Screening biomarkers for autism spectrum disorder using plasma proteomics combined with machine learning methods
Xiaoxiao Tang1, Xiaoqian Ran1, Zhiyuan Liang1
1College of Life Science and Oceanography, Shenzhen University, Shenzhen 518071, PR China.
Insights
This study identified four plasma proteins (PPBP, APCS, FGG, PF4V1) as potential blood biomarkers for early autism spectrum disorder (ASD) detection. These biomarkers show promise for improving early diagnosis and intervention strategies in children.
Area of Science:
- Biochemistry
- Proteomics
- Neurodevelopmental Disorders
Background:
- Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder in children.
- Early intervention significantly improves outcomes for individuals with ASD.
- Identifying reliable biomarkers is crucial for early ASD detection and timely intervention.
Purpose of the Study:
- To investigate novel blood-based protein biomarkers for the early detection of autism spectrum disorder (ASD).
- To identify and validate a panel of proteins in plasma that can differentiate ASD cases from controls.
Main Methods:
- Utilized Sequential Window Acquisition of All Theoretical Spectra-Mass Spectrometry (SWATH-MS) for initial proteomic profiling.
- Employed machine learning algorithms to screen for candidate biomarkers from differentially expressed proteins.
- Validated potential biomarkers using targeted proteomics Multiple Reaction Monitoring (MRM) in an independent cohort.
Main Results:
- Identified 51 differentially expressed proteins (DEPs) associated with immune response and metabolic pathways.
- Machine learning identified 10 candidate protein biomarkers.
- Four proteins (PPBP, APCS, FGG, PF4V1) were validated as significant discriminators, achieving an Area Under the Curve (AUC) of 0.8087 for ASD screening.
Conclusions:
- A specific combination of four plasma proteins demonstrates significant potential as a screening tool for autism spectrum disorder (ASD).
- These findings support the development of blood-based assays for early ASD identification.
- Further research is warranted to confirm the clinical utility of these protein biomarkers in diverse populations.
Background And Aims:
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder in children. Early intervention is effective. Investigation of novel blood biomarkers of ASD facilitates early detection and intervention.
Materials And Methods:
Sequential window acquisition of all theoretical spectra-mass spectrometry (SWATH-MS)-based proteomics technology and 30 DSM-V defined ASD cases versus age- and sex-matched controls were initially evaluated, and candidate biomarkers were screened using machine learning methods. Candidate biomarkers were validated by targeted proteomics multiple reaction monitoring (MRM) analysis using an independent group of 30 ASD cases vs. controls.
Results:
Fifty-one differentially expressed proteins (DEPs) were identified by SWATH analysis. They were associated with the immune response, complements and coagulation cascade pathways, and apolipoprotein-related metabolic pathways. Machine learning analysis screened 10 proteins as biomarker combinations (TFRC, PPBP, APCS, ALDH1A1, CD5L, SPARC, FGG, SHBG, S100A9, and PF4V1). In the MRM analysis, four proteins (PPBP, APCS, FGG, and PF4V1) were significantly different between the groups, and their combination as a screening indicator showed high potential (AUC = 0.8087, 95 % confidence interval 0.6904-0.9252, p < 0.0001).
Conclusions:
Our study provides data that suggests that a few plasma proteins have potential use in screening for ASD.
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