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Updated: May 23, 2025
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Harnessing integrated bioinformatics to identify new diagnostic and therapeutic strategies for heart failure
Shuo Sun1, Chaojie Lai2, Chengchen Huang2
1Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China; Jining Medical University, Jining 272067, China; School of Life Sciences, Jining Medical University, Rizhao 276826, China.
Insights
Researchers identified 18 genes and developed a heart failure (HF) diagnostic model using machine learning. Network-based analysis identified mirtazapine and triamterene as potential HF treatments, validated in mouse models.
Area of Science:
- Cardiovascular Research
- Genomics
- Pharmacology
Background:
- Heart failure (HF) presents a major public health challenge, especially for aging populations.
- New diagnostic and therapeutic strategies are crucial for managing HF.
- Transcriptome sequencing data offers a valuable resource for understanding HF.
Purpose of the Study:
- To identify HF-related genes and develop a diagnostic scoring model.
- To evaluate bioinformatics methods for identifying potential HF therapeutic drugs.
- To validate candidate drugs in an animal model of heart failure.
Main Methods:
- Analysis of large-scale transcriptome sequencing data from HF patients.
- Application of two machine learning algorithms for gene identification and model development.
- Comparison of three bioinformatics methods, including network-based proximity analysis, for drug target prediction.
- In vivo validation of candidate drugs (mirtazapine, cabergoline, triamterene) in a mouse model of acute myocardial infarction transitioning to chronic HF.
Main Results:
- Identification of 18 HF-related genes and a robust HF diagnostic scoring model.
- Network-based proximity analysis demonstrated superior predictive ability for identifying potential HF drugs.
- Mirtazapine showed cardioprotective effects in both early and chronic HF stages in mice.
- Triamterene (positive control) also exhibited protective effects, while cabergoline was effective in the early phase.
- Mechanistic studies implicated growth factor receptor-bound protein 14 and Ras-related protein Rab-3A in cardioprotection.
Conclusions:
- The study provides a novel diagnostic model and identifies promising therapeutic agents for heart failure.
- Network-based proximity analysis is a powerful tool for drug discovery in HF.
- Mirtazapine and triamterene warrant further investigation as potential treatments for HF.
- Understanding the mechanistic roles of specific proteins can guide future therapeutic development for HF.
Abstract:
Heart failure (HF) is a life-threatening condition that poses a significant challenge on public health, particularly among the elder populations. To develop new diagnostic and therapeutic strategies for HF, we analyzed large-scale transcriptome sequencing data from HF patients as an exploratory approach. We identified 18 HF-related genes and developed a robust scoring model for HF diagnosis, by applying two machine learning algorithms for data analysis. Meanwhile, we evaluated and compared the predictive abilities of three bioinformatics methods in identifying potential HF treatment drugs. Significantly, an unconventional network-based proximity analysis, integrating multidimensional drug target information, outperformed other methods in the assessment of predictive ability. To validate these findings, we tested several candidate drugs in a mouse model transitioning from acute myocardial infarction (MI) to chronic HF. Among the candidates, mirtazapine exhibited cardioprotective effects in both early (1-week) post-MI and chronic HF (4-week post-MI) settings, while cabergoline showed potential efficacy primarily in the early post-MI phase. Additionally, the screened triamterene, used as a positive control, exhibited protective effects in both early post-MI and chronic HF stages. Mechanistic studies revealed that growth factor receptor-bound protein 14 and Ras-related protein Rab-3A were critical to the observed cardioprotection. These findings provide valuable evidence and insights for exploring potential therapeutic agents for HF treatment.
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