Identification and multi-layered validation of seven diagnostic biomarkers for dilated cardiomyopathy via integrative
Jingwei Li1, Zhongyang Song2,3, Guanwei Wang1
1College of Clinical Traditional Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, China.
Insights
This study identified seven key molecular biomarkers for dilated cardiomyopathy (DCM) using multi-omics data. These findings offer promising candidates for future heart failure research and diagnostics.
Area of Science:
- Cardiology
- Genomics
- Bioinformatics
Background:
- Dilated cardiomyopathy (DCM) is a leading cause of heart failure with limited specific molecular biomarkers.
- Identifying tissue-level biomarkers is crucial for understanding and diagnosing DCM.
Purpose of the Study:
- To identify and prioritize candidate biomarkers for DCM using an integrative multi-omics bioinformatics framework.
- To evaluate the diagnostic potential and biological relevance of these candidates in myocardial tissue.
Main Methods:
- Integrated bulk myocardial transcriptomic data with WGCNA hub genes to identify candidate biomarkers.
- Applied machine learning algorithms (LASSO, random forest, SVM-RFE, XGBoost) for core candidate selection.
- Validated candidates using external microarray and RNA-seq datasets, GTEx, HPA, and snRNA-seq data.
Main Results:
- Seven candidate biomarkers (HMGN2, AQP3, SERPINA3, FREM1, HMOX2, CSDC2, TUBA3E) were prioritized.
- SERPINA3, HMOX2, FREM1, and HMGN2 showed consistent support across validation cohorts.
- Orthogonal validation confirmed cardiac expression and cell-type localization, with some candidates enriched in cardiomyocytes or fibroblasts.
Conclusions:
- Identified seven prioritized, disease-responsive molecular candidates for DCM.
- Findings provide testable hypotheses for translational research in heart failure.
- These candidates are valuable for future biomarker development rather than immediate therapeutic targets.
Background:
Dilated cardiomyopathy (DCM) is the most common non-ischemic cardiomyopathy and a major cause of heart failure, but disease-specific molecular biomarkers remain limited. This study aimed to identify and prioritize tissue-level, disease-responsive candidate biomarkers for DCM using an integrative multi-omics bioinformatics framework.
Methods:
Bulk myocardial transcriptomic data from GSE57338 were used as the discovery cohort, and GSE26887, GSE42955, and GSE79962 served as external microarray validation cohorts. GSE116250 was used for independent RNA-seq validation. Differentially expressed genes were intersected with WGCNA hub genes to define candidate genes. Four machine-learning algorithms, including LASSO, random forest, SVM-RFE, and XGBoost, were applied to identify core diagnostic candidates. Tissue-level model performance was evaluated by ROC analysis, calibration assessment, nomogram visualization, and decision curve analysis. Orthogonal validation was performed using GTEx, HPA, and snRNA-seq data. Immune infiltration, bidirectional Mendelian randomization, and CellOracle-based GRN analysis with a co-expression-based functional importance score were used as hypothesis-generating analyses. The workflow explicitly separated diagnostic performance, localization evidence, and exploratory mechanistic context in myocardial tissue.
Results:
Integration of 309 DEGs and 2,093 WGCNA hub genes yielded 270 candidates. Seven candidates-HMGN2, AQP3, SERPINA3, FREM1, HMOX2, CSDC2, and TUBA3E-were selected by at least three algorithms. In the discovery cohort, the RF model achieved an AUC of 0.985 and the logistic model achieved a C-statistic of 0.993; however, these estimates were interpreted as potentially optimistic upper bounds because feature selection was not nested within cross-validation. External validation showed uneven robustness: SERPINA3, HMOX2, FREM1, and HMGN2 were consistently supported across microarray and RNA-seq cohorts, whereas AQP3, CSDC2, and TUBA3E were exploratory. GTEx, HPA, and snRNA-seq supported cardiac expression and cell-type localization, including cardiomyocyte enrichment of CSDC2/HMOX2 and fibroblast enrichment of FREM1. MR and GRN analyses suggested disease-responsive rather than disease-driving biology, including possible heart failure-associated AQP3 downregulation and a putative PPARGC1A-CSDC2/HMOX2 regulatory context.
Conclusion:
This study identifies seven prioritized, predominantly disease-responsive tissue-level molecular candidates for DCM. These findings provide candidates and testable hypotheses for future translational research, rather than disease-driving therapeutic targets or a directly applicable clinical test.