摘要
目的通过药物性肝损伤(DILI)细胞的基因组信息学分析获得肝脏特异性差异表达基因(LSDEG)。方法从GSE54255和GSE102006数据集中筛选LSDEG,构建了LSDEG的蛋白质互作网络(PPI)、模块分析和关键(Hub)基因。Hub基因进行京都基因与基因组百科全书通路(KEGG)和基因本体论-生物过程(GO-BP)富集以及转录因子(TF)-基因共调节网络。结果筛选113个LSDEG,LSDEG-PPI网络由与炎症反应和肝细胞凋亡相关的模块组成。筛选JAK2、ICAM1、IRF1、NFκBIA、MDM2和ERRB这6个Hub基因,其生物学途径集中在物质代谢、免疫调节和细胞死亡等方面,调控TF分别为STAT家族、NFκB家族、ETS家族、TP53、SP1和EP300。结论6个Hub基因是DILI特异性差异表达基因,它们降低药物代谢活性和增加毒性代谢物的积累。同时,异常诱导肝脏内细胞免疫的攻击性和体液免疫的炎症风暴,最终导致肝细胞损伤。
Objective To obtain liver-specific differentially expressed genes(LSDEG)through genomic informatics analysis of drug-induced liver injury.Methods The LSDEGs were screened from GSE54255and GSE102006datasets,and the protein-protein interaction network(PPI)network,module analysis and Hub gene of LSDEG were constructed.The Hub gene was enriched with Kyoto Encyclopedia of Genes and Genomes(KEGG)and Gene Ontology-Biological Processes(GOBP)and the transcription factor(TF)-gene co-regulation network.Results Totally 113LSDEGs were screened.The LSDEG-PPI network was composed of modules related to inflammatory reaction and hepatocyte apoptosis.We filtered 26Hub gemes,such as JAK2,ICAM1,IRF1,NFκBIA,MDM2 and ERRB2,and the biological pathway of which focused on material metabolism,immune regulation and cell death.The regulatory transcription factors were STAT family,NFκB family,ETS family,TP53,SP1and EP300,respectively.Conclusions The six Hub genes are differentially expressed genes specific to drug-induced liver injury,which reduce drug metabolic activity and increase the accumulation of toxic metabolites.Furthermore,the abnormality induces the aggressiveness of cellular immunity in the liver and the inflammatory storm of humoral immunity,which eventually leads to liver cell damage.
作者
窦锦明
王振华
DOU Jinming;WANG Zhenhua(Clinical Pharmacy,Weifang Hospital of Traditional Chinese Medicine,Weifang261041,China)
出处
《社区医学杂志》
CAS
2023年第15期780-785,共6页
Journal Of Community Medicine
基金
中国毒理学会临床毒理专项项目(CST2019CT307)
山东省中医药科技项目(2021M094)
潍坊市中医药科研项目(2020-1-004)。
关键词
药物性肝损伤
差异表达
基因
生物信息学
drug-induced liver injury
differential expression
genes
bioinformatics