目的 利用生物信息学分析方法挖掘三叉神经痛的关键基因并探索其重要免疫相关生物标志物。方法 本研究从基因表达综合数据库（GEO）获取与三叉神经痛相关的基因表达谱GSE186505以进行进一步分析。用生物信息学方法筛选与三叉神经痛相关的差异表达基因(DEGs)，并进行基因本体（GO）功能注释和京都基因与基因组百科全书（KEGG）富集分析探索DEGs的潜在生物学功能，通过蛋白质-蛋白质相互作用（PPI）网络进一步筛选三叉神经痛相关的关键基因，利用Cibersort分析不同样品中免疫细胞的浸润程度。结果 共筛选出56个DEGs，其中包括23个上调基因和33个下调基因。在PPI分析中鉴定了两个必要的PPI模块，基于STRING数据库筛选出ANXA5, ENO1, CA2, FBP1, CD9, SMPD3, VSTM1, SPNS3, PRSS33, FAS, LENG8 这11个关键基因可能参与三叉神经痛的发生机制。功能富集分析表明，细胞对肿瘤坏死因子的反应，半胱氨酸型内肽酶活性的调节，膜的外部成分，磷脂结合的外部成分和嘧啶代谢与三叉神经痛密切相关（P＜0.05）。免疫浸润分析显示，幼稚B细胞、M2巨噬细胞在TN样本中表达明显增加，而记忆B细胞、幼稚CD4+T细胞在健康对照组中表达增加。结论 三叉神经痛的发病机制可能是通过调节ANXA5, ENO1, CA2, FBP1, CD9, SMPD3, VSTM1, SPNS3, PRSS33, FAS, LENG8等基因，参与细胞对肿瘤坏死因子的反应、半胱氨酸型内肽酶活性的调节等生物过程，调控嘧啶代谢、糖酵解/糖异生、碳代谢等信号通路来完成的。三叉神经痛发病与幼稚B细胞、M2巨噬细胞增多密切相关。
Objective To screen key genes of trigeminal neuralgia (TN) and explore its important immune-related biomarkers by bioinformatics analysis. Methods This research utilized the GSE186505 expression profiling data from the Gene Expression Omnibus (GEO) database as the training dataset for further analysis. Differentially expressed genes (DEGs) between healthy controls and patients with trigeminal neuralgia were selected using the limma package. The potential biological functions of DEGs were explored via Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Key genes related to TN were further screened by protein–protein interaction (PPI) network. The degree of infiltration of immune cells in different samples was analyzed by CIBERSORT analysis package. Results 56 DEGs were screened from TN samples using limma package, including 23 up-regulated genes and 33 down-regulated genes. Two necessary PPI modules were identified in PPI analysis. Based on the STRING database, 11 key genes, ANXA5, ENO1, CA2, FBP1, CD9, SMPD3, VSTM1, SPNS3, PRSS33, FAS, LENG8, were screened for their possible involvement in the development of TN. Functional enrichment analysis demonstrated that cellular response to tumor necrosis factor, regulation of cysteine−type endopeptidase activity, extrinsic component of membrane, phospholipid binding and pyrimidine metabolism showed strongly associated with TN (p 0.05). Immuno-infiltration analysis showed that naive B cells and M2 macrophages had significantly increased expression in the TN samples, while memory B cells and naive CD4+ T cells had increased expression in the healthy controls. Conclusions The pathogenesis of TN is probably mediated by the regulation of genes ANXA5, ENO1, CA2, FBP1, CD9, SMPD3, VSTM1, SPNS3, PRSS33, FAS, LENG8, which participate in biological processes such as cellular responses to tumor necrosis factors, regulation of cysteine-type endopeptidase activity, and regulates signaling pathways such as pyrimidine metabolism, glycolysis/gluconeogenesis, and carbon metabolism to do so. The pathogenesis of TN is closely associated with an increase in naive B cells and M2 macrophages.
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