Abstract：
OBJECTIVE: This study aimed to explore potential hub genes and pathways of plaque vulnerability and to investigate possible therapeutic targets for acute coronary syndrome (ACS).
RESULTS: Four microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein-protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases.
CONCLUSIONS: We performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.
RESULTS: Four microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein-protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases.
CONCLUSIONS: We performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.
摘要：
目的：本研究旨在探索斑块易损性的潜在枢纽基因和通路，并探讨急性冠脉综合征（ACS）的可能治疗靶点。
结果:从基因表达综合(GEO)数据库下载四个微阵列数据集。差异表达基因（DEGs），加权基因共表达网络（WGCNA）和免疫细胞干预分析（IIA）用于鉴定斑块易损性的基因。然后，京都基因和基因组百科全书（KEGG）途径富集，疾病本体论,进行基因本体注释和蛋白质-蛋白质相互作用（PPI）网络分析以探索枢纽基因。构建了随机森林和人工神经网络进行验证。此外，CMap和Herb数据库用于探索可能的治疗靶点.在GSE62646中鉴定了总共168个具有调整的P<0.05的DEGs和约1974个IIA基因。检测到三个模块，并与CAD-Class相关联，包括可以在GSE90074中找到的891个基因。删除重复项后，114个hub基因用于功能分析。GO功能确定157个项目，并在调整后的P<0.05（错误发现率，FDR设置为<0.05)。基于GSE48060和GSE34822数据集建立随机森林和人工神经网络模型,分别,来验证之前的hub基因。五个基因（GZMA，GZMB,选择KLRB1，KLRD1和TRPM6），在CMap和Herb数据库中仅筛选了其中两个（GZMA和GZMB）作为治疗靶标。
结论：我们进行了全面分析，并验证了GZMA和GZMB作为斑块易损性的目标，这为ACS的预防提供了治疗策略。然而,它是否可以用作血液样本的预测因子还需要进一步的实验验证。
结果:从基因表达综合(GEO)数据库下载四个微阵列数据集。差异表达基因（DEGs），加权基因共表达网络（WGCNA）和免疫细胞干预分析（IIA）用于鉴定斑块易损性的基因。然后，京都基因和基因组百科全书（KEGG）途径富集，疾病本体论,进行基因本体注释和蛋白质-蛋白质相互作用（PPI）网络分析以探索枢纽基因。构建了随机森林和人工神经网络进行验证。此外，CMap和Herb数据库用于探索可能的治疗靶点.在GSE62646中鉴定了总共168个具有调整的P<0.05的DEGs和约1974个IIA基因。检测到三个模块，并与CAD-Class相关联，包括可以在GSE90074中找到的891个基因。删除重复项后，114个hub基因用于功能分析。GO功能确定157个项目，并在调整后的P<0.05（错误发现率，FDR设置为<0.05)。基于GSE48060和GSE34822数据集建立随机森林和人工神经网络模型,分别,来验证之前的hub基因。五个基因（GZMA，GZMB,选择KLRB1，KLRD1和TRPM6），在CMap和Herb数据库中仅筛选了其中两个（GZMA和GZMB）作为治疗靶标。
结论：我们进行了全面分析，并验证了GZMA和GZMB作为斑块易损性的目标，这为ACS的预防提供了治疗策略。然而,它是否可以用作血液样本的预测因子还需要进一步的实验验证。
