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Plasma proteomics analysis in Atrial Septal Defect: Biomarker Discovery Utilizing WGCNA and Machine Learning Techniques

ABSTRACT: Atrial septal defect (ASD) poses challenges for diagnosis and risk stratification. The role of proteomic dysregulation remains underexplored. This study integrates plasma proteomics, co-expression networks, and machine learning to identify ASD diagnostic biomarkers. Methods: We characterized the plasma proteome of 82 ASD patients and 87 controls using 10x Blood + Data independent acquisition (DIA) mass spectrometry, quantifying 5,892 proteins. Weighted co-expression network analysis (WGCNA) identified phenotype-associated protein modules. Machine learning (logistic regression with feature selection) prioritized biomarkers, validated by enzyme-linked immunosorbent assay (ELISA). Results: 348 proteins were differentially expressed in ASD (130 up; 218 down). Functional enrichment revealed dysregulation in ribosomal subunits, extracellular matrix organization, and neutrophil extracellular trap formation. Integration of WGCNA and machine learning defined a biomarker signature: RAB8B, COPS8, FN3K, and RPL36. ELISA confirmed significant dysregulation in independent cohorts. Conclusion: We identified candidate protein biomarkers potentially disrupting cardiac septal morphogenesis. These findings advance understanding of ASD's molecular basis and highlight diagnostic targets, offering implications for improved screening and treatment strategies

ORGANISM(S): Homo Sapiens

SUBMITTER: Xiaoshan Zhang

PROVIDER: PXD069475 | iProX | Wed Oct 15 00:00:00 BST 2025

REPOSITORIES: iProX

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