Abstract：
BACKGROUND: This study used bioinformatics combined with statistical methods to identify plasma biomarkers that can predict intracranial aneurysm (IA) rupture and provide a strong theoretical basis for the search for new IA rupture prevention methods.
METHODS: We downloaded gene expression profiles in the GSE36791 and GSE122897 datasets from the Gene Expression Omnibus (GEO) database. Data were normalized using the \"sva\" R package and differentially expressed genes (DEGs) were identified using the \"limma\" R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used for DEG function analysis. Univariate logistic regression analysis, least absolute shrinkage and selection operator (LASSO) regression modeling, and the support vector machine recursive feature elimination (SVM-RFE) algorithm were used to identify key biomarker genes. Data from GSE122897 and GSE13353 were extracted to verify our findings.
RESULTS: Eight co-DEG mRNAs were identified in the GSE36791 and GSE122897 datasets. Genes associated with inflammatory responses were clustered in the co-DEG mRNAs in IAs. CD6 and C-C chemokine receptor 7 (CCR7) were identified as key genes associated with IA. CD6 and CCR7 were upregulated in patients with IA and their expression levels were positively correlated. There were significant differences in the infiltration of immune cells between IAs and normal vascular wall tissues (p < 0.05). A predictive nomogram was designed using this two-gene signature. Binary transformation of CD6 and CCR7 was performed according to the cut-off value to construct the receiver-operating characteristic (ROC) curve and showed a strong predictive ability of the CD6-CCR7 gene signature (p < 0.01; area under the curve (AUC): 0.90; 95% confidence interval (CI): 0.88-0.92). Furthermore, validation of this two-gene signature using the GSE122897 and GSE13353 datasets proved it to be valuable for clinical application.
CONCLUSIONS: The identified two-gene signature (CD6-CCR7) for evaluating the risk of IA rupture demonstrated good clinical application value.
METHODS: We downloaded gene expression profiles in the GSE36791 and GSE122897 datasets from the Gene Expression Omnibus (GEO) database. Data were normalized using the \"sva\" R package and differentially expressed genes (DEGs) were identified using the \"limma\" R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used for DEG function analysis. Univariate logistic regression analysis, least absolute shrinkage and selection operator (LASSO) regression modeling, and the support vector machine recursive feature elimination (SVM-RFE) algorithm were used to identify key biomarker genes. Data from GSE122897 and GSE13353 were extracted to verify our findings.
RESULTS: Eight co-DEG mRNAs were identified in the GSE36791 and GSE122897 datasets. Genes associated with inflammatory responses were clustered in the co-DEG mRNAs in IAs. CD6 and C-C chemokine receptor 7 (CCR7) were identified as key genes associated with IA. CD6 and CCR7 were upregulated in patients with IA and their expression levels were positively correlated. There were significant differences in the infiltration of immune cells between IAs and normal vascular wall tissues (p < 0.05). A predictive nomogram was designed using this two-gene signature. Binary transformation of CD6 and CCR7 was performed according to the cut-off value to construct the receiver-operating characteristic (ROC) curve and showed a strong predictive ability of the CD6-CCR7 gene signature (p < 0.01; area under the curve (AUC): 0.90; 95% confidence interval (CI): 0.88-0.92). Furthermore, validation of this two-gene signature using the GSE122897 and GSE13353 datasets proved it to be valuable for clinical application.
CONCLUSIONS: The identified two-gene signature (CD6-CCR7) for evaluating the risk of IA rupture demonstrated good clinical application value.
摘要：
背景：这项研究利用生物信息学结合统计学方法来鉴定可预测颅内动脉瘤（IA）破裂的血浆生物标志物，并为寻找新的IA破裂预防方法提供了强有力的理论基础。
方法：我们从基因表达综合（GEO）数据库下载了GSE36791和GSE122897数据集中的基因表达谱。使用“sva”R包标准化数据，并使用“limma”R包鉴定差异表达基因（DEG）。基因本体论(GO)和京都基因和基因组百科全书(KEGG)途径富集分析用于DEG功能分析。单因素Logistic回归分析，最小绝对收缩和选择算子(LASSO)回归建模，和支持向量机递归特征消除(SVM-RFE)算法用于识别关键生物标记基因。提取来自GSE122897和GSE13353的数据以验证我们的发现。
结果:在GSE36791和GSE122897数据集中鉴定了8个co-DEGmRNA。与炎症反应相关的基因聚集在IA的co-DEGmRNA中。CD6和C-C趋化因子受体7(CCR7)被鉴定为与IA相关的关键基因。CD6和CCR7在IA患者中上调，其表达水平呈正相关。免疫细胞浸润在IAs和正常血管壁组织间有明显差别(p<0.05)。使用这种双基因签名设计了预测列线图。根据截断值进行CD6和CCR7的二元转化以构建受试者-工作特征(ROC)曲线,并显示CD6-CCR7基因标签的强预测能力(p<0.01;曲线下面积(AUC):0.90;95%置信区间(CI):0.88-0.92)。此外，使用GSE122897和GSE13353数据集验证该双基因签名证明其在临床应用中很有价值.
结论：鉴定的双基因标记（CD6-CCR7）用于评估IA破裂风险具有良好的临床应用价值。
方法：我们从基因表达综合（GEO）数据库下载了GSE36791和GSE122897数据集中的基因表达谱。使用“sva”R包标准化数据，并使用“limma”R包鉴定差异表达基因（DEG）。基因本体论(GO)和京都基因和基因组百科全书(KEGG)途径富集分析用于DEG功能分析。单因素Logistic回归分析，最小绝对收缩和选择算子(LASSO)回归建模，和支持向量机递归特征消除(SVM-RFE)算法用于识别关键生物标记基因。提取来自GSE122897和GSE13353的数据以验证我们的发现。
结果:在GSE36791和GSE122897数据集中鉴定了8个co-DEGmRNA。与炎症反应相关的基因聚集在IA的co-DEGmRNA中。CD6和C-C趋化因子受体7(CCR7)被鉴定为与IA相关的关键基因。CD6和CCR7在IA患者中上调，其表达水平呈正相关。免疫细胞浸润在IAs和正常血管壁组织间有明显差别(p<0.05)。使用这种双基因签名设计了预测列线图。根据截断值进行CD6和CCR7的二元转化以构建受试者-工作特征(ROC)曲线,并显示CD6-CCR7基因标签的强预测能力(p<0.01;曲线下面积(AUC):0.90;95%置信区间(CI):0.88-0.92)。此外，使用GSE122897和GSE13353数据集验证该双基因签名证明其在临床应用中很有价值.
结论：鉴定的双基因标记（CD6-CCR7）用于评估IA破裂风险具有良好的临床应用价值。
