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.

Keywords: differentially expressed genes, inflammatory bowel disease, toll-like receptor pathway, FcγR signaling, anti-TNFα therapy


中文翻译：

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综合基因表达谱分析揭示了抗 TNFα 无反应 IBD 患者可能的分子机制和候选生物标志物。

目的：探索与抗 TNFα 药物反应相关的 IBD 患者基因表达谱中的分子机制并寻找候选生物标志物。
方法：使用 NetworkAnalyst 整合数据集 GSE12251、GSE16879 和 GSE23597 中反应与无反应 IBD 患者的差异表达基因 (DEG)。我们对基因本体和京都基因和基因组百科全书（KEGG）通路进行了功能富集分析，并从蛋白质-蛋白质相互作用网络中提取了枢纽基因。通过 CIBERSORT 估计免疫细胞类型的比例。应用 ROC 曲线分析和二项式 Lasso 回归评估数据集 GSE12251、GSE16879 和 GSE23597 以及另外两个数据集 GSE107865 和 GSE42296 中中心基因的表达水平。
结果：从综合数据集中获得了 287 个 DEG。它们富含 14 个基因本体术语和 11 个 KEGG 通路。在无反应个体中，从 M2 到 M1 巨噬细胞的极化相对较高。我们发现了九个中枢基因（TLR4、TLR1、TLR8、CCR1、CD86、CCL4、HCK 和 FCGR2A），主要与无应答抗 TNFα 个体中 Toll 样受体 (TLR) 通路和 FcγR 信号传导之间的相互作用有关。FCGR2A、HCK、TLR1、TLR4、TLR8 和 CCL4 在肠组织中显示出巨大的预测价值。此外，FCGR2A、HCK 和 TLR8 可能是抗 TNFα 无反应 IBD 患者的候选血液生物标志物。
结论：IBD 患者先天免疫细胞中 FcγR-TLR 轴之间过度激活的相互作用可用于识别无反应个体并增加我们对抗 TNFα 治疗抗性的理解。

关键词：差异表达基因，炎症性肠病，toll​​样受体通路，FcγR信号通路，抗TNFα治疗