Bioinformatics-based screening of genes associated with dilated cardiomyopathy.

BACKGROUND

Due to the lack of appropriate diagnostic biomarkers and intervention targets, the diagnosis and treatment of dilated cardiomyopathy (DCM) in clinical practice are considerably challenging. Therefore, this study aimed to identify reliable biomarker genes using bioinformatics methods to improve the clinical management of DCM.

METHODS

Three DCM gene datasets, GSE120895, GSE42955, and GSE3586, were downloaded from the Gene Expression Omnibus (GEO). Differential gene analysis was used to screen for differentially expressed genes in these datasets, and weighted gene coexpression network analysis (WGCNA) was used to screen for the gene coexpression modules most relevant to DCM. Machine learning algorithms and a Protein-protein interaction (PPI) network were used to screen for the core DCM genes in the gene coexpression module.

RESULTS

WGCNA identified the turquoise module as the most relevant gene module for DCM disease. Subsequently, machine learning algorithms identified 8 core genes while PPI screening identified 10 core genes. was found in both machine learning algorithms and PPI screening.

CONCLUSIONS

In this study, the gene was found to be a core gene in DCM, demonstrating the closest association with this disease. Further research on is expected to provide a target for the diagnosis and treatment of DCM.