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Background <p>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.</p> Methods <p>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).</p> Results <p>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.</p> Conclusion <p>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.</p> Graphical abstract <p></p>

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Integrated proteomics and machine learning for identifying candidate serum biomarkers in acute myocardial infarction-complicated cardiogenic shock: a prospective exploratory study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Xi Wang,
  • Qian-feng Xiao,
  • Fang-yang Huang,
  • Si Wang,
  • Ying Xu,
  • Xiao-bo Pu,
  • Yan Yang,
  • Mao Chen,
  • Xin Wei

摘要

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