Data-independent acquisition-based blood proteomics unveils predictive biomarkers for neonatal necrotizing enterocolitis
Year: 2024
Type: article
Study design: Observational Study
Abstract: Necrotizing enterocolitis (NEC) is a life-threatening condition affecting preterm infants, sometimes necessitating surgical treatment. This study aimed to analyze differentially expressed proteins (DEPs) and access their biological and clinical significance in the plasma of neonates with NEC. Peripheral blood samples were collected from NEC infants at various time points, and plasma was separated. Data-independent acquisition (DIA) technology was utilized to identify DEPs among NEC patients at different stages. Bioinformatic analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, and protein-to-protein interaction analyses were performed on the DEPs. External datasets, along with receiver operating characteristic curves and gene set enrichment analysis, were used to clinically and biologically validate the findings. DEPs between the NEC and pre-NEC groups indicated reduced protein, heme, nitrogen, and purine nucleotide biosynthesis during NEC formation. In addition, enriched DEPs among the NEC groups at different time points suggested reconstructed extracellular matrix, aberrant B-lymphocyte immune responses, and decreased glycosaminoglycan levels during NEC progression. These findings were both clinically and biologically validated using external datasets. Our study highlights the clinical and biological relevance of proteomics in NEC patients. This study demonstrates key pathways involved in NEC pathogenesis and establishes DIA mass spectrometry as a powerful and noninvasive tool for evaluating and predicting NEC formation and progression.
Authors: Feng Chen, Feng Chen, Kezhe Tan, Zhibao Lv, Faling Chen, Faling Chen, Weijue Xu, Xiaohui Gong, Li Lu, Hailiang Sun, Qinqin Fu, Wenjun Zhuang
Language: English
Topic: Infant Nutrition and Health
Subfield: Nutrition and Dietetics
Field: Nursing
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,790
Awards: G4462395992, G5570486293
OpenAlex ID: W4404513904
