Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression
Peipei Chen1, Bo Long2, Yi Xu1
1Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Insights
Arrhythmogenic right ventricular cardiomyopathy (ARVC) research identified four key genes (CXCL2, TNFRSF11B, LIFR, C5AR1) and pathways. These findings may aid in diagnosing ARVC, a sudden cardiac arrest cause in young people.
Area of Science:
- Genetics and Genomics
- Cardiovascular Diseases
- Systems Biology
Background:
- Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a significant cause of sudden cardiac death in young individuals.
- The progression of ARVC involves a complex gene interaction network impacting diagnosis and prognosis.
- Identifying key genes and pathways is crucial for understanding ARVC's pathogenic mechanisms.
Purpose of the Study:
- To identify crucial genes and biological pathways involved in the pathogenic mechanism of ARVC.
- To explore the potential of identified genes and pathways for ARVC diagnosis.
- To apply systems biology approaches to complement traditional genetic analyses in ARVC research.
Main Methods:
- Screening of differentially expressed genes (DEGs) between ARVC tissues and donor myocardial samples.
- Weighted Gene Coexpression Network Analysis (WGCNA) to identify significant gene modules associated with ARVC.
- Gene Set Net Correlations Analysis (GSNCA) to pinpoint key pathways and hub genes.
- Validation of hub genes using t-tests and receiver operating characteristic (ROC) curves.
Main Results:
- A total of 619 DEGs were identified between ARVC and control samples.
- WGCNA highlighted two significant gene modules (brown and turquoise) linked to ARVC.
- GSNCA identified four key ARVC-related pathways: cytokine-cytokine receptor interaction, chemokine signaling, neuroactive ligand receptor interaction, and JAK-STAT signaling.
- Four hub genes (CXCL2, TNFRSF11B, LIFR, C5AR1) were identified and validated with AUC > 0.8.
Conclusions:
- The identified hub genes and pathways may play a role in ARVC diagnosis.
- Systems biology computational methods offer a novel approach to studying rare diseases like ARVC.
- Further experimental validation is needed, but these findings provide new insights for therapeutic interventions.
Abstract:
As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine-cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.
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