Machine learning-based plasma proteomic analysis identifies a novel disease-defining biomarkers

ABSTRACT: Recent advances in serum proteomic analysis tools have expanded our understanding of various diseases. To identify a novel disease-specific proteomic markers, we used the high-throughput proximity extension assay (PEA) platform to profile plasma proteins from 62 patients with atopic dermatitis (AD), or ulcerative colitis (UC) and 29 healthy subjects. Differentially expressed protein (DEP) analysis yielded 85 (20 unique) and 99 (14 unique) upregulated proteins in AD and UC, respectively, compared to healthy subjects. We integrated a machine-learning (ML) model and selected 24 proteins to distinguish between the diseases, which accurately predicted disease-defining biomarkers based on distinctive proteomic signatures and disease severity. Correlation analysis with disease severity identified upregulated ML-selected proteins such as CCL13, CCL26, CD70, CDON, LY6D and MMP1 in AD, and ITM2A and REG4 in UC. Among these identified proteins, we suggest CDON, CD70 and LY6D, which had highly correlated expression levels, as AD-specific biomarkers. Thus, our results provide insight into the application of ML algorithms for disease diagnosis, and our model may be expanded to other disease contexts.

INSTRUMENT(S): Olink Explore 384

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

DISEASE(S): Atopic Dermatitis,Ulcerative Colitis

SUBMITTER: Jasper Roldan Go  

LAB HEAD: Hyun Je Kim

PROVIDER: PAD000005 | Pride | 2025-03-05

REPOSITORIES: Pride