Identification of important modules and biomarkers in diabetic cardiomyopathy based on WGCNA and LASSO analysis

Area of Science:

• Cardiovascular Research
• Metabolic Disorders
• Biomarker Discovery

Background:

• Diabetic cardiomyopathy (DCM) diagnosis lacks specific and sensitive biomarkers, posing a clinical challenge.
• There is an unmet need for reliable biomarkers to aid in DCM diagnosis and prognosis assessment.
• Identifying novel diagnostic markers is crucial for improving patient outcomes in type 2 diabetes.

Purpose of the Study:

• To identify specific diagnostic markers for diabetic cardiomyopathy.
• To develop a predictive model for DCM using gene expression data.
• To explore potential therapeutic targets for DCM.

Main Methods:

• Integrated two gene expression datasets (GSE106180, GSE161827) to identify differentially expressed genes (DEGs).
• Employed Weighted Gene Coexpression Network Analysis (WGCNA) and clustering to identify key gene modules and hub genes.
• Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct and validate a diagnostic model.

Main Results:

• Identified 3066 DEGs and observed significant differences in immune cell infiltration between control and DCM groups.
• Selected key genes from significant modules, with hub genes enriched in proteasomal and mitochondrial pathways.
• Developed a diagnostic model comprising OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB, achieving high AUC values (0.9333 and 0.96) in validation datasets.

Conclusions:

• A diagnostic model for diabetic cardiomyopathy was successfully constructed using six potential biomarkers.
• OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB show promise as novel biomarkers for DCM.
• These findings may enhance early diagnosis and treatment strategies for diabetic cardiomyopathy.