背景:非酒精性脂肪性肝病(NAFLD)是慢性肝病的普遍原因,包括广泛的疾病,包括简单的脂肪变性,脂肪性肝炎,纤维化,肝硬化,还有肝癌.然而,由于NAFLD的全球流行,侵入性肝活检是诊断的黄金标准,有必要为NAFLD的早期诊断确定一种更实用的方法,并具有有用的治疗靶标;因此,分子生物标志物最容易达到这些目的。为此,我们探讨了NAFLD患者纤维化进展的hub基因和生物学通路.
方法:从基因表达Omnibus数据库下载来自具有GEO登录号GSE49541的微阵列芯片的原始数据,并应用R包(Affy和Limma)研究低(轻度0-1纤维化评分)至高(重度3-4纤维化评分)纤维化阶段NAFLD患者进展中涉及的差异表达基因(DEGs)。随后,分析了具有途径富集的显著DEGs,包括基因本体论(GO),KEGG和Wikipathway。为了探索关键基因,使用STRING数据库建立并可视化了蛋白质-蛋白质相互作用网络(PPI),使用Cytoscape和Gephi软件进行进一步分析。进行生存分析以确定在NAFLD进展为肝细胞癌的过程中hub基因的总生存。
结果:共鉴定出311个显著基因,其中278的表达上调,33的表达下调低群。这些重要基因的基因功能富集分析表明主要参与细胞外基质(ECM)-受体相互作用,蛋白质消化吸收,和AGE-RAGE信号通路。PPI网络由196个节点和572条边构成,使用p值<1.0e-16进行PPI富集。基于这个截止,我们确定了12个基因在四个中心性得分最高:Betweenness,亲近,和特征向量。这十二个hub基因是CD34,THY1,CFTR,COL3A1,COL1A1,COL1A2,SPP1,THBS1,THBS2,LUM,VCAN,和VWF。其中四个枢纽基因,即CD34,VWF,SPP1和VCAN,与肝细胞癌的发生发展显著相关。
结论:这种对DEGs的PPI网络分析确定了与纤维化进展有关的关键hub基因及其在NAFLD患者中发挥作用的生物学途径。这12个基因为进一步集中研究以确定治疗应用的潜在靶标提供了极好的机会。
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a prevalent cause of chronic liver disease and encompasses a broad spectrum of disorders, including simple steatosis, steatohepatitis, fibrosis, cirrhosis, and liver cancer. However, due to the global epidemic of NAFLD, where invasive liver biopsy is the gold standard for diagnosis, it is necessary to identify a more practical method for early NAFLD diagnosis with useful therapeutic targets; as such, molecular biomarkers could most readily serve these aims. To this end, we explored the hub genes and biological pathways in fibrosis progression in NAFLD patients.
METHODS: Raw data from microarray chips with GEO accession GSE49541 were downloaded from the Gene Expression Omnibus database, and the R package (Affy and Limma) was applied to investigate differentially expressed genes (DEGs) involved in the progress of low- (mild 0-1 fibrosis score) to high- (severe 3-4 fibrosis score) fibrosis stage NAFLD patients. Subsequently, significant DEGs with pathway enrichment were analyzed, including gene ontology (GO), KEGG and Wikipathway. In order to then explore critical genes, the protein-protein interaction network (PPI) was established and visualized using the STRING database, with further analysis undertaken using Cytoscape and Gephi software. Survival analysis was undertaken to determine the overall survival of the hub genes in the progression of NAFLD to hepatocellular carcinoma.
RESULTS: A total of 311 significant genes were identified, with an expression of 278 being upregulated and 33 downregulated in the high vs. low group. Gene functional enrichment analysis of these significant genes demonstrated major involvement in extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, and the AGE-RAGE signaling pathway. The PPI network was constructed with 196 nodes and 572 edges with PPI enrichment using a p-value < 1.0 e-16. Based on this cut-off, we identified 12 genes with the highest score in four centralities: Degree, Betweenness, Closeness, and Eigenvector. Those twelve hub genes were CD34, THY1, CFTR, COL3A1, COL1A1, COL1A2, SPP1, THBS1, THBS2, LUM, VCAN, and VWF. Four of these hub genes, namely CD34, VWF, SPP1, and VCAN, showed significant association with the development of hepatocellular carcinoma.
CONCLUSIONS: This PPI network analysis of DEGs identified critical hub genes involved in the progression of fibrosis and the biological pathways through which they exert their effects in NAFLD patients. Those 12 genes offer an excellent opportunity for further focused research to determine potential targets for therapeutic applications.