ArticleJournal of proteome research2024
Proteomics Defines Plasma Biomarkers for the Early Diagnosis of Biliary Atresia.
Article in Journal of proteome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Integrated transcriptomic and co-expression network analysis identifiesTranslational pediatrics · 2025Article
- Development and validation of a screening model for early diagnosis of biliary atresia in neonates with cholestasis.Pediatric surgery international · 2025Article
- Elevated Epithelial Splicing Regulatory Protein 1 Expression in Biliary Atresia Indicates Its Potential as a Molecular Marker.Biomolecules · 2025Article
- Does thyroid function disorder play a vital role in the pathogenesis of neonatal cholestasis?Clinical and experimental hepatology · 2025Article
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early diagnosis of biliary atresia (BA) is crucial for improving the chances of survival and preserving the liver function of pediatric patients with BA. Herein, we performed proteomics analysis using data-independent acquisition (DIA) and parallel reaction monitoring (PRM) to explore potential biomarkers for the early diagnosis of BA compared to other non-BA jaundice cases. Consequently, we detected and validated differential protein expression in the plasma of patients with BA compared to the plasma of patients with intrahepatic cholestasis. Bioinformatics analysis revealed the enriched biological processes characteristic of BA by identifying the differential expression of specific proteins. Signaling pathway analysis revealed changes in the expression levels of proteins associated with an alteration in immunoglobulin levels, which is indicative of immune dysfunction in BA. The combination of polymeric immunoglobulin receptor expression and immunoglobulin lambda variable chain (IGL c2225_light_IGLV1-47_IGLJ2), as revealed via machine learning, provided a useful early diagnostic model for BA, with a sensitivity of 0.8, specificity of 1, accuracy of 0.89, and area under the curve value of 0.944. Thus, our study identified a possible effective plasma biomarker for the early diagnosis of BA and could help elucidate the underlying mechanisms of BA.
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