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Development and verification of the nomogram for dilated cardiomyopathy gene diagnosis
Li-Qiang Zhou1, Chuan Liu1, Yeqing Zou1
1Jiangxi Key Laboratory of Molecular Medicine, The Second Affiliated Hospital of Nanchang University, 1 Minde Road, Donghu District, Nanchang, 330006, Jiangxi, China.
Abstract:
Dilated cardiomyopathy (DCM) is a primary myocardial disease of unclear mechanism and poor prevention. The purpose of this study is to explore the potential molecular mechanisms and targets of DCM via bioinformatics methods and try to diagnose and prevent disease progression early. We screened 333 genes differentially expressed between DCM and normal heart samples from GSE141910, and further used Weighted correlation network analysis to identify 197 DCM-related genes. By identifying the key modules in the protein-protein interaction network and Least Absolute Shrinkage and Selection Operator regression analysis, seven hub DCM genes (CX3CR1, AGTR2, ADORA3, CXCL10, CXCL11, CXCL9, SAA1) were identified. Calculating the area under the receiver's operating curve revealed that these 7 genes have an excellent ability to diagnose and predict DCM. Based on this, we built a logistic regression model and drew a nomogram. The calibration curve showed that the actual incidence is basically the same as the predicted incidence; while the C-index values of the nomogram and the four external validation data sets are 0.95, 0.90, 0.96, and 0.737, respectively, showing excellent diagnostic and predictive ability; while the decision curve indicated the wide applicability of the nomogram is helpful for clinicians to make accurate decisions.
Insights
This study identifies seven key genes (CX3CR1, AGTR2, ADORA3, CXCL10, CXCL11, CXCL9, SAA1) that can accurately diagnose and predict dilated cardiomyopathy (DCM). These findings offer potential for early disease detection and management.
Area of Science:
- Cardiovascular Biology
- Genomics
- Bioinformatics
Background:
- Dilated cardiomyopathy (DCM) is a primary myocardial disease with unknown mechanisms and limited prevention strategies.
- Early diagnosis and prevention of DCM progression remain significant clinical challenges.
Purpose of the Study:
- To explore potential molecular mechanisms and therapeutic targets for DCM using bioinformatics.
- To develop a diagnostic and predictive tool for early DCM detection and management.
Main Methods:
- Differential gene expression analysis of 333 genes from GSE141910 dataset.
- Weighted gene correlation network analysis (WGCNA) to identify 197 DCM-related genes.
- Protein-protein interaction network analysis, LASSO regression, and nomogram construction for diagnostic model development.
Main Results:
- Seven hub genes (CX3CR1, AGTR2, ADORA3, CXCL10, CXCL11, CXCL9, SAA1) were identified as key players in DCM.
- A logistic regression model and nomogram demonstrated excellent diagnostic and predictive ability for DCM (C-index values ranging from 0.737 to 0.96).
- Validation across multiple datasets confirmed the nomogram's reliability and clinical applicability.
Conclusions:
- The identified seven hub genes serve as potential biomarkers for DCM diagnosis and prognosis.
- The developed nomogram provides a valuable tool for clinicians to accurately assess DCM risk and guide treatment decisions.
- This study offers novel insights into DCM pathogenesis and potential avenues for early intervention.
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01:30Cardiomyopathy II: Dilated Cardiomyopathy
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