Related Experiment Video
Updated: Jul 12, 2025
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA
Wei Yu1, Lingjiao Li1, Xingling Tan1
1Chongqing Medical University, Chongqing, China.
Insights
Researchers identified five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) as potential biomarkers for dilated cardiomyopathy (DCM). These genes show promise for diagnosing and potentially treating this progressive heart condition.
Area of Science:
- Cardiology
- Molecular Biology
- Genetics
Background:
- Dilated cardiomyopathy (DCM) is a severe heart condition with high mortality.
- The precise molecular mechanisms underlying DCM remain unclear.
- Identifying novel biomarkers and therapeutic targets for DCM is critical.
Purpose of the Study:
- To identify key genes associated with dilated cardiomyopathy (DCM).
- To develop predictive models for DCM diagnosis.
- To explore the relationship between identified genes and immune cell infiltration in DCM.
Main Methods:
- Weighted Gene Co-expression Network Analysis (WGCNA) and Cytoscape algorithms were used to screen for hub genes.
- Gene expression was validated in a doxorubicin-induced mouse model of DCM using RT-qPCR.
- Risk prediction and neural network models were constructed and validated.
Main Results:
- Eight hub genes were identified, with five (ASPN, MFAP4, PODN, HTRA1, FAP) showing significantly higher expression in DCM mice.
- Predictive models demonstrated high accuracy and sensitivity for DCM diagnosis.
- Significant differences in immune cell abundance were observed between DCM and normal samples.
Conclusions:
- The identified genes (ASPN, MFAP4, PODN, HTRA1, FAP) are strongly associated with DCM.
- These genes represent potential diagnostic biomarkers for DCM.
- Further research into these genes may lead to novel therapeutic strategies for DCM.
Background:
Dilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.
Methods:
The hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.
Results:
A total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.
Conclusion:
The current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.
Related Concept Videos
01:30Cardiomyopathy II: Dilated Cardiomyopathy
01:11Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
01:25Cardiomyopathy I: Introduction and Classification
01:29Cardiomyopathy III: Hypertrophic Cardiomyopathy