基于基因表达数据库分析非酒精性脂肪性肝病炎症损伤相关核心基因  

Analysis of hub genes associated with inflammation injury in non-alcoholic fatty liver disease based on gene expression omnibus database

作　　者：程呈 卢帅 陈蓉 李新萍 白睿峰 崔更力 陈烁 殷家伟 胡建鹏 汪垚卓 蒋协远 陈海翎 Cheng Cheng;Lu Shuai;Chen Rong;Li Xinping;Bai Ruifeng;Cui Gengli;Chen Shuo;Yin Jiawei;Hu Jianpeng;Wang Yaozhuo;Jiang Xieyuan;Chen Hailing(Department of Osteoporosis,2 Department of Trauma Orthopedics,3 Department of Clinical Laboratory,Beijing Jishuitan Hospital,Capital Medical University,Beijing 100035,China)

机构地区：[1]首都医科大学附属北京积水潭医院骨质疏松科,100035 [2]首都医科大学附属北京积水潭医院创伤骨科,100035 [3]首都医科大学附属北京积水潭医院检验科,100035

出　　处：《中华损伤与修复杂志(电子版)》2025年第2期99-106,共8页Chinese Journal of Injury Repair and Wound Healing(Electronic Edition)

基　　金：国家重点研发计划(2024YFC3044700);首都医科大学附属北京积水潭医院科研优才计划(KYYC202301);北京市卫生健康委员会第五批北京市属医学科研院所公益发展改革试点项目(京医研2023-8)。

摘　　要：目的探究在非酒精性脂肪性肝病(NAFLD)发展过程中发生显著变化的炎症损伤相关核心基因。方法从基因表达数据库(GEO)的GSE167523数据集中选择98例NAFLD患者,通过基因集变异分析(GSVA)计算炎症指数。设置高炎症指数和低炎症指数两组进行比较。使用R语言limma包识别组间差异表达基因(DEGs)。应用R语言ClusterProfiler包对DEGs进行基因本体(GO)富集分析及京都基因与基因组百科全书(KEGG)富集分析。采用STRING数据库进行蛋白-蛋白相互作用(PPI)网络分析,确定核心基因。同时,在数据集GSE89632和GSE130970中进行基因表达量的验证。使用ROC曲线评价其诊断能力并进行miRNA和转录因子调控网络分析。结果共鉴定出308个DEGs,其中上调基因243个,下调基因65个。富集分析结果显示,DEGs主要富集在趋化因子信号通路、病毒蛋白与细胞因子和细胞因子受体的相互作用等途径中。通过对DEGs进行PPI网络分析,发现TOP2A、MKI67、CDC20、DLGAP5、MCM10等16个核心基因。ROC曲线分析提示这16个核心基因是NAFLD的潜在生物标志物。其中,11个核心基因的表达水平在数据集GSE89632的对照组和NAFLD组,以及数据集GSE130970的高炎症指数组和低炎症指数组之间差异均有统计学意义(P<0.05)。结论TOP2A、CDC20、HJURP等炎症损伤相关核心基因有望成为NAFLD诊断与治疗的潜在靶点。Objective To explore the hub genes related to inflammation injury that changed significantly during the development of non-alcoholic fatty liver disease(NAFLD).Methods A total of 98 patients with NAFLD were selected from the GSE167523 dataset in the Gene Expression Omnibus(GEO)database to calculate the inflammatory indices by performing the gene set variation analysis(GSVA).Two separate groups of high-inflammatory indices and low-inflammatory indices were set up for comparison purposes.Using the limma software package in R language to identify differentially expressed genes(DEGs)between the groups.ClusterProfile software package in R language was used for gene ontology(GO)enrichment analysis and Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis on DEGs.Hub genes were screened by protein-protein interaction(PPI)network analysis using STRING database.Additionally,gene expression levels were validated in the GSE89632 and GSE130970 datasets.Used the ROC curves to evaluate the diagnostic ability of them and performed the miRNA and transcription factor regulatory network analysis.Results A total of 308 DEGs were identified,including 243 upregulated genes and 65 downregulated genes.The enrichment analysis results showed that these DEGs were mainly enriched in chemokine signaling pathways,interactions between viral proteins and cytokines,as well as cytokine receptor interactions.PPI network analysis showed that the key modules containing 16 genes,including TOP2A,MKI67,CDC20,DLGAP5,and MCM10.ROC curves analysis implied that these 16 hub genes were potential biomarkers of NAFLD.Among them,the expression of 11 hub genes showed statistically significant differences between control group and NAFLD group in the GSE89632 dataset,and between high inflammation indices group and low inflammation indices group in the GSE130970 dataset(P<0.05).Conclusion Inflammation-related hub genes,such as TOP2A,CDC20,and HJURP,show promise as potential diagnostic and therapeutic targets for NAFLD.

关 键 词：GEO数据库 非酒精性脂肪性肝病 炎症损伤 核心基因 

分 类 号：R57[医药卫生—消化系统]