Ding J X, Huang W B, Jiang X X, Zhang L D, Fang H, Jin J
Department of Infectious Diseases, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
Zhonghua Gan Zang Bing Za Zhi. 2022 Mar 20;30(3):297-303. doi: 10.3760/cma.j.cn501113-20210525-00251.
To screen and analyze the key differentially expressed genes characteristics in nonalcoholic fatty liver disease (NAFLD) with bioinformatics method. NAFLD-related expression matrix GSE89632 was downloaded from the GEO database. Limma package was used to screen differentially expressed genes (DEGs) in healthy, steatosis (SS), and nonalcoholic steatohepatitis (NASH) samples. WGCNA was used to analyze the output gene module. The intersection of module genes and differential genes was used to determine the differential genes characteristic, and then GO function and KEGG signaling pathway enrichment analysis were performed. The protein-protein interaction network (PPI) was constructed using the online website STRING and Cytoscape software, and the key (Hub) genes were screened. Finally, R software was used to analyze the receiver operating characteristic curve (ROC) of the Hub gene. 92 differentially expressed genes characteristic were obtained through screening, which were mainly enriched in inflammatory response-related functions of "lipopolysaccharide response and molecular response of bacterial origin", as well as cancer signaling pathways of "proteoglycan in cancer" and "T-cell leukemia virus infection-related". 10 hub genes (FOS, CXCL8, SERPINE1, CYR61, THBS1, FOSL1, CCL2, MYC, SOCS3 and ATF3) had good diagnostic value. The differentially expressed hub genes among the 10 NAFLD disease-related characteristics obtained with bioinformatics analysis may become a diagnostic and prognostic marker and potential therapeutic target for NAFLD. However, further basic and clinical studies are needed to validate.
采用生物信息学方法筛选和分析非酒精性脂肪性肝病（NAFLD）中关键的差异表达基因特征。从GEO数据库下载NAFLD相关的表达矩阵GSE89632。使用Limma软件包筛选健康、脂肪变性（SS）和非酒精性脂肪性肝炎（NASH）样本中的差异表达基因（DEG）。运用加权基因共表达网络分析（WGCNA）对输出的基因模块进行分析。通过模块基因与差异基因的交集确定差异基因特征，随后进行基因本体（GO）功能和京都基因与基因组百科全书（KEGG）信号通路富集分析。利用在线网站STRING和Cytoscape软件构建蛋白质-蛋白质相互作用网络（PPI），并筛选关键（Hub）基因。最后，使用R软件分析Hub基因的受试者工作特征曲线（ROC）。通过筛选获得了92个差异表达基因特征，这些特征主要富集于“脂多糖反应和细菌来源的分子反应”等炎症反应相关功能，以及“癌症中的蛋白聚糖”和“T细胞白血病病毒感染相关”等癌症信号通路。10个Hub基因（FOS、CXCL8、SERPINE1、CYR61、THBS1、FOSL1、CCL2、MYC、SOCS3和ATF3）具有良好的诊断价值。通过生物信息学分析获得的10个与NAFLD疾病相关特征中的差异表达Hub基因可能成为NAFLD的诊断和预后标志物以及潜在的治疗靶点。然而，还需要进一步的基础和临床研究来验证。
