Yang Yun-Chuan, Ma Xiang, Zhou Chi, Xu Nan, Ding Ding, Ma Zhong-Zheng, Zhou Lei, Cui Pei-Yuan
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Bengbu Medical University, Bengbu 233000, Anhui Province, China.
Medical College, Jinan University, Guangzhou 510000, Guangdong Province, China.
World J Clin Cases. 2024 Oct 26;12(30):6391-6406. doi: 10.12998/wjcc.v12.i30.6391.
The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis (PBC) and finding relevant biomarkers for diagnosis and therapeutic evaluation.
To determine PBC-associated hub genes and assess their clinical utility for disease prediction.
PBC expression data were obtained from the Gene Expression Omnibus database. Overlapping genes from differential expression analysis and weighted gene co-expression network analysis (WGCNA) were identified as key genes for PBC. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes. Hub genes were identified in protein-protein interaction (PPI) networks using the Degree algorithm in Cytoscape software. The relationship between hub genes and immune cells was investigated. Finally, a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC.
We identified 71 overlapping key genes using differential expression analysis and WGCNA. These genes were primarily enriched in pathways related to cytokine-cytokine receptor interaction, and Th1, Th2, and Th17 cell differentiation. We utilized Cytoscape software and identified five hub genes (, , , , and ) in PPI networks. These hub genes showed a strong correlation with immune cell infiltration in PBC. However, inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk.
Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment, thereby offering significant clinical utility.
识别特定的基因表达模式对于理解原发性胆汁性胆管炎(PBC)的潜在机制以及寻找用于诊断和治疗评估的相关生物标志物至关重要。
确定与PBC相关的枢纽基因,并评估其在疾病预测中的临床应用价值。
从基因表达综合数据库获取PBC表达数据。通过差异表达分析和加权基因共表达网络分析(WGCNA)确定的重叠基因被识别为PBC的关键基因。进行京都基因与基因组百科全书和基因本体分析以探索关键基因的潜在作用。使用Cytoscape软件中的度算法在蛋白质-蛋白质相互作用(PPI)网络中识别枢纽基因。研究枢纽基因与免疫细胞之间的关系。最后,进行孟德尔随机化研究以确定枢纽基因对PBC的因果效应。
通过差异表达分析和WGCNA,我们确定了71个重叠的关键基因。这些基因主要富集在与细胞因子-细胞因子受体相互作用以及Th1、Th2和Th17细胞分化相关的通路中。我们利用Cytoscape软件在PPI网络中识别出五个枢纽基因(、、、和)。这些枢纽基因与PBC中的免疫细胞浸润显示出强烈的相关性。然而,逆方差加权分析并未表明枢纽基因对PBC风险的因果效应。
枢纽基因有可能作为PBC预测和治疗的有价值生物标志物,从而具有重要的临床应用价值。