptive function, autism symptoms, and behavioral problems were administered. Dried blood spots collected from the participants were analyzed using the SWATH LC-MS platform. Core proteins were identified to build a classifying model to predict ASD and control group status.

Results

Among the 849 proteins quantified, 33 candidate proteins were identified by combining two different algorithms. Candidate proteins were involved in biological pathways related to the skin, muscle functioning, immune system, and cytoskeleton organization. Of the candidate proteins, we selected 7 core proteins that overlapped between different algorithms. The core proteins, PSME1, two isoforms of TPM1, two isoforms of TPM3, S100A6, and TBCA, were negatively correlated with the Childhood Autism Rating Scale, Aberrant Behavior Checklist, and Social Responsiveness Scale, and positively correlated with the Full-scale Intellectual Quotient. Furthermore, a logistic regression model with the core proteins predicted the ASD group with an area under the curve (AUC) of 0.956, sensitivity of 0.967, and specificity of 0.867.

Conclusion

We performed a proteomic analysis of dried blood spot (DBS) from ASD and control group children to explore candidate biomarkers. Our data supports the possibility of using proteins as potential biomarkers for ASD, although further verification is warranted in an independent cohort.