综合生物信息学分析鉴定心力衰竭的关键基因和生物学途径。

The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis.

机构信息

Department of Geriatrics, Minhang Hospital, Fudan University, 170 Xinsong Road, Shanghai 201199, China.

Department of Cardiology, Minhang Hospital, Fudan University, 170 Xinsong Road, Shanghai 201199, China.

出版信息

Comput Math Methods Med. 2021 Nov 26;2021:3859338. doi: 10.1155/2021/3859338. eCollection 2021.

DOI:10.1155/2021/3859338

PMID:34868339

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8642006/

Abstract

PURPOSE

Heart failure (HF) is a clinical syndrome caused by ventricular insufficiency. In order to further explore the biomarkers related to HF, we apply the high-throughput database.

MATERIALS AND METHODS

The GSE21610 was applied for the differentially expressed gene (DEG) analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The Gene Set Enrichment Analysis (GSEA) was used for gene expression profile GSE21610. The Protein-Protein Interaction (PPI) network and modules were also constructed for research. These hub gene function pathways were estimated in HF progression.

RESULT

We have identified 434 DEGs in total, including 304 downregulated DEGs and 130 upregulated DEGs. GO and KEGG illustrated that DEGs in HF were significantly enriched in G protein-coupled receptor binding, peroxisome, and cAMP signaling pathway. GSEA results showed gene set GSE21610 was gathered in lipid digestion, defense response to fungus, and intestinal lipid absorption. Finally, through analyzing the PPI network, we screened hub genes CDH1, TFRC, CCL2, BUB1B, and CD19 by the Cytoscape software.

CONCLUSION

This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to HF. These analysis results provide us with new ideas for finding biomarkers and treatment methods for HF.

摘要

目的

心力衰竭（HF）是一种由心室功能不全引起的临床综合征。为了进一步探讨与 HF 相关的生物标志物，我们应用了高通量数据库。

材料与方法

应用 GSE21610 进行差异表达基因（DEG）分析。采用数据库检索、可视化和综合发现（DAVID）进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析。采用基因集富集分析（GSEA）对基因表达谱 GSE21610 进行分析。还构建了蛋白质-蛋白质相互作用（PPI）网络和模块，以研究这些关键基因在 HF 进展中的功能途径。

结果

我们总共鉴定出 434 个 DEG，包括 304 个下调 DEG 和 130 个上调 DEG。GO 和 KEGG 表明，HF 中的 DEG 显著富集于 G 蛋白偶联受体结合、过氧化物酶体和 cAMP 信号通路。GSEA 结果表明，基因集 GSE21610 聚集在脂质消化、对真菌的防御反应和肠道脂质吸收中。最后，通过分析 PPI 网络，我们使用 Cytoscape 软件筛选出与 HF 相关的关键基因 CDH1、TFRC、CCL2、BUB1B 和 CD19。