Department of Cardiology, First Affiliated Hospital, Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi, 530021, China.
BMC Cardiovasc Disord. 2024 Oct 18;24(1):577. doi: 10.1186/s12872-024-04255-6.
The aim of this study was to investigate potential hub genes for dilated cardiomyopathy (DCM).
Five DCM-related microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were used for identification. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, disease ontology, gene ontology annotation and protein-protein interaction (PPI) network analysis were then performed, while a random forest was constructed to explore central genes. Artificial neural networks were used to compare with known genes and to develop new diagnostic models. 240 population blood samples were collected and expression of hub genes was verified in these samples using RT-PCR and demonstrated by Nomogram.
After differential analysis, 33 genes were statistically significant (adjusted P < 0.05). Functional enrichment of these differential genes resulted in 85 Gene Ontology (GO) functions identified and 6 pathways enriched for the KEGG pathway. PPI networks and molecular complex assays identified 10 hub genes (adjusted P < 0.05). Random forest identified SMOC2 and SFRP4 as the most important, followed by FCER1G and FRZB. NeuraHF models (SMOC2, SFRP4, FCER1G and FRZB) were selected by artificial neural network model and had better diagnostic efficacy for the onset of DCM, compared with the traditional KG-DCM models (MYH7, ACTC1, TTN and LMNA). Finally, SFRP4 and FRZB were expressed higher in DCM verified by RT-PCR and as a factor for DCM identified by Nomogram.
We performed an integrated analysis and identified SFRP4 and FRZB as a new factor for DCM. But the exact mechanism still needs further experimental verification.
本研究旨在探讨扩张型心肌病（DCM）的潜在关键基因。
从基因表达综合数据库（GEO）中下载了 5 个与 DCM 相关的微阵列数据集。使用差异表达基因（DEG）进行鉴定。然后进行京都基因与基因组百科全书（KEGG）通路富集、疾病本体、基因本体注释和蛋白质-蛋白质相互作用（PPI）网络分析，同时构建随机森林以探索核心基因。使用人工神经网络与已知基因进行比较并开发新的诊断模型。收集了 240 个人群血液样本，并用 RT-PCR 在这些样本中验证了枢纽基因的表达，并通过Nomogram 进行了验证。
经过差异分析，有 33 个基因具有统计学意义（调整后 P<0.05）。这些差异基因的功能富集导致确定了 85 个基因本体（GO）功能和 6 个 KEGG 通路富集。PPI 网络和分子复合物检测确定了 10 个枢纽基因（调整后 P<0.05）。随机森林确定 SMOC2 和 SFRP4 为最重要的基因，其次是 FCER1G 和 FRZB。人工神经网络模型选择了 NeuraHF 模型（SMOC2、SFRP4、FCER1G 和 FRZB），与传统的 KG-DCM 模型（MYH7、ACTC1、TTN 和 LMNA）相比，对 DCM 的发病具有更好的诊断效果。最后，通过 RT-PCR 验证了 DCM 中 SFRP4 和 FRZB 的表达较高，并通过 Nomogram 确定了它们是 DCM 的一个因素。
我们进行了综合分析，确定 SFRP4 和 FRZB 是 DCM 的一个新因素。但确切的机制仍需要进一步的实验验证。
