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Published on: September 20, 2024
Subtypes identification on heart failure with preserved ejection fraction via network enhancement fusion using
Yongqing Wu1, Huihui Wang1, Zhi Li2
1Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, PR China.
Identifying heart failure with preserved ejection fraction (HFpEF) subtypes using multi-omics data improves prognosis. A novel network fusion method identified high-risk and low-risk HFpEF patient groups with distinct survival rates.
Area of Science:
- Cardiovascular Medicine
- Bioinformatics
- Genomics
Background:
- Heart failure with preserved ejection fraction (HFpEF) presents significant clinical heterogeneity, complicating effective treatment and prognosis.
- Understanding the molecular underpinnings of HFpEF is crucial for developing targeted therapies.
Purpose of the Study:
- To identify distinct clinical subtypes of HFpEF by integrating multi-omics data.
- To develop a novel computational framework for robust subtype discovery in complex diseases.
Main Methods:
- Integration of mRNA, DNA methylation, and microRNA expression data from HFpEF patients.
- Application of a novel network enhancement followed by similarity network fusion (ne-SNF) for data integration.
- Utilized spectral clustering to identify patient subgroups.
Main Results:
- The ne-SNF method successfully identified two distinct HFpEF patient subtypes with significantly different survival outcomes.
- A high-risk group (16.8% of patients) exhibited a 5-year mortality rate of 63.3%, compared to 33.0% in the low-risk group (83.2%).
- Identified differentially expressed genes, methylation patterns, and miRNAs associated with these subtypes, enriched in HFpEF-related pathways.
Conclusions:
- The ne-SNF approach provides a powerful pipeline for multi-omics data integration and subtype discovery in HFpEF.
- Subtyping HFpEF patients can lead to more precise prognostic assessments and personalized treatment strategies.
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