Identifying Immune-related Molecular Biomarkers in Autism Spectrum Disorder Using Data-independent Acquisition Proteomics and Machine Learning.

Abstract

This study presents a reproducible protocol for identifying serum protein biomarkers associated with autism spectrum disorder (ASD) using data-independent acquisition (DIA) mass spectrometry combined with machine learning (ML). DIA enables unbiased, high-resolution profiling of the serum proteome, including low-abundance proteins, while ensuring reproducibility across samples. ML approaches were applied to select diagnostically informative protein panels and improve model robustness. The analysis included serum from 99 children with ASD and 70 age-matched controls. High-abundance proteins were depleted, peptides were prepared using standardized digestion and fractionation procedures, and DIA was performed on a high-resolution mass spectrometer. Data processing and quantification identified differentially expressed proteins, which underwent functional enrichment analysis. Eight immune-related proteins emerged as strong candidates for biomarker development. A logistic regression model trained on these proteins achieved 95.27% accuracy, a Kappa value of 0.9025, and an AUC of 1.000 in cross-validation. These findings demonstrate the potential of DIA-based proteomics, combined with machine learning, as a robust framework for biomarker discovery in ASD and for adaptation in broader clinical research.