Objective: This study aimed to identify candidate protein biomarkers from maternal serum for Down syndrome (DS) by integrated proteomic and bioinformatics analysis.
Methods: A pregnancy DS group of 18 women and a control group with the same number were prepared, and the maternal serum proteins were analyzed by isobaric tags for relative and absolute quantitation and mass spectrometry, to identify DS differentially expressed maternal serum proteins (DS-DEMSPs). Comprehensive bioinformatics analysis was then employed to analyze DS-DEMSPs both in this paper and seven related publications.
Results: Down syndrome differentially expressed maternal serum proteins from different studies are significantly enriched with common Gene Ontology functions, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, transcription factor binding sites, and Pfam protein domains, However, the DS-DEMSPs are less functionally related to known DS-related genes. These evidences suggest that common molecular mechanisms induced by secondary effects may be present upon DS carrying. A simple scoring scheme revealed Alpha-2-macroglobulin, Apolipoprotein A1, Apolipoprotein E, Complement C1s subcomponent, Complement component 5, Complement component 8, alpha polypeptide, Complement component 8, beta polypeptide and Fibronectin as potential DS biomarkers.
Conclusion: The integration of proteomics and bioinformatics studies provides a novel approach to develop new prenatal screening methods for noninvasive yet accurate diagnosis of DS.
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