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Multi-omics approaches, integrating genomics, transcriptomics, proteomics, and metabolomics, offer a powerful strategy for distinguishing subtypes of angina pectoris (AP). This integrated method enhances the precision and speed of diagnosing stable (SAP), unstable (UAP), and variant (VAP) angina pectoris.

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Area of Science:

  • Cardiovascular Research
  • Genomics and Systems Biology
  • Biomarker Discovery

Background:

  • Angina pectoris (AP) is a chest pain syndrome due to myocardial ischemia, with subtypes including stable (SAP), unstable (UAP), and variant (VAP) angina.
  • Complex mechanisms of AP hinder rapid diagnosis and treatment of its subtypes, posing risks for myocardial infarction and heart failure.
  • High-throughput sequencing and omics technologies have advanced disease mechanism elucidation.

Purpose of the Study:

  • To review research on multi-omics approaches for angina pectoris (AP) diagnosis.
  • To summarize the application of genomics, transcriptomics, proteomics, and metabolomics in identifying biomarkers for AP subtypes.
  • To discuss the integration of multi-omics data and compare single-omics versus multi-omics strategies for differentiating AP types.

Main Methods:

  • Systematic review of literature focusing on multi-omics studies and angina pectoris.
  • Analysis of research progress in genomics, transcriptomics, proteomics, and metabolomics applied to AP.
  • Evaluation of biomarker screening and data integration methods for AP subtypes.

Main Results:

  • Multi-omics approaches provide comprehensive insights into AP's molecular underpinnings.
  • Integration of multi-omics data facilitates the identification of robust biomarkers for distinguishing SAP, UAP, and VAP.
  • Single-omics approaches have limitations in accurately differentiating AP subtypes compared to integrated strategies.

Conclusions:

  • The integration of multi-omics technologies is crucial for accurate and rapid diagnosis of stable, unstable, and variant angina pectoris.
  • Multi-omics integration represents a promising avenue for advancing personalized medicine in cardiovascular diseases.
  • Further research into multi-omics data integration methods can optimize clinical decision-making for AP patients.