Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers

Hualei Dai1,2, Ying Liu3, Meng Zhu4

  • 1Cardiovascular Center, The Affiliated Hospital of Yunnan University, Yunnan University, Kunming, Yunnan, China.

Insights

New biomarkers GATM and MGST1 show promise for diagnosing hypertrophic cardiomyopathy (HCM). These findings may lead to better risk assessment and understanding of HCM progression, potentially involving immune cells.

Area of Science:

  • Cardiology
  • Biomarker Discovery
  • Machine Learning

Background:

  • Hypertrophic cardiomyopathy (HCM) is a hereditary heart condition with significant mortality.
  • The role of immune inflammation in HCM pathogenesis is not fully understood.

Purpose of the Study:

  • To identify novel biomarkers for hypertrophic cardiomyopathy (HCM) using machine learning.
  • To develop and validate a risk assessment model for HCM patients.

Main Methods:

  • Employed five machine learning algorithms (LASSO, SVM, RF, Boruta, XGBoost) to identify HCM biomarkers.
  • Developed a nomogram using GATM and MGST1 for risk assessment and validated it with clinical samples.
  • Analyzed GATM and MGST1 expression levels in HCM and normal heart tissues.

Main Results:

  • Identified five novel HCM biomarkers: DARS2, GATM, MGST1, SDSL, and ARG2.
  • GATM and MGST1 demonstrated significant diagnostic utility for HCM (AUC > 0.8) in training and test cohorts.
  • A risk assessment model based on GATM and MGST1 showed high performance (AUC 0.88-0.9).
  • GATM and MGST1 were upregulated in HCM tissues, distinguishing them from normal tissues (AUC 0.79-0.86).
  • Monocytes and multipotent progenitors (MPP) are implicated in HCM pathogenesis.

Conclusions:

  • GATM and MGST1 are novel, validated biomarkers for HCM diagnosis and risk stratification.
  • Elevated GATM and MGST1 expression in HCM tissues suggests their role in disease progression.
  • Monocytes and MPP may contribute to HCM development, with potential links to cancer pathways.