基于基因共表达网络的系统筛选挖掘潜在的瓣膜性心房颤动（VAF）生物标志物和通路。
Mining of Potential Biomarkers and Pathway in Valvular Atrial Fibrillation (VAF) via Systematic Screening of Gene Coexpression Network.
机构信息
Department of Cardiovascular Surgery, The Third Affiliated Hospital of Sun Yat-sen University, No. 600, Tianhe Road, Tianhe District, Guangzhou City, Guangdong Province, China.
Department of Hematopathology, The First Affiliated Hospital of Jinan University, Tianhe District, Guangzhou City, Guangdong Province, China.
出版信息
Comput Math Methods Med. 2022 Oct 3;2022:3645402. doi: 10.1155/2022/3645402. eCollection 2022.
PURPOSE
We apply the bioinformatics method to excavate the potential genes and therapeutic targets associated with valvular atrial fibrillation (VAF).
METHODS
The downloaded gene expression files from the gene expression omnibus (GEO) included patients with primary severe mitral regurgitation complicated with sinus or atrial fibrillation rhythm. Subsequently, the differential gene expression in left and right atrium was analyzed by R software. Additionally, weighted correlation network analysis (WGCNA), principal component analysis (PCA), and linear model for microarray data (LIMMA) algorithm were used to determine hub genes. Then, Metascape database, DAVID database, and STRING database were used to annotate and visualize the gene ontology (GO) analysis, KEGG pathway enrichment analysis, and PPI network analysis of differentially expressed genes (DEGs). Finally, the TFs and miRNAs were predicted by using online tools, such as PASTAA and miRDB.
RESULTS
20,484 differentially expressed genes related to atrial fibrillation were obtained through the analysis of left and right atrial tissue samples of GSE115574 gene chip, and 1,009 were with statistical significance, including 45 upregulated genes and 964 downregulated genes. And the hub genes implicated in AF of NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B were screened. Finally, the main regulators of targeted candidate biomarkers and microRNAs, EIF5A2, HIF1A, ZIC2, ELF1, and STAT2, were found in this study.
CONCLUSION
These hub genes, NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B, are important for the development of VAF, and their enrichment pathways and TFs elucidate the involved molecular mechanisms and assist in the validation of drug targets.
目的
我们应用生物信息学方法挖掘与瓣心房颤动（VAF）相关的潜在基因和治疗靶点。
方法
从基因表达综合数据库（GEO）中下载包含原发性严重二尖瓣反流伴窦性或房性颤动节律患者的基因表达文件。然后，使用 R 软件分析左、右心房的差异基因表达。此外，采用加权相关网络分析（WGCNA）、主成分分析（PCA）和线性模型微阵列数据（LIMMA）算法确定关键基因。然后，使用 Metascape 数据库、DAVID 数据库和 STRING 数据库对差异表达基因（DEGs）的基因本体（GO）分析、KEGG 通路富集分析和 PPI 网络分析进行注释和可视化。最后，使用在线工具，如 PASTAA 和 miRDB，预测 TFs 和 miRNAs。
结果
通过分析 GSE115574 基因芯片左、右心房组织样本，获得 20484 个与心房颤动相关的差异表达基因，其中 1009 个基因具有统计学意义，包括 45 个上调基因和 964 个下调基因。筛选出 NPC2、ODC1、SNAP29、LAPTM5、ST8SIA5 和 FCGR3B 等与 AF 相关的关键基因。最后，在本研究中发现靶向候选生物标志物和 microRNAs 的主要调控因子 EIF5A2、HIF1A、ZIC2、ELF1 和 STAT2。
结论
这些关键基因 NPC2、ODC1、SNAP29、LAPTM5、ST8SIA5 和 FCGR3B 对 VAF 的发展具有重要意义，其富集通路和 TFs 阐明了涉及的分子机制，并有助于验证药物靶点。
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