使用综合生物信息学分析鉴定肝纤维化的关键基因，途径和

StephenW

PeerJ. 2019 Mar 22;7:e6645. doi: 10.7717/peerj.6645. eCollection 2019.
Identification of key genes, pathways and potential therapeutic agents for liver fibrosis using an integrated bioinformatics analysis.
Zhan Z1,2, Chen Y1,2, Duan Y1,2, Li L3, Mew K4, Hu P1,2, Ren H1,2, Peng M1,2.
Author information

1
    Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Chongqing Medical University, Chongqing, China.
2
    Department of Infectious Diseases, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
3
    Department of Hepatic Disease, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China.
4
    Department of Foreign Language, Chongqing Medical University, Chongqing, China.

Abstract
Background:

Liver fibrosis is often a consequence of chronic liver injury, and has the potential to progress to cirrhosis and liver cancer. Despite being an important human disease, there are currently no approved anti-fibrotic drugs. In this study, we aim to identify the key genes and pathways governing the pathophysiological processes of liver fibrosis, and to screen therapeutic anti-fibrotic agents.
Methods:

Expression profiles were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by R packages (Affy and limma). Gene functional enrichments of each dataset were performed on the DAVID database. Protein-protein interaction (PPI) network was constructed by STRING database and visualized in Cytoscape software. The hub genes were explored by the CytoHubba plugin app and validated in another GEO dataset and in a liver fibrosis cell model by quantitative real-time PCR assay. The Connectivity Map L1000 platform was used to identify potential anti-fibrotic agents.
Results:

We integrated three fibrosis datasets of different disease etiologies, incorporating a total of 70 severe (F3-F4) and 116 mild (F0-F1) fibrotic tissue samples. Gene functional enrichment analyses revealed that cell cycle was a pathway uniquely enriched in a dataset from those patients infected by hepatitis B virus (HBV), while the immune-inflammatory response was enriched in both the HBV and hepatitis C virus (HCV) datasets, but not in the nonalcoholic fatty liver disease (NAFLD) dataset. There was overlap between these three datasets; 185 total shared DEGs that were enriched for pathways associated with extracellular matrix constitution, platelet-derived growth-factor binding, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling. In the PPI network, 25 hub genes were extracted and deemed to be essential genes for fibrogenesis, and the expression trends were consistent with GSE14323 (an additional dataset) and liver fibrosis cell model, confirming the relevance of our findings. Among the 10 best matching anti-fibrotic agents, Zosuquidar and its corresponding gene target ABCB1 might be a novel anti-fibrotic agent or therapeutic target, but further work will be needed to verify its utility.
Conclusions:

Through this bioinformatics analysis, we identified that cell cycle is a pathway uniquely enriched in HBV related dataset and immune-inflammatory response is clearly enriched in the virus-related datasets. Zosuquidar and ABCB1 might be a novel anti-fibrotic agent or target.
KEYWORDS:

Bioinformatics; Liver cirrhosis; Microarray analysis; Therapeutics

PMID:
    30923657
PMCID:
    PMC6432904
DOI:
    10.7717/peerj.6645

StephenW

PeerJ。 2019年3月22日; 7：e6645。 doi：10.7717 / peerj.6645。 eCollection 2019。
使用综合生物信息学分析鉴定肝纤维化的关键基因，途径和潜在治疗剂。
詹Z1,2，陈Y1,2，段Y1,2，李L3，喵K4，胡P1,2，任H1,2，彭M1,2。
作者信息

1
    重庆医科大学传染病分子生物学教育部重点实验室，重庆
2
    重庆医科大学附属第二医院感染科，重庆
3
    重庆市中医医院肝病科，重庆，中国。
4
    重庆医科大学外语系，重庆，中国。

抽象
背景：

肝纤维化通常是慢性肝损伤的结果，并且有可能发展为肝硬化和肝癌。尽管是一种重要的人类疾病，但目前还没有批准的抗纤维化药物。在本研究中，我们的目标是确定控制肝纤维化病理生理过程的关键基因和途径，并筛选治疗性抗纤维化药物。
方法：

从Gene Expression Omnibus（GEO）下载表达谱，并通过R包（Affy和limma）鉴定差异表达的基因（DEG）。在DAVID数据库上进行每个数据集的基因功能富集。蛋白质 - 蛋白质相互作用（PPI）网络由STRING数据库构建，并在Cytoscape软件中可视化。通过CytoHubba插件应用程序探索中枢基因，并通过定量实时PCR测定在另一个GEO数据集和肝纤维化细胞模型中验证。连接图L1000平台用于鉴定潜在的抗纤维化剂。
结果：

我们整合了三种不同疾病病因的纤维化数据集，包括总共70种严重（F3-F4）和116种轻度（F0-F1）纤维化组织样本。基因功能富集分析显示细胞周期是一个独特的途径，来自那些感染乙型肝炎病毒（HBV）的患者的数据集，而免疫炎症反应在HBV和丙型肝炎病毒（HCV）数据集中都得到了丰富，但是不属于非酒精性脂肪性肝病（NAFLD）数据集。这三个数据集之间存在重叠;共有185个共享的DEGs，它们富含与细胞外基质构成，血小板衍生生长因子结合，蛋白质消化和吸收，粘着斑和PI3K-Akt信号传导相关的途径。在PPI网络中，提取了25个中枢基因并被认为是纤维发生的必需基因，并且表达趋势与GSE14323（另外的数据集）和肝纤维化细胞模型一致，证实了我们的发现的相关性。在10种最佳匹配的抗纤维化药物中，Zosuquidar及其相应的基因靶ABCB1可能是一种新型的抗纤维化药物或治疗靶点，但还需要进一步的工作来验证其效用。
结论：

通过这种生物信息学分析，我们发现细胞周期是独特地富集HBV相关数据集的途径，并且免疫炎症反应在病毒相关数据集中明显丰富。 Zosuquidar和ABCB1可能是一种新型的抗纤维化剂或靶标。
关键词：

生物信息学;肝硬化;微阵列分析;疗法

结论：
    30923657
PMCID：
    PMC6432904
DOI：
    10.7717 / peerj.6645

StephenW

https://peerj.com/articles/6645.pdf