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Identification of Biomarkers Associated with Heart Failure Caused by Idiopathic Dilated Cardiomyopathy Using WGCNA
1Department of Clinical Laboratory, Jining First People's Hospital, Jining, Shandong, China.
Insights
Idiopathic dilated cardiomyopathy-induced heart failure (IDCM-HF) research identifies Aquaporin 3 (AQP3) and Cytochrome P450 2J2 (CYP2J2) as potential diagnostic markers. These genes show promise for novel treatments and improved diagnosis of IDCM-HF.
Area of Science:
- Cardiology
- Genomics
- Biomarker Discovery
Background:
- Idiopathic dilated cardiomyopathy-induced heart failure (IDCM-HF) pathogenesis and genetic factors remain poorly understood.
- There is a critical need for specific diagnostic markers and effective treatments for IDCM-HF.
Purpose of the Study:
- To elucidate the molecular mechanisms underlying IDCM-HF.
- To identify potential molecular diagnostic markers for IDCM-HF.
Main Methods:
- Gene expression profiles from IDCM-HF and non-heart failure (NF) specimens were analyzed.
- Weighted gene co-expression network analysis (WGCNA) and machine learning algorithms (SVM-RFE, LASSO) were employed to identify key genes.
- Differentially expressed genes (DEGs) were identified and screened for diagnostic efficacy.
Main Results:
- 490 differentially expressed genes (DEGs) were identified between IDCM-HF and NF groups.
- Thirteen candidate genes were selected, with Aquaporin 3 (AQP3) and Cytochrome P450 2J2 (CYP2J2) demonstrating high diagnostic efficacy.
- AQP3 was significantly downregulated, while CYP2J2 was significantly upregulated in IDCM-HF patients compared to NF controls.
Conclusions:
- This study uniquely combines WGCNA and machine learning for IDCM-HF biomarker screening.
- AQP3 and CYP2J2 are proposed as novel diagnostic markers for IDCM-HF.
- These identified genes may serve as potential therapeutic targets for IDCM-HF.
Background:
The genetic factors and pathogenesis of idiopathic dilated cardiomyopathy-induced heart failure (IDCM-HF) have not been understood thoroughly; there is a lack of specific diagnostic markers and treatment methods for the disease. Hence, we aimed to identify the mechanisms of action at the molecular level and potential molecular markers for this disease.
Methods:
Gene expression profiles of IDCM-HF and non-heart failure (NF) specimens were acquired from the database of Gene Expression Omnibus (GEO). We then identified the differentially expressed genes (DEGs) and analyzed their functions and related pathways by using "Metascape". Weighted gene co-expression network analysis (WGCNA) was utilized to search for key module genes. Candidate genes were identified by intersecting the key module genes identified via WGCNA with DEGs and further screened via the support vector machine-recursive feature elimination (SVM-RFE) method and the least absolute shrinkage and selection operator (LASSO) algorithm. At last, the biomarkers were validated and evaluated the diagnostic efficacy by the area under curve (AUC) value and further confirmed the differential expression in the IDCM-HF and NF groups using an external database.
Results:
We detected 490 genes exhibiting differential expression between IDCM-HF and NF specimens from the GSE57338 dataset, with most of them being concentrated in the extracellular matrix (ECM) of cells related to biological processes and pathways. After screening, 13 candidate genes were identified. Aquaporin 3 (AQP3) and cytochrome P450 2J2 (CYP2J2) showed high diagnostic efficacy in the GSE57338 and GSE6406 datasets, respectively. In comparison to the NF group, AQP3 was significantly down-regulated in the IDCM-HF group, while CYP2J2 was significantly up-regulated.
Conclusion:
As far as we know, this is the first study that combines WGCNA and machine learning algorithms to screen for potential biomarkers of IDCM-HF. Our findings suggest that AQP3 and CYP2J2 could be used as novel diagnostic markers and treatment targets of IDCM-HF.
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