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Updated: Jun 17, 2025
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Derivation and external validation of mass spectrometry-based proteomic model using machine learning algorithms to
Jianing Wu1, Ke Ma1, Jie Ma1
1Beijing Anzhen Hospital of Capital Medical University, Beijing, China; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China.
Background:
A poor prognosis is associated with atherosclerotic plaque rupture (PR) despite after conventional therapy for patients with acute coronary syndrome (ACS). Timely identification of PR improves the risk stratification and prognosis of ACS patients.
Methods:
A derivation cohort of 110 patients with ACS who underwent pre-intervention optical coherence tomography (OCT) were matched 1:1 to the PR and intact fibrous cap (IFC) groups according to traditional risk factors. Candidate PR proteins were identified via mass spectrometry (MS)-based proteomics using unbiased machine learning methods and were further validated by enzyme-linked immunosorbent assay (ELISA) in an external validation cohort of 85 patients with ACS. The performance of candidate biomakers was assessed using the receiver operating characteristic curve analysis.
Results:
1121 proteins were identified and 535 filtered proteins were used for analysis. Nine candidate proteins were screened by five machine learning algorithms. Three proteins (APOC3, RAB39A, and KNG1) were significantly different between the PR and IFC in validation cohort. The performance of plasm APOC3, RAB39A, and KNG1 for differentiating PR and IFC was superior to that of the conventional biomarkers and risk factors.
Conclusion:
The proteins (APOC3, RAB39A, and KNG1) serve as a potential novel diagnostic tool to identify PR in ACS patients.
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