Jin Feng, Tianjiao Wei, Xiaona Cui, Rui Wei, Tianpei Hong
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The Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database were used to perform the enrichment analysis and construct protein–protein interaction (PPI) networks, respectively. Subsequently, transcription factor networks and key modules were identified. The hub genes were validated in a mice model of high fat diet (HFD)-induced NAFLD and in cultured HepG2 cells by real-time quantitative PCR.</p></div><div><h3>Results</h3><p>Based on the GSE49541 dataset, 57 DEGs were selected and enriched in chemokine activity and cellular component, including the extracellular region. Twelve transcription factors associated with DEGs were indicated from PPI analysis. Upregulated expression of five hub genes (<em>SOX9</em>, <em>CCL20</em>, <em>CXCL1</em>, <em>CD24</em>, and <em>CHST4</em>), which were identified from the dataset, was also observed in the livers of HFD-induced NAFLD mice and in HepG2 cells exposed to palmitic acid or advanced glycation end products.</p></div><div><h3>Conclusion</h3><p>The hub genes <em>SOX9</em>, <em>CCL20</em>, <em>CXCL1</em>, <em>CD24</em>, and <em>CHST4</em> are involved in the aggravation of NAFLD. 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引用次数: 7
摘要
背景非酒精性脂肪性肝病(NAFLD)的全球患病率正在上升。NAFLD的发病机制是多方面的,其潜在机制尚不明确。我们进行了数据挖掘分析,以更好地了解该疾病,并确定与NAFLD进展相关的中心基因。方法从Gene Expression Omnibus数据库中获取数据集GSE49541,其中包含40例轻度NAFLD样本和32例晚期NAFLD样本。差异表达基因(DEGs)用R编程语言进行鉴定。利用Database for Annotation, Visualization and Integrated Discovery (DAVID)在线工具和Search tool for Retrieval of Interacting Genes/Proteins (STRING)数据库分别进行富集分析和构建蛋白-蛋白相互作用(PPI)网络。随后,转录因子网络和关键模块被确定。通过实时定量PCR在高脂饮食(HFD)诱导的NAFLD小鼠模型和培养的HepG2细胞中验证了枢纽基因。结果基于GSE49541数据集,选择了57个基因,并富集了趋化因子活性和细胞成分,包括细胞外区域。PPI分析显示12个与deg相关的转录因子。从数据集中鉴定出的5个中心基因(SOX9、CCL20、CXCL1、CD24和CHST4)的表达上调也在hfd诱导的NAFLD小鼠的肝脏和暴露于棕榈酸或晚期糖基化终产物的HepG2细胞中观察到。结论中枢基因SOX9、CCL20、CXCL1、CD24、CHST4参与了NAFLD的加重。我们的研究结果为NAFLD进展的潜在机制提供了新的见解。
Identification of key genes and pathways in mild and severe nonalcoholic fatty liver disease by integrative analysis
Background
The global prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing. The pathogenesis of NAFLD is multifaceted, and the underlying mechanisms are elusive. We conducted data mining analysis to gain a better insight into the disease and to identify the hub genes associated with the progression of NAFLD.
Methods
The dataset GSE49541, containing the profile of 40 samples representing mild stages of NAFLD and 32 samples representing advanced stages of NAFLD, was acquired from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the R programming language. The Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database were used to perform the enrichment analysis and construct protein–protein interaction (PPI) networks, respectively. Subsequently, transcription factor networks and key modules were identified. The hub genes were validated in a mice model of high fat diet (HFD)-induced NAFLD and in cultured HepG2 cells by real-time quantitative PCR.
Results
Based on the GSE49541 dataset, 57 DEGs were selected and enriched in chemokine activity and cellular component, including the extracellular region. Twelve transcription factors associated with DEGs were indicated from PPI analysis. Upregulated expression of five hub genes (SOX9, CCL20, CXCL1, CD24, and CHST4), which were identified from the dataset, was also observed in the livers of HFD-induced NAFLD mice and in HepG2 cells exposed to palmitic acid or advanced glycation end products.
Conclusion
The hub genes SOX9, CCL20, CXCL1, CD24, and CHST4 are involved in the aggravation of NAFLD. Our results offer new insights into the underlying mechanism of NAFLD progression.
期刊介绍:
This journal aims to promote progress from basic research to clinical practice and to provide a forum for communication among basic, translational, and clinical research practitioners and physicians from all relevant disciplines. Chronic diseases such as cardiovascular diseases, cancer, diabetes, stroke, chronic respiratory diseases (such as asthma and COPD), chronic kidney diseases, and related translational research. Topics of interest for Chronic Diseases and Translational Medicine include Research and commentary on models of chronic diseases with significant implications for disease diagnosis and treatment Investigative studies of human biology with an emphasis on disease Perspectives and reviews on research topics that discuss the implications of findings from the viewpoints of basic science and clinical practic.