Zhan Zhu, Chen Yuhe, Duan Yuanqin, Li Lin, Mew Kenley, Hu Peng, Ren Hong, Peng Mingli
Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Chongqing Medical University, Chongqing, China.
Department of Infectious Diseases, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
PeerJ. 2019 Mar 22;7:e6645. doi: 10.7717/peerj.6645. eCollection 2019.
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.
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.
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.
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.
肝纤维化通常是慢性肝损伤的结果，有发展为肝硬化和肝癌的潜在风险。尽管这是一种重要的人类疾病，但目前尚无获批的抗纤维化药物。在本研究中，我们旨在确定调控肝纤维化病理生理过程的关键基因和通路，并筛选具有治疗作用的抗纤维化药物。
从基因表达综合数据库（GEO）下载表达谱，使用R包（Affy和limma）鉴定差异表达基因（DEG）。在DAVID数据库上对每个数据集进行基因功能富集分析。通过STRING数据库构建蛋白质-蛋白质相互作用（PPI）网络，并在Cytoscape软件中进行可视化。通过CytoHubba插件应用探索枢纽基因，并在另一个GEO数据集和肝纤维化细胞模型中通过定量实时PCR分析进行验证。使用连通性图谱L1000平台鉴定潜在的抗纤维化药物。
我们整合了三个病因不同的纤维化数据集，共纳入70个重度（F3-F4）和116个轻度（F0-F1）纤维化组织样本。基因功能富集分析显示，细胞周期是在乙型肝炎病毒（HBV）感染患者的数据集中唯一富集的通路，而免疫炎症反应在HBV和丙型肝炎病毒（HCV）数据集中均有富集，但在非酒精性脂肪性肝病（NAFLD）数据集中未富集。这三个数据集之间存在重叠；共有185个共享的DEG，这些基因富集于与细胞外基质组成、血小板衍生生长因子结合、蛋白质消化和吸收、粘着斑以及PI3K-Akt信号传导相关的通路。在PPI网络中，提取了25个枢纽基因，被认为是纤维化形成的关键基因，其表达趋势与GSE14323（另一个数据集）和肝纤维化细胞模型一致，证实了我们研究结果的相关性。在10种最匹配的抗纤维化药物中，唑磺达及其相应的基因靶点ABCB1可能是一种新型抗纤维化药物或治疗靶点，但需要进一步研究来验证其效用。
通过这项生物信息学分析，我们确定细胞周期是HBV相关数据集中唯一富集的通路，免疫炎症反应在病毒相关数据集中明显富集。唑磺达和ABCB1可能是一种新型抗纤维化药物或靶点。
