Dataset Information

Screening of Serum Biomarkers for Coronary Artery Calcification Using DIA Quantitative Proteomics and Construction of a Regression Model

ABSTRACT: Abstract Background: Using DIA quantitative proteomics technology to screen for coronary artery calcification (CAC)-associated differentially expressed proteins, this study explores the value of SMOC1, HSP90B1, and OPTN as potential serum biomarkers for CAC and constructs an individualized risk prediction model. Methods: A two-stage case-control design was employed, enrolling 320 patients divided into a CAC group (160 cases) and a non-calcified control group (160 cases). Sixty cases were stratified and randomly selected for the discovery cohort (30 cases each), with the remaining 260 cases forming the validation cohort. The discovery cohort samples were randomly divided into three biologically replicated subgroups. Serum samples were pooled in equal volumes for DIA proteomics analysis to screen differentially expressed proteins, followed by bioinformatics analysis. Candidate proteins in the validation cohort were measured via ELISA. Single-factor and multivariate logistic regression analyses were conducted using clinical data to construct a nomogram prediction model. Model performance was evaluated using ROC curves, calibration curves, and decision curves. Results: A total of 39 differentially expressed proteins were identified (18 upregulated and 21 downregulated). GO enrichment analysis revealed that the differentially expressed proteins were enriched in protein folding, immune receptor signaling pathways, and apoptosis process regulation. KEGG enrichment analysis showed enrichment in T cell receptor signaling pathways, Notch signaling pathways, and calcium reabsorption pathways. SMOC1 (upregulated), HSP90B1 (downregulated), and OPTN (downregulated) were selected as candidate proteins. ELISA validation results were consistent with proteomics findings (all P < 0.001). Multivariate logistic regression analysis identified age, serum uric acid, alkaline phosphatase, fasting blood glucose, SMOC1, HSP90B1, and OPTN as independent predictors of CAC (P < 0.05). The regression model constructed based on these indicators achieved an AUC of 0.894 (95% CI: 0.855–0.933) for CAC prediction. Calibration curves demonstrated good agreement between predicted and observed probabilities, while decision curves indicated positive clinical net benefit within the 0.1–0.8 probability threshold range. Conclusion: SMOC1, HSP90B1, and OPTN are potential serum biomarkers for coronary artery calcification (CAC). A nomogram model incorporating these three biomarkers and clinical indicators performs well in predicting CAC.

ORGANISM(S): Homo Sapiens

SUBMITTER: XIaoyu Liu

PROVIDER: PXD075378 | iProX | Sat Mar 07 00:00:00 GMT 2026

REPOSITORIES: iProX

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Screening of Serum Biomarkers for Coronary Artery Calcification Using DIA Quantitative Proteomics and Construction of a Regression Model

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