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Novel algorithm for diagnosis of Arrhythmogenic cardiomyopathy and dilated cardiomyopathy: Key gene expression

Youming Zhang1, Jiaxi Xie2, Yizhang Wu1

  • 1Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.

The Journal of Gene Medicine
|December 15, 2022
PubMed

Insights

A new diagnostic algorithm using gene signatures can differentiate arrhythmogenic cardiomyopathy (ACM) from dilated cardiomyopathy (DCM). This tool shows potential for clinical practice and aids understanding of cardiac function in these conditions.

Area of Science:

  • Cardiology
  • Genomics
  • Bioinformatics

Background:

  • Arrhythmogenic cardiomyopathy (ACM) and dilated cardiomyopathy (DCM) present similar clinical symptoms, making differentiation challenging.
  • Accurate diagnosis is crucial for effective treatment and management of these distinct cardiac conditions.

Purpose of the Study:

  • To develop a novel diagnostic algorithm for distinguishing ACM from DCM.
  • To identify key gene signatures associated with each cardiomyopathy.

Main Methods:

  • Utilized public datasets of human ACM and DCM myocardial samples.
  • Applied consensus clustering, non-negative matrix factorization, principal component analysis, and weighted gene co-expression network analysis.
  • Employed machine learning algorithms (random forest, LASSO) to identify candidate genes and assessed diagnostic efficacy using ROC curves and nomograms.

Main Results:

  • Gene expression patterns in ACM and DCM were highly similar, necessitating advanced analytical approaches.
  • Identified thirteen candidate genes with a combined diagnostic value (AUC=0.86) for differentiating ACM from DCM.
  • TATA-box binding protein associated factor 15 showed a significant negative correlation with cardiac index and left ventricular ejection fraction.

Conclusions:

  • Developed an effective diagnostic model with key gene signatures to differentiate ACM and DCM in clinical settings.
  • Identified novel genes significantly related to cardiac function, enhancing the understanding of cardiomyopathy pathogenesis.
  • The proposed algorithm offers a potential tool for improved clinical diagnosis and patient management.
Abstract

Keywords:
WGCNAarrhythmogenic cardiomyopathydilated cardiomyopathygene expression profilingmachine learning

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