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中华损伤与修复杂志(电子版) ›› 2025, Vol. 20 ›› Issue (02) : 99 -106. doi: 10.3877/cma.j.issn.1673-9450.2025.02.003

论著

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
程呈1, 卢帅2, 陈蓉1, 李新萍1, 白睿峰3, 崔更力1, 陈烁2, 殷家伟2, 胡建鹏1, 汪垚卓1, 蒋协远2,(), 陈海翎1,()   
  1. 1. 100035 首都医科大学附属北京积水潭医院骨质疏松科
    2. 100035 首都医科大学附属北京积水潭医院创伤骨科
    3. 100035 首都医科大学附属北京积水潭医院检验科
  • 收稿日期:2024-11-15 出版日期:2025-04-01
  • 通信作者: 蒋协远, 陈海翎
  • 基金资助:
    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)

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

Cheng Cheng1, Shuai Lu2, Rong Chen1, Xinping Li1, Ruifeng Bai3, Gengli Cui1, Shuo Chen2, Jiawei Yin2, Jianpeng Hu1, Yaozhuo Wang1, Xieyuan Jiang2,(), Hailing Chen1,()   

  1. 1. Department of Osteoporosis,Beijing Jishuitan Hospital,Capital Medical University,Beijing 100035,China
    2. Department of Trauma Orthopedics,Beijing Jishuitan Hospital,Capital Medical University,Beijing 100035,China
    3. Department of Clinical Laboratory,Beijing Jishuitan Hospital,Capital Medical University,Beijing 100035,China
  • Received:2024-11-15 Published:2025-04-01
  • Corresponding author: Xieyuan Jiang, Hailing Chen
引用本文:

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. 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[J/OL]. 中华损伤与修复杂志(电子版), 2025, 20(02): 99-106.

Cheng Cheng, Shuai Lu, Rong Chen, Xinping Li, Ruifeng Bai, Gengli Cui, Shuo Chen, Jiawei Yin, Jianpeng Hu, Yaozhuo Wang, Xieyuan Jiang, Hailing Chen. Analysis of hub genes associated with inflammation injury in non-alcoholic fatty liver disease based on gene expression omnibus database[J/OL]. Chinese Journal of Injury Repair and Wound Healing(Electronic Edition), 2025, 20(02): 99-106.

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TOP2AMKI67CDC20DLGAP5MCM10 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

TOP2ACDC20HJURP 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 MKI67CDC20DLGAP5,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.

图1 GSE167523 数据集中高炎症指数组与低炎症指数组的DEGs。 A 示火山图;B 示热图 注:红色为表达上调的基因,蓝色为表达下调的基因
图2 DEGs 的GO 与KEGG 富集分析。 A 示GO-生物过程富集分析;B 示GO-细胞组分富集分析;C 示GO-分子功能富集分析;D 示KEGG富集分析 注:颜色从红色至绿色,越接近绿色相关越显著;圆圈越大,代表富集到该通路的数目越大
图3 筛选核心基因。 A 示DEGs 的PPI 网络分析(置信度>0.4,隐藏断开节点);B 示核心基因的筛选;C 示高炎症指数组和低炎症指数组核心基因的表达水平比较;D 示核心基因表达分布的ROC 曲线(AUC>0.7) 注:不同的颜色线条代表不同的核心基因
图4 基因调控网络构建及核心基因表达。 A 示采用Cytoscape 软件构建miRNAs 核心基因;B 示采用Cytoscape 软件构建TFs 核心基因;C示NAFLD 组与对照组之间核心基因表达水平比较;D 示在GSE130970 数据集中验证高炎症指数组和低炎症指数组核心基因的表达 注:图A、B 中蓝色代表核心基因, 橙色椭圆形为miRNA, 紫色菱形为转录因子
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