目的 筛选支架内再狭窄（ISR）中差异表达的免疫相关基因（DEIRGs），分析ISR中免疫细胞浸润情况，并阐明ISR发生发展的机制。 方法 由基因表达数据库（GEO）下载GSE46560数据集样本mRNA基因表达数据，分为ISR组与非ISR（non-ISR）组。采用R软件“Limma”包筛选出差异表达基因（DEGs）并与免疫相关基因（IRGs）交集获得ISR中DEIRGs。采用R软件进行DEIRGs的基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析，采用STRING数据库构建蛋白-蛋白互作（PPI）网络，以Cytoscape软件可视化并计算核心基因（Hub基因）。绘制Hub基因的受试者工作特征（ROC）曲线，计算ROC曲线下面积（AUC），并评价其诊断价值。采用CIBERSORT软件分析ISR中免疫细胞浸润情况，Pearson相关分析法分析免疫细胞间及其与关键基因之间的相关性。 结果 共鉴定出331个DEGs（P<0.05， | log₂FC | >1），其中176个基因表达上调，155个基因表达下调，获得38个DEIRGs。GO功能富集分析，在生物过程（BP）中DEIRGs主要富集在防御反应、免疫反应和免疫系统；在细胞组分（CC）中DEIRGs主要定位于胞外区和细胞质膜等；在分子功能（MF）中主要参与调控信号受体结合和细胞因子受体活性等。KEGG信号通路富集分析，ISR中DEIRGs主要富集于磷脂酰肌醇3-激酶/蛋白激酶B（PI3K-AKT）和转化生长因子β（TGF-β）等信号通路。 PPI 网络， 前10位Hub基因中 CD19 具有最高节点。 与 non-ISR 组比较，ISR组样本中 CD19 mRNA 表达水平明显升高（P<0.05）。 CD19 mRNA 表达的 ROC 曲线中 AUC值为0.92（P<0.05）。免疫细胞浸润分析，与non-ISR组比较，ISR组患者滤泡辅助性T淋巴细胞（Tfh）浸润水平升高（P<0.05），初始B淋巴细胞、CD8+T淋巴细胞、幼稚CD4+T淋巴细胞和M0巨噬细胞等浸润水平升高，但差异无统计学意义（P>0.05），记忆性B淋巴细胞、活化性记忆CD4+T淋巴细胞、调节性T淋巴细胞、静息性自然杀伤（NK）细胞、活化性NK细胞、单核细胞、静息性肥大细胞和中性粒细胞等浸润水平降低，但差异无统计学意义（P>0.05）。Tfh与M0巨噬细胞和静息肥大细胞等呈正相关关系（r=0.88，P<0.05；r=0.68，P<0.05），与单核细胞和中性粒细胞呈负相关关系（r=-0.49，P<0.05；r=-0.42，P<0.05）。 结论 CD19可能通过调控PI3K-AKT信号通路激活影响Tfh和B淋巴细胞，促进ISR的发生发展。CD19可作为诊断ISR的生物标志物。

Objective To screen the differentially expressed immune-related genes （DEIRGs） in in-stent restenosis （ISR）， and to analyze the immune cell infiltration in ISR， and to clarify the mechanism of occurrence and development of ISR. Methods The mRNA gene expression data of GSE46560 dataset samples were downloaded from the Gene Expression Omnibus （GEO），and divided into ISR group and non-ISR group. The “Limma” package in R software was used to identify the differentially expressed genes （DEGs） which were then intersected with immune-related genes （IRGs） to identify the DEIRGs in ISR； R software was used for Gene Ontology （GO） functional enrichment andalysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） signaling pathway enrichment analysis on DEIRGs；the STRING database was used to construct the protein-protein interaction （PPI） network， which was visualized and analyzed for Hub genes by Cytoscape software； the receiver operating characteristic （ROC） curve of the Hub genes were plotted， and the area under the curve （AUC） was calculated and the diagnostic value was evaluated； CIBERSORT software was used to analyze the immune cell infiltration in ISR； Pearson correlation analysis was used to analyze the relationships between the immune cells and the relationships between the immune cells and key genes. Results A total of 331 DEGs were identified （P<0.05， | log₂FC| >1）， including 176 upregulated genes and 155 downregulated genes， and 38 DEIRGs were obstained. The GO functional enrichment analysis results showed that the DEIRGs were mainly enriched in biological processes （BP） such as defense response， immune response， and immune system； in cellular components （CC），the DEIRGs were located primarily in the extracellular region and cytoplasmic membrane； and in molecular functions （MF）， the DEIRGs were mainly involved in regulating signaling receptor binding and cytokine receptor activity.The KEGG signaling pathway enrichment analysis results indicated that the DEIRGs in ISR were primarily enriched in the phosphatidylinositol 3-kinase/protein kinase B （PI3K-AKT） and transforming growth factor-β （TGF-β） signaling pathways. In the PPI network， CD19 had the highest node among the top 10 Hub genes. Compared with non-ISR group， the expression level of the CD19 gene in the samples in ISR group was increased （P<0.05）. The AUC value in the ROC curve of CD19 gene expression was 0.92 （P<0.05）. The immune cell infiltration analysis results showed that compared with non-ISR group， the infiltration level of T lymphocyte follicular helper （Tfh） cells in the patients in ISR group were increased （P<0.05）， the infiltration levels of immature B lymphocytes， CD8+T lymphocytes， naive CD4+T lymphocytes， and M0 macrophages were increased， but the differences were not statistically significant （P>0.05）， while the infiltration levels of memory B lymphocytes， activated memory CD4+ T lymphocytes， regulatory T cells， resting natural killer （NK） cells， activated NK cells， monocytes， resting mast cells， and neutrophils were decreased， but the differences were not statistically significant （P>0.05）. There were positive correlations between Tfh cells and M0 macrophages and resting mast cells （r=0.88，P<0.05；r=0.68，P<0.05）， and there were negative correlations between Tfh cells and monocytes and neutrophils（r=-0.49，P<0.05；r=-0.42，P<0.05）. Conclusion CD19 may influence the occurrence and development of ISR by regulating the activation of the PI3K-AKT signaling pathway to affect the Tfh and B lymphocytes. CD19 can serve as a biomarker for the diagnosis of ISR.