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Background <p>Dilated cardiomyopathy (DCM), a life-threatening cardiovascular disease, affects more than 0.04% of the general population. However, the key genes associated with DCM are still unknown.</p> Methods <p>Three publicly available datasets (GSE120895, GSE53081, and GSE42955) were obtained from the Gene Expression Omnibus (GEO) database. Weighted correlation network analysis (WGCNA), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these DEGs. A protein‒protein interaction (PPI) network was then constructed to select the hub genes.</p> Results <p>862 differentially expressed genes (DEGs) were identified using an integrated bioinformatics approach. WGCNA indicated that DEGs in the blue and turquoise modules exhibited the strongest associations with DCM risk (blue module: <i>r</i> = 0.29, <i>P</i> = 2.5e-4; turquoise module: <i>r</i> = 0.25, <i>P</i> = 1.6e-3). KEGG pathway enrichment analysis of the hub genes revealed that the RTK-RAS pathway score was associated with DCM risk. Receiver operating characteristic (ROC) analysis revealed that the expression levels of CTGF and APOA1 may serve as sensitive and specific predictors of DCM risk (AUC of CTGF = 0.83, <i>P</i> = 0.002; AUC of APOA1 = 0.867, <i>P</i> = 0.001). Furthermore, the expression of CTGF and APOA1 was confirmed by Western blot.</p> Conclusions <p>Our results revealed that CTGF and APOA1 are correlated with DCM risk and represent potential biomarkers worthy of further investigation. The identification of these hub genes and the implicated RTK-RAS pathway provides a hypothesis and a direction for future functional research on DCM.</p>

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Unveiling CTGF and APOA1: key hub genes and therapeutic targets in dilated cardiomyopathy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Ran Wang,
  • Jinmeng Chen,
  • Jian Zhu,
  • Xianlin Zhang,
  • Xiaomei Li,
  • Yiyao Jiang,
  • Pingping Jiang

摘要

Background

Dilated cardiomyopathy (DCM), a life-threatening cardiovascular disease, affects more than 0.04% of the general population. However, the key genes associated with DCM are still unknown.

Methods

Three publicly available datasets (GSE120895, GSE53081, and GSE42955) were obtained from the Gene Expression Omnibus (GEO) database. Weighted correlation network analysis (WGCNA), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these DEGs. A protein‒protein interaction (PPI) network was then constructed to select the hub genes.

Results

862 differentially expressed genes (DEGs) were identified using an integrated bioinformatics approach. WGCNA indicated that DEGs in the blue and turquoise modules exhibited the strongest associations with DCM risk (blue module: r = 0.29, P = 2.5e-4; turquoise module: r = 0.25, P = 1.6e-3). KEGG pathway enrichment analysis of the hub genes revealed that the RTK-RAS pathway score was associated with DCM risk. Receiver operating characteristic (ROC) analysis revealed that the expression levels of CTGF and APOA1 may serve as sensitive and specific predictors of DCM risk (AUC of CTGF = 0.83, P = 0.002; AUC of APOA1 = 0.867, P = 0.001). Furthermore, the expression of CTGF and APOA1 was confirmed by Western blot.

Conclusions

Our results revealed that CTGF and APOA1 are correlated with DCM risk and represent potential biomarkers worthy of further investigation. The identification of these hub genes and the implicated RTK-RAS pathway provides a hypothesis and a direction for future functional research on DCM.