Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis.
Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis.
复制标题
基于生物信息学分析鉴定肝纤维化中miRNA靶基因调控网络
作者：
Tai Y;Zhao C;Gao J;Lan T;Tong H
背景 肝硬化是全世界死亡的主要原因之一。 MicroRNA（miRNA）可以调节肝纤维化，但其潜在机制尚不完全清楚，miRNA与mRNA之间的相互作用也尚未明确阐明。方法从Gene Expression Omnibus数据库中获取肝硬化样本和对照样本的miRNA和mRNA表达阵列。进行miRNA-mRNA整合分析、功能富集分析和蛋白质-蛋白质相互作用（PPI）网络构建，以鉴定差异表达的miRNA（DEM）和mRNA（DEG）、miRNA-mRNA相互作用网络、富集通路和枢纽基因。最后，利用体外细胞模型验证了结果。结果通过生物信息学分析，我们在肝硬化样本和对照样本之间确定了 13 个 DEM。在这些 DEM 中，6 个上调（hsa-miR-146b-5p、hsa-miR-150-5p、hsa-miR-224-3p、hsa-miR-3135b、hsa-miR-3195 和 hsa-miR-4725-3p）和 7 个下调（hsa-miR-1234-3p、hsa-miR-30b-3p、 hsa-miR-3162-3p、hsa-miR-548aj-3p、hsa-miR-548x-3p、hsa-miR-548z 和 hsa-miR-890) miRNA 在激活的 LX2 细胞中得到进一步验证。 miRNA-mRNA 相互作用网络揭示了 13 个 miRNA 和 245 个相应靶基因之间总共 361 个 miRNA-mRNA 对。此外，PPI网络分析揭示了参与细胞外基质（ECM）组织的前20个枢纽基因，包括COL1A1、FBN1和TIMP3；参与免疫反应的CCL5、CXCL9、CXCL12、LCK和CD24； CDH1、PECAM1、SELL 和 CAV1 调节细胞粘附。所有 DEG 以及枢纽基因的功能富集分析显示出相似的结果，因为 ECM 相关途径、细胞表面相互作用和粘附以及免疫反应在两项分析中均显着富集。结论 我们鉴定了 13 个差异表达的 miRNA 作为肝硬化的潜在生物标志物。此外，我们还鉴定了肝硬化中的 361 个 miRNA-mRNA 调节对和 20 个枢纽基因，其中大部分与胶原蛋白和 ECM 成分、免疫反应和细胞粘附有关。这些结果将为肝硬化的发病机制提供新的机制见解，并确定其治疗的候选靶标。
Background Liver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated. Methods miRNA and mRNA expression arrays of cirrhotic samples and control samples were acquired from the Gene Expression Omnibus database. miRNA-mRNA integrated analysis, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs), miRNA-mRNA interaction networks, enriched pathways and hub genes. Finally, the results were validated with in vitro cell models. Results By bioinformatics analysis, we identified 13 DEMs between cirrhotic samples and control samples. Among these DEMs, six upregulated (hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-224-3p, hsa-miR-3135b, hsa-miR-3195, and hsa-miR-4725-3p) and seven downregulated (hsa-miR-1234-3p, hsa-miR-30b-3p, hsa-miR-3162-3p, hsa-miR-548aj-3p, hsa-miR-548x-3p, hsa-miR-548z, and hsa-miR-890) miRNAs were further validated in activated LX2 cells. miRNA-mRNA interaction networks revealed a total of 361 miRNA-mRNA pairs between 13 miRNAs and 245 corresponding target genes. Moreover, PPI network analysis revealed the top 20 hub genes, including COL1A1, FBN1 and TIMP3, which were involved in extracellular matrix (ECM) organization; CCL5, CXCL9, CXCL12, LCK and CD24, which participated in the immune response; and CDH1, PECAM1, SELL and CAV1, which regulated cell adhesion. Functional enrichment analysis of all DEGs as well as hub genes showed similar results, as ECM-associated pathways, cell surface interaction and adhesion, and immune response were significantly enriched in both analyses. Conclusions We identified 13 differentially expressed miRNAs as potential biomarkers of liver cirrhosis. Moreover, we identified 361 regulatory pairs of miRNA-mRNA and 20 hub genes in liver cirrhosis, most of which were involved in collagen and ECM components, immune response, and cell adhesion. These results would provide novel mechanistic insights into the pathogenesis of liver cirrhosis and identify candidate targets for its treatment.
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DOI：
10.1038/s41580-021-00354-w
发表时间：
2021-06
期刊：
Nature reviews. Molecular cell biology
影响因子：
--
作者：
Agbu P;Carthew RW
通讯作者：
Carthew RW
影响因子：
25.7
作者：
Asrani, Sumeet K.;Devarbhavi, Harshad;Kamath, Patrick S.
通讯作者：
Kamath, Patrick S.
影响因子：
4
作者：
Cabantous, Sandrine;Hou, Xunya;Dessein, Alain
通讯作者：
Dessein, Alain
影响因子：
13.5
作者：
Roderburg, Christoph;Urban, Gerd-Willem;Luedde, Tom
通讯作者：
Luedde, Tom
影响因子：
3.8
作者：
Ge, Shanfei;Zhang, Lunli;Xiang, Tianxing
通讯作者：
Xiang, Tianxing