Key genes and related pathways of hepatic fibrosis based on bioinformatics analysis

ZHANG La-ying; HE An-ling; CHEN Ming-kai

doi:10.11659/jjssx.03E023022

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Key genes and related pathways of hepatic fibrosis based on bioinformatics analysis

Basic Research | 更新时间：2024-02-02

- Key genes and related pathways of hepatic fibrosis based on bioinformatics analysis
- JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY Vol. 33, Issue 2, Pages: 106-111(2024)
- 作者机构：
  
  武汉大学人民医院消化内科，湖北武汉 430060
- 作者简介：
- 基金信息：
- DOI：10.11659/jjssx.03E023022
  CLC： R575.2
- Published：25 February 2024，
  
  Received：04 March 2023，
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张腊英,何安玲,陈明锴.基于生物信息学分析肝纤维化的关键基因及相关通路研究[J].局解手术学杂志,2024,33(02):106-111.

ZHANG La-ying,HE An-ling,CHEN Ming-kai.Key genes and related pathways of hepatic fibrosis based on bioinformatics analysis[J].JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY,2024,33(02):106-111.
张腊英,何安玲,陈明锴.基于生物信息学分析肝纤维化的关键基因及相关通路研究[J].局解手术学杂志,2024,33(02):106-111. DOI： 10.11659/jjssx.03E023022.

ZHANG La-ying,HE An-ling,CHEN Ming-kai.Key genes and related pathways of hepatic fibrosis based on bioinformatics analysis[J].JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY,2024,33(02):106-111. DOI： 10.11659/jjssx.03E023022.

摘要

目的

通过生物信息学分析筛选肝纤维化治疗的关键基因及相关通路，挖掘肝纤维化患者的差异表达基因，预测肝纤维化的潜在治疗靶点。

方法

从GEO数据库中获得基因表达谱GSE197112；通过Limma筛选差异表达基因；使用DAVID在线数据库对差异表达基因进行GO富集分析和KEGG信号通路富集分析；差异表达基因通过STRING数据库获取其蛋白-蛋白互作（PPI）网络图并用Cytoscape软件进行可视化，同时通过Cytoscape软件中的插件CytoHubba筛选肝纤维化靶点基因。

结果

该研究共筛选了399个差异表达基因（

0.01，|Log2FC|

1.5），包括300个下调基因和99个上调基因。这些基因主要参与了有丝分裂检验点、DNA复制、染色体分离、细胞分裂、细胞凋亡过程、适应性免疫应答等GO生物过程，主要调控产生IgA的肠道免疫网络、孕酮介导的卵母细胞成熟、人T细胞白血病病毒1感染、细胞周期、抗原处理和呈递、p53信号通路、癌症转录失调、细胞黏附分子等。通过Cytoscape软件筛选出5个靶点基因：TTK、KIF2C、ASPM、DLGAP5、PBK。

结论

本研究通过生物信息学的方法筛选出肝纤维化中399个差异表达基因和5个靶点基因，这些基因在肝纤维化相关的生物学过程中发挥了关键作用，为肝纤维化的药物治疗提供了新方向。

Abstract

Objective

By screening key genes and related pathways for hepatic fibrosis treatment through bioinformatics analysis， the differentially expressed genes of hepatic fibrosis patients were mined to predict potential therapeutic targets for liver fibrosis.

Methods

Gene expression profiles GSE197112 were obtained from GEO database. Differentially expressed genes were screened by Limma. DAVID online database was used to conduct GO enrichment analysis and KEGG signaling pathway enrichment analysis of differentially expressed genes. The protein-protein interaction （PPI） network diagram of differentially expressed genes were obtained from STRING database and

visualize by Cytoscape software. At the same time， the plug-in CytoHubba in Cytoscape software was used to screen the target genes of

hepatic fibrosis.

Results

A total of 399 differentially expressed genes were screened （

0.01， ∣log2FC∣

1.5）， including 300 down-regulated genes and 99 up-regulated genes. These genes were mainly involved in GO biological processes such as mitosis checkpoint， DNA replication， chromosome segregation， cell division， apoptosis， adaptive immune response and so on， and mainly regulated the intestinal immune network for IgA production， progesterone-mediated oocyte maturation， human T-cell leukemia virus 1 infection， cell cycle， antigen processing and presentation， p53 signaling pathway， cancer transcription disorder， cell adhesion molecules and so on. Five target genes were screened by Cytoscape software： TTK， KIF2C， ASPM， DLGAP5， PBK.

Conclusion

In this study， 399 differentially expressed genes and 5 target genes in hepatic fibrosis were screened by bioinformatics methods， which play key roles in the biological processes related to hepatic fibrosis， and provide a new direction for the pharmacological treatment of liver fibrosis.

关键词

生物信息学肝纤维化差异表达基因GEO数据库靶点

Keywords

bioinformaticshepatic fibrosisdifferentially expressed genesGEO databasetargets

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