# Unveiling Diagnostic Biomarkers in Autism: A Comparative Proteome Analysis of CNTNAP2 Knockout Mice and Human ASD Patients

## TL;DR

This study identifies a set of 10 proteins that could serve as biomarkers for autism by comparing mouse models and human patients.

## Contribution

The novel contribution is the identification of a cross-species protein panel using proteomic and machine learning approaches for autism diagnosis.

## Key findings

- 132 proteins were found consistently dysregulated in both CNTNAP2 knockout mice and human ASD patients.
- A ten-protein panel was identified that robustly discriminates ASD from control samples using machine learning.
- The biomarker panel showed strong performance in independent test sets with an AUC of 0.75.

## Abstract

Autism Spectrum Disorder (ASD) is a biologically heterogeneous neurodevelopmental condition, presenting a major barrier to the identification of robust and translatable molecular biomarkers. Here, we employ a cross-species proteomic framework to identify conserved protein signatures associated with ASD. Quantitative proteomic profiling of brain and serum from CNTNAP2 knockout mice, integrated with serum proteomes from individuals with ASD, revealed 132 proteins consistently dysregulated across species. Functional pathway analyses implicated coordinated alterations in lipid metabolism, synaptic signaling, and immune regulation. To prioritize diagnostically informative candidates, we applied machine learning-based feature selection and identified a minimal panel of ten proteins (COL1A1, ITIH4, CLU, NID1, C5, MASP1, PON1, PLTP, HSPA5, and FETUB) that robustly discriminated ASD from control samples. Gene ontology and KEGG pathway analyses highlighted enrichment of immune regulatory pathways, synaptic transmission, oxidative stress responses, and lipid metabolic processes, consistent with emerging models linking neuroimmune dysregulation and metabolic imbalance to ASD pathophysiology. An XGBClassifier trained on this biomarker panel achieved strong performance in independent test sets (AUC = 0.75). Together, these findings establish cross-species proteomic integration combined with machine learning as a powerful strategy for uncovering conserved, biologically grounded biomarkers in ASD, providing a framework for future validation and translational development.