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首页> 外文期刊>Journal of Inflammation Research >Integrated Gene Expression Profiling Analysis Reveals Probable Molecular Mechanism and Candidate Biomarker in Anti-TNFα Non-Response IBD Patients
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Integrated Gene Expression Profiling Analysis Reveals Probable Molecular Mechanism and Candidate Biomarker in Anti-TNFα Non-Response IBD Patients

机译:综合基因表达分析分析显示抗TNFα非反应IBD患者的可能分子机制和候选生物标志物

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Purpose: To explore the molecular mechanism and search for candidate biomarkers in the gene expression profile of IBD patients associated with the response to anti-TNFα agents. Methods: Differentially expressed genes (DEGs) of response vs non-response IBD patients in datasets GSE12251, GSE16879, and GSE23597 were integrated using NetworkAnalyst. We conducted functional enrichment analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and extracted hub genes from the protein–protein interaction network. The proportion of immune cell types was estimated via CIBERSORT. ROC curve analysis and binomial Lasso regression were applied to assess the expression level of hub genes in datasets GSE12251, GSE16879, and GSE23597, and another two datasets GSE107865 and GSE42296. Results: A total of 287 DEGs were obtained from the integrated dataset. They were enriched in 14 Gene Ontology terms and 11 KEGG pathways. Polarization from M2 to M1 macrophages was relatively high in non-response individuals. We found nine hub genes (TLR4, TLR1, TLR8, CCR1, CD86, CCL4, HCK, and FCGR2A), mainly related to the interaction between Toll-like Receptor (TLR) pathway and FcγR signaling in non-response anti-TNFα individuals. FCGR2A, HCK, TLR1, TLR4, TLR8, and CCL4 show great value for prediction in intestinal tissue. Besides, FCGR2A, HCK, and TLR8 might be candidate blood biomarkers of anti-TNFα non-response IBD patients. Conclusion: Over-activated interaction between FcγR-TLR axis in the innate immune cells of IBD patients might be used to identify non-response individuals and increased our understanding of resistance to anti-TNFα therapy.
机译:目的:探讨与对抗TNFα剂的反应相关的IBD患者基因表达谱中的分子机制和搜索候选生物标志物。方法:使用NetworkAnalyst集成了DataSets GSE12251,GSE16879和GSE23597中的差异表达的响应基因(DEGS)对响应的响应与非响应IBD患者。我们进行了基因本体论的功能性浓缩分析,基因和基因组(Kegg)途径和从蛋白质 - 蛋白质相互作用网络中提取的轮毂基因提取的枢纽基因。通过Cibersort估计免疫细胞类型的比例。曲线曲线分析和二元套索回归被应用于评估数据集GSE12251,GSE16879和GSE23597中的集线器基因的表达水平,以及另外两个数据集GSE107865和GSE42296。结果:从集成数据集中获得了总共287次。它们以14个基因本体论术语和11 kegg途径富集。来自M2至M1巨噬细胞的极化在非反应个体中相对较高。我们发现九个集线基因(TLR4,TLR1,TLR8,CCR1,CD86,CCL4,HCK和FCGR2A),主要与无响应抗TNFα个体中的Toll样受体(TLR)途径和FCγR信令之间的相互作用相关。 FCGR2A,HCK,TLR1,TLR4,TLR8和CCL4显示出肠组织预测的良好值。此外,FCGR2A,HCK和TLR8可能是抗TNFα非反应IBD患者的候选血液生物标志物。结论:在IBD患者的先天免疫细胞中FCγR-TLR轴之间的过激活相互作用可用于鉴定非反应个体,并增加了对抗TNFα治疗的抗性的理解。

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