Integrated proteomics and machine learning for identifying candidate serum biomarkers in acute myocardial infarction-complicated cardiogenic shock: a prospective exploratory study
- 1Department of Cardiology, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610041 People’s Republic of China
- 2Cardiac Structure and Function Research Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041 People’s Republic of China
Abstract
Background: Cardiogenic shock secondary to acute myocardial infarction (AMI-CS) prohibitively impacts survival. This prospective study aimed to discover and internally verify candidate serum protein biomarkers and evaluate their potential prognostic value for 30-day mortality in AMI-CS patients.
Methods: AMI-CS patients were consecutively enrolled into discovery (n = 30) and verification (n = 60) cohorts. Candidate biomarkers were screened using Data-Independent Acquisition (DIA) mass spectrometry, analyzed via differential abundance and weighted gene co-expression network analysis (WGCNA), and verified via targeted Parallel Reaction Monitoring (PRM). Boruta feature selection for five machine learning algorithms were embedded within a rigorous nested cross-validation scheme. Incremental prognostic value over clinical predictors was evaluated using Cox regression and metrics including the integrated discrimination improvement (IDI).
Results: DIA proteomics identified 216 proteins differentially abundant between 30-day survivors and non-survivors, and WGCNA defined an outcome-associated module linked to shock severity and enriched for oxidative stress and energy metabolism. During PRM verification, leakage-free nested cross-validation random forest model selected a seven-protein panel (YWHAZ, QDPR, MDH2, FAH, PSMA1, FABP5, and AHCY), which achieved a mean area under the ROC curve of 0.82 (95% CI: 0.69–0.95) for 30-day mortality. Incorporating this panel significantly enhanced the IDI of the SCAI model (IDI 0.143, P = 0.016) and the IABP-SHOCK II model (0.138, P = 0.034), which remained independent after adjusting for core clinical confounders.
Conclusion: This exploratory study identifies a seven-protein candidate panel with potential value for early short-term risk stratification in AMI-CS patients. Large-scale multi-center external validation is strictly warranted to confirm its real-world clinical utility.
Graphical abstract:
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12014-026-09626-z.
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BibTeX
@article{wang2026integrated,
title = {{Integrated proteomics and machine learning for identifying candidate serum biomarkers in acute myocardial infarction-complicated cardiogenic shock: a prospective exploratory study}},
author = {Xi Wang and Qian-feng Xiao and Fang-yang Huang and Si Wang and Ying Xu and Xiao-bo Pu and Yan Yang and Mao Chen and Xin Wei},
journal = {Clinical Proteomics},
year = {2026},
month = jul,
doi = {10.1186/s12014-026-09626-z},
url = {https://doi.org/10.1186/s12014-026-09626-z},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Integrated proteomics and machine learning for identifying candidate serum biomarkers in acute myocardial infarction-complicated cardiogenic shock: a prospective exploratory study
AU - Xi Wang
AU - Qian-feng Xiao
AU - Fang-yang Huang
AU - Si Wang
AU - Ying Xu
AU - Xiao-bo Pu
AU - Yan Yang
AU - Mao Chen
AU - Xin Wei
PY - 2026
DA - 2026/07/20
JO - Clinical Proteomics
DO - 10.1186/s12014-026-09626-z
UR - https://doi.org/10.1186/s12014-026-09626-z
AB - Background: Cardiogenic shock secondary to acute myocardial infarction (AMI-CS) prohibitively impacts survival. This prospective study aimed to discover and internally verify candidate serum protein biomarkers and evaluate their potential prognostic value for 30-day mortality in AMI-CS patients. Methods: AMI-CS patients were consecutively enrolled into discovery (n = 30) and verification (n = 60) cohorts. Candidate biomarkers were screened using Data-Independent Acquisition (DIA) mass spectrometry, analyzed via differential abundance and weighted gene co-expression network analysis (WGCNA), and verified via targeted Parallel Reaction Monitoring (PRM). Boruta feature selection for five machine learning algorithms were embedded within a rigorous nested cross-validation scheme. Incremental prognostic value over clinical predictors was evaluated using Cox regression and metrics including the integrated discrimination improvement (IDI). Results: DIA proteomics identified 216 proteins differentially abundant between 30-day survivors and non-survivors, and WGCNA defined an outcome-associated module linked to shock severity and enriched for oxidative stress and energy metabolism. During PRM verification, leakage-free nested cross-validation random forest model selected a seven-protein panel (YWHAZ, QDPR, MDH2, FAH, PSMA1, FABP5, and AHCY), which achieved a mean area under the ROC curve of 0.82 (95% CI: 0.69–0.95) for 30-day mortality. Incorporating this panel significantly enhanced the IDI of the SCAI model (IDI 0.143, P = 0.016) and the IABP-SHOCK II model (0.138, P = 0.034), which remained independent after adjusting for core clinical confounders. Conclusion: This exploratory study identifies a seven-protein candidate panel with potential value for early short-term risk stratification in AMI-CS patients. Large-scale multi-center external validation is strictly warranted to confirm its real-world clinical utility. Graphical abstract: Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12014-026-09626-z.
SN - 1542-6416
SN - 1559-0275
ER -