The use of multiple datasets to identify autophagy-related molecular mechanisms in intracerebral hemorrhage.
The use of multiple datasets to identify autophagy-related molecular mechanisms in intracerebral hemorrhage.
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使用多个数据集来识别脑出血中自噬相关的分子机制。
DOI：
10.3389/fgene.2023.1032639
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发表时间：
2023
影响因子：
3.7
通讯作者：
Huang, Tianfeng
中科院分区：
文献类型：
--
作者：
Xiao, Yinggang;Zhang, Yang;Wang, Cunjin;Ge, Yali;Gao, Ju;Huang, Tianfeng
背景资料：脑出血是一种致死率和致残率极高的脑卒中综合征，但自噬在脑出血中的作用机制尚不清楚。本研究通过生物信息学方法鉴定了脑出血自噬的关键基因，并探讨了其作用机制。 方法：我们从基因表达综合数据库（GEO）下载ICH患者芯片数据。基于GENE数据库，鉴定了自噬的差异表达基因（DEG）。我们通过蛋白质-蛋白质相互作用（PPI）网络分析确定了关键基因，并在基因本体论（GO）和京都基因和基因组百科全书（KEGG）中分析了其相关通路。利用基因基序排序、miRWalk和ENCORI数据库分析关键基因转录因子（TF）调控网络和ceRNA网络。最后，通过基因集富集分析（GSEA）获得相关的靶向通路。 结果如下：共获得11个脑出血自噬相关DEG，通过PPI和受试者工作特征（ROC）曲线分析，确定IL-1B、STAT 3、NLRP 3和NOD 2为具有临床预测价值的关键基因。候选基因表达水平与免疫浸润水平显著相关，多数关键基因与免疫细胞浸润水平呈正相关。关键基因主要与细胞因子和受体相互作用、免疫应答等途径有关。ceRNA网络预测了8，654个相互作用对（24个miRNA和2，952个lncRNA）。 结论：我们利用多个生物信息学数据集筛选出IL-1B、STAT 3、NLRP 3和NOD 2为脑出血发病的关键基因。
Background: Intracerebral hemorrhage (ICH) is a stroke syndrome with high mortality and disability rates, but autophagy’s mechanism in ICH is still unclear. We identified key autophagy genes in ICH by bioinformatics methods and explored their mechanisms. Methods: We downloaded ICH patient chip data from the Gene Expression Omnibus (GEO) database. Based on the GENE database, differentially expressed genes (DEGs) for autophagy were identified. We identified key genes through protein–protein interaction (PPI) network analysis and analyzed their associated pathways in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene-motif rankings, miRWalk and ENCORI databases were used to analyze the key gene transcription factor (TF) regulatory network and ceRNA network. Finally, relevant target pathways were obtained by gene set enrichment analysis (GSEA). Results: Eleven autophagy-related DEGs in ICH were obtained, and IL-1B, STAT3, NLRP3 and NOD2 were identified as key genes with clinical predictive value by PPI and receiver operating characteristic (ROC) curve analysis. The candidate gene expression level was significantly correlated with the immune infiltration level, and most of the key genes were positively correlated with the immune cell infiltration level. The key genes are mainly related to cytokine and receptor interactions, immune responses and other pathways. The ceRNA network predicted 8,654 interaction pairs (24 miRNAs and 2,952 lncRNAs). Conclusion: We used multiple bioinformatics datasets to identify IL-1B, STAT3, NLRP3 and NOD2 as key genes that contribute to the development of ICH.
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影响因子：
29
作者：
Biffi A;Bailey D;Anderson CD;Ayres AM;Gurol EM;Greenberg SM;Rosand J;Viswanathan A
通讯作者：
Viswanathan A
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
3.4
作者：
Ji XC;Shi YJ;Zhang Y;Chang MZ;Zhao G
通讯作者：
Zhao G
影响因子：
2.9
作者：
Huang, Jingyang;Liu, Baoyi;Yuan, Zhongrui
通讯作者：
Yuan, Zhongrui
影响因子：
6.3
作者：
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通讯作者：
Stamova, Boryana