通过微阵列数据集的综合分析确定巨结肠疾病潜在的转录调控网络。

IF 0.8 4区 医学 Q4 PEDIATRICS World Journal of Pediatric Surgery Pub Date : 2023-01-01 DOI:10.1136/wjps-2022-000547
Wenyao Xu, Hui Yu, Dian Chen, Weikang Pan, Weili Yang, Jing Miao, Wanying Jia, Baijun Zheng, Yong Liu, Xinlin Chen, Ya Gao, Donghao Tian
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引用次数: 0

摘要

目的:巨结肠病(Hirschsprung disease, HSCR)是儿童常见的神经系统疾病之一,与至少20个基因相关,涉及复杂的调控机制。转录调控网络(TRN)在调节基因表达和肠神经系统发育方面已被广泛报道,但在HSCR中仍有待研究。本研究旨在确定与HSCR发病机制和诊断有关的潜在TRN。方法:基于来自基因表达Omnibus数据库的三个微阵列数据集,使用multiMiR包研究microRNA (miRNA)-靶标相互作用,然后进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析。然后,我们从TransmiR数据库中收集转录因子(tf),构建TF-miRNA-mRNA调控网络,并使用cytoHubba识别关键模块。最后,通过支持向量机方法确定受试者工作特征(ROC)曲线,建立基于机器学习的综合诊断模型。结果:我们鉴定了58个中心差异表达microRNAs (DEMis)和16个差异表达mrna (DEMs)。DEMis和DEMs的强大靶基因主要富集于GO/KEGG的几个方面,包括神经发生、细胞底物粘附、PI3K-Akt、Ras/丝裂原活化蛋白激酶和Rho/ROCK信号。此外,鉴定出2个tf (TP53和TWIST1)、4个mirna (has-miR-107、has-miR-10b-5p、has-miR-659-3p和has-miR-371a-5p)和4个mrna (PIM3、CHUK、F2RL1和CA1)来构建TF-miRNA-mRNA调控网络。ROC分析显示关键TRN调控具有较强的诊断价值(曲线下面积均大于0.8)。结论:本研究提示TF-miRNA-mRNA网络的潜在作用,有助于丰富HSCR发病和诊断的内涵,为治疗提供新的视野。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets.

Objective: Hirschsprung disease (HSCR) is one of the common neurocristopathies in children, which is associated with at least 20 genes and involves a complex regulatory mechanism. Transcriptional regulatory network (TRN) has been commonly reported in regulating gene expression and enteric nervous system development but remains to be investigated in HSCR. This study aimed to identify the potential TRN implicated in the pathogenesis and diagnosis of HSCR.

Methods: Based on three microarray datasets from the Gene Expression Omnibus database, the multiMiR package was used to investigate the microRNA (miRNA)-target interactions, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Then, we collected transcription factors (TFs) from the TransmiR database to construct the TF-miRNA-mRNA regulatory network and used cytoHubba to identify the key modules. Finally, the receiver operating characteristic (ROC) curve was determined and the integrated diagnostic models were established based on machine learning by the support vector machine method.

Results: We identified 58 hub differentially expressed microRNAs (DEMis) and 16 differentially expressed mRNAs (DEMs). The robust target genes of DEMis and DEMs mainly enriched in several GO/KEGG terms, including neurogenesis, cell-substrate adhesion, PI3K-Akt, Ras/mitogen-activated protein kinase and Rho/ROCK signaling. Moreover, 2 TFs (TP53 and TWIST1), 4 miRNAs (has-miR-107, has-miR-10b-5p, has-miR-659-3p, and has-miR-371a-5p), and 4 mRNAs (PIM3, CHUK, F2RL1, and CA1) were identified to construct the TF-miRNA-mRNA regulatory network. ROC analysis revealed a strong diagnostic value of the key TRN regulons (all area under the curve values were more than 0.8).

Conclusion: This study suggests a potential role of the TF-miRNA-mRNA network that can help enrich the connotation of HSCR pathogenesis and diagnosis and provide new horizons for treatment.

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来源期刊
CiteScore
1.40
自引率
12.50%
发文量
38
审稿时长
13 weeks
期刊最新文献
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