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通过综合生物信息学分析预测主动脉瓣钙化相关关键基因

Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis.

作者信息

Wang Dinghui, Xiong Tianhua, Yu Wenlong, Liu Bin, Wang Jing, Xiao Kaihu, She Qiang

机构信息

Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.

出版信息

Front Genet. 2021 May 11;12:650213. doi: 10.3389/fgene.2021.650213. eCollection 2021.

DOI:10.3389/fgene.2021.650213
PMID:34046056
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8144713/
Abstract

Valvular heart disease is obtaining growing attention in the cardiovascular field and it is believed that calcific aortic valve disease (CAVD) is the most common valvular heart disease (VHD) in the world. CAVD does not have a fully effective treatment to delay its progression and the specific molecular mechanism of aortic valve calcification remains unclear. We obtained the gene expression datasets GSE12644 and GSE51472 from the public comprehensive free database GEO. Then, a series of bioinformatics methods, such as GO and KEGG analysis, STING online tool, Cytoscape software, were used to identify differentially expressed genes in CAVD and healthy controls, construct a PPI network, and then identify key genes. In addition, immune infiltration analysis was used via CIBERSORT to observe the expression of various immune cells in CAVD. A total of 144 differential expression genes were identified in the CAVD samples in comparison with the control samples, including 49 up-regulated genes and 95 down-regulated genes. GO analysis of DEGs were most observably enriched in the immune response, signal transduction, inflammatory response, proteolysis, innate immune response, and apoptotic process. The KEGG analysis revealed that the enrichment of DEGs in CAVD were remarkably observed in the chemokine signaling pathway, cytokine-cytokine receptor interaction, and PI3K-Akt signaling pathway. Chemokines CXCL13, CCL19, CCL8, CXCL8, CXCL16, MMP9, CCL18, CXCL5, VCAM1, and PPBP were identified as the hub genes of CAVD. It was macrophages that accounted for the maximal proportion among these immune cells. The expression of macrophages M0, B cells memory, and Plasma cells were higher in the CAVD valves than in healthy valves, however, the expression of B cells naïve, NK cells activated, and macrophages M2 were lower. We detected that chemokines CXCL13, CXCL8, CXCL16, and CXCL5, and CCL19, CCL8, and CCL18 are the most important markers of aortic valve disease. The regulatory macrophages M0, plasma cells, B cells memory, B cells naïve, NK cells activated, and macrophages M2 are probably related to the occurrence and the advancement of aortic valve stenosis. These identified chemokines and these immune cells may interact with a subtle adjustment relationship in the development of calcification in CAVD.

摘要

心脏瓣膜病在心血管领域日益受到关注,据信钙化性主动脉瓣疾病(CAVD)是全球最常见的心脏瓣膜病(VHD)。CAVD尚无完全有效的治疗方法来延缓其进展,主动脉瓣钙化的具体分子机制仍不清楚。我们从公共综合免费数据库GEO中获取了基因表达数据集GSE12644和GSE51472。然后,使用一系列生物信息学方法,如GO和KEGG分析、STING在线工具、Cytoscape软件,来识别CAVD和健康对照中的差异表达基因,构建蛋白质-蛋白质相互作用(PPI)网络,进而识别关键基因。此外,通过CIBERSORT进行免疫浸润分析,以观察CAVD中各种免疫细胞的表达。与对照样本相比,在CAVD样本中总共鉴定出144个差异表达基因,包括49个上调基因和95个下调基因。对差异表达基因的GO分析最显著地富集于免疫应答、信号转导、炎症应答、蛋白水解、固有免疫应答和凋亡过程。KEGG分析显示,CAVD中差异表达基因在趋化因子信号通路、细胞因子-细胞因子受体相互作用和PI3K-Akt信号通路中显著富集。趋化因子CXCL13、CCL19、CCL8、CXCL8、CXCL16、MMP9、CCL18、CXCL5、VCAM1和PPBP被鉴定为CAVD的枢纽基因。在这些免疫细胞中,巨噬细胞占比最大。CAVD瓣膜中巨噬细胞M0、记忆B细胞和浆细胞的表达高于健康瓣膜,然而,幼稚B细胞、活化NK细胞和巨噬细胞M2的表达较低。我们检测到趋化因子CXCL13、CXCL8、CXCL16和CXCL5以及CCL19、CCL8和CCL18是主动脉瓣疾病的最重要标志物。调节性巨噬细胞M0、浆细胞、记忆B细胞、幼稚B细胞、活化NK细胞和巨噬细胞M2可能与主动脉瓣狭窄的发生和进展有关。这些鉴定出的趋化因子和这些免疫细胞可能在CAVD钙化发展过程中以微妙的调节关系相互作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/989b/8144713/56049cb13ec9/fgene-12-650213-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/989b/8144713/56049cb13ec9/fgene-12-650213-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/989b/8144713/04027621314a/fgene-12-650213-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/989b/8144713/4b77706fa60f/fgene-12-650213-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/989b/8144713/35f518f143ed/fgene-12-650213-g003.jpg
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PeerJ. 2020 Aug 18;8:e9773. doi: 10.7717/peerj.9773. eCollection 2020.
2
Identification of Potential Hub Genes of Atherosclerosis Through Bioinformatic Analysis.通过生物信息学分析鉴定动脉粥样硬化的潜在枢纽基因。
J Comput Biol. 2021 Jan;28(1):60-78. doi: 10.1089/cmb.2019.0334. Epub 2020 Apr 15.
3
GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration.
血浆炎症蛋白在肠道微生物群驱动的瓣膜性心脏病中的中介作用:一项孟德尔随机化研究
Cell Biochem Biophys. 2025 May 28. doi: 10.1007/s12013-025-01780-9.
4
Causal association between triglycerides and cholesterol-lowering medication with non-rheumatic valve disease: A 2-sample Mendelian randomization study.三酰甘油与非风湿性瓣膜病患者降胆固醇药物之间的因果关系:两样本 Mendelian 随机研究。
Medicine (Baltimore). 2024 Jul 19;103(29):e38971. doi: 10.1097/MD.0000000000038971.
5
Identification of Diagnostic Genes of Aortic Stenosis That Progresses from Aortic Valve Sclerosis.从主动脉瓣硬化进展而来的主动脉瓣狭窄诊断基因的鉴定
J Inflamm Res. 2024 May 28;17:3459-3473. doi: 10.2147/JIR.S453100. eCollection 2024.
6
CircRNA/lncRNA-miRNA-mRNA network and gene landscape in calcific aortic valve disease.环状 RNA/长链非编码 RNA-微小 RNA-mRNA 网络与钙化性主动脉瓣疾病的基因全景。
BMC Genomics. 2023 Jul 25;24(1):419. doi: 10.1186/s12864-023-09441-y.
7
Macrophage DCLK1 promotes obesity-induced cardiomyopathy via activating RIP2/TAK1 signaling pathway.巨噬细胞 DCLK1 通过激活 RIP2/TAK1 信号通路促进肥胖诱导的心肌病。
Cell Death Dis. 2023 Jul 13;14(7):419. doi: 10.1038/s41419-023-05960-4.
8
Exploration and validation of the influence of angiogenesis-related factors in aortic valve calcification.血管生成相关因子在主动脉瓣钙化中作用的探索与验证
Front Cardiovasc Med. 2023 Feb 7;10:1061077. doi: 10.3389/fcvm.2023.1061077. eCollection 2023.
9
Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks.使用机器学习方法和人工神经网络开发并分析用于主动脉瓣钙化的综合诊断模型。
Front Cardiovasc Med. 2022 Dec 1;9:913776. doi: 10.3389/fcvm.2022.913776. eCollection 2022.
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Diagnostics (Basel). 2020 Mar 22;10(3):171. doi: 10.3390/diagnostics10030171.
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5
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Onco Targets Ther. 2019 Aug 16;12:6591-6604. doi: 10.2147/OTT.S218928. eCollection 2019.
6
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Sci Rep. 2019 Aug 21;9(1):12176. doi: 10.1038/s41598-019-48435-3.
7
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PeerJ. 2019 Jun 17;7:e7135. doi: 10.7717/peerj.7135. eCollection 2019.
8
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J Cell Physiol. 2020 Jan;235(1):394-407. doi: 10.1002/jcp.28980. Epub 2019 Jun 21.
9
Development of calcific aortic valve disease: Do we know enough for new clinical trials?钙化性主动脉瓣疾病的发生发展:我们对新临床试验的认识是否足够?
J Mol Cell Cardiol. 2019 Jul;132:189-209. doi: 10.1016/j.yjmcc.2019.05.016. Epub 2019 May 25.
10
CXCL13/CXCR5 signaling axis in cancer.趋化因子配体 13/趋化因子受体 5 信号轴在癌症中的作用。
Life Sci. 2019 Jun 15;227:175-186. doi: 10.1016/j.lfs.2019.04.053. Epub 2019 Apr 23.