Objective: To extract the differentially expressed key genes of primary biliary cholangitis (PBC) using bioinformatics methods, so as to provide information for further study into the mechanism. Methods: The GSE119600 dataset was downloaded from the GEO database to obtain differentially expressed genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for differentially expressed genes. Protein-protein interaction (PPI) network reconstruction, Cytoscape software visualization, and core gene screening were performed. The area under the receiver operating characteristic curve (ROC AUC) was used to assess the diagnostic effectiveness of genes and plot the pROC software package. The x-Cell software was used to calculate the enrichment score of 34 immune cells in each sample. Finally, four key genes (PSMA4, PSMA1, PSMB1, and PSMA3) were selected. Blood samples were analyzed using the qPCR method. Results:: A total of 373 immune-related differentially expressed genes were identified. Eight genes (PSMC6, PSMB2, PSMB1, PSMA3, PSMA4, PSMA1, PSMD7, and PSMB5) were screened from the 178 nodes and 596 edges as hub genes of the PPI network, which were significantly related to amino acid metabolism, hematopoietic stem cell differentiation, cell cycle, and immune processes. PSMA4, PSMA1, PSMB1, and PSMA3 were defined as immunological biomarkers for PBC with an AUC value of the ROC curve > 0.7. Immunoinfiltrating cell analysis showed that the proportion of eosinophils was significantly higher in PBC patients compared to the control group, whereas the proportion of CD4+ memory T cells, plasma cells, Th2 cells, and cDC cells was significantly lower in PBC patients than the control group. Plasma cells were associated with all four immunological biomarkers. Seven PBC patients and seven healthy subjects were selected for peripheral blood qPCR validation, which demonstrates that PSMB1, PSMA3, PSMA1, and PSMA4 levels were significantly lower in PBC patients than healthy subjects, with a statistically significant difference. Conclusion:: Bioinformatics screened eight key genes, of which four were key immunological markers and may serve as a basis for clinical diagnosis and mechanism exploration.
目的： 采用生物信息学方法挖掘原发性胆汁性胆管炎(PBC)的差异表达的关键基因，以期为进一步研究机制提供信息。 方法： 从GEO数据库中下载GSE119600数据集，获取差异表达基因；对差异基因进行基因本体论(GO)富集与京都基因和基因组百科全书(KEGG)路径分析；再构建蛋白-蛋白相互作用(PPI)网络，用Cytoscape软件进行可视化并筛选核心基因。受试者操作特征(ROC)曲线下面积(AUC)用于评估基因诊断的有效性，并由"pROC"软件包绘制。用x-Cell工具计算每个样本中34种免疫细胞的富集评分。最后，选取PSMA4、PSMA1、PSMB1和PSMA3 4个关键基因，用qPCR法对血液样本进行鉴定。 结果： 共鉴定出373个免疫相关差异表达基因。从178个节点596条边的PPI网络中筛选出8个基因(PSMC6、PSMB2、PSMB1、PSMA3、PSMA4、PSMA1、PSMD7和PSMB5)作为hub基因，与细胞氨基酸代谢相关过程、造血干细胞分化相关过程、细胞周期相关过程和免疫相关过程显著相关。然后按ROC曲线的AUC值>0.7，将PSMA4、PSMA1、PSMB1和PSMA3定义为PBC的免疫生物标志物。通过免疫浸润细胞分析，我们发现PBC患者的嗜酸性粒细胞比例明显高于对照组，CD4(+)记忆T细胞、浆细胞、Th2细胞和cDC细胞比例明显低于对照组。所有4种免疫生物标志物都与浆细胞相关。纳入7例PBC患者及7名健康人外周血qPCR验证，PSMB1、PSMA3、PSMA1、PSMA4水平显著低于健康人，差异有统计学意义。 结论： 通过生物信息学筛选出8个关键基因，其中4个为关键免疫标志物，可能为临床诊断提供和机制探讨依据。.
Keywords: Bioinformation; Differential gene expression; Immunocyte; Primary biliary cholangitis; Protesome.