Data Sheet 1_Screening of serum biomarkers for coronary artery calcification using DIA qua

320 patient serum samples were analyzed using DIA quantitative proteomics to identify biomarkers for coronary artery calcification. The study identified 39 differentially expressed proteins and validated three candidates—SMOC1, HSP90B1, and OPTN—via ELISA in a validation cohort of 260 cases. A logistic regression model incorporating these proteins and clinical indicators achieved an AUC of 0.894 for predicting CAC.

Use Cases

• Training biomarker discovery models based on 39 differentially expressed serum proteins.
• Validating proteomics findings with ELISA measurements from a 260-case cohort.
• Constructing clinical risk prediction models using age, serum uric acid, alkaline phosphatase, fasting blood glucose, and protein biomarkers.

Strengths

• Includes a two-stage case-control design with 320 total patients and a separate validation cohort of 260 cases.
• Model performance is quantitatively reported with an AUC of 0.894, NRI of 0.908, and IDI of 0.128.
• Candidate biomarkers were validated with ELISA, showing expression trends consistent with proteomics findings (P < 0.001).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is contained in a single 861.9 KB DOCX file, which may require parsing to extract structured data.

Provenance

Source

figshare

Collection Method

A two-stage case-control study employing DIA quantitative proteomics and ELISA validation.

Freshness

Last updated 2026-06-01 04:15:05; freshness should be verified.