PDF(Pubmed)
Abstract：
Angiogenesis has been discovered to be a critical factor in developing tumors and ischemic diseases. However, the role of angiogenesis-related genes (ARGs) in acute myocardial infarction (AMI) remains unclear.
The GSE66360 dataset was used as the training cohort, and the GSE48060 dataset was used as the external validation cohort. The random forest (RF) algorithm was used to identify the signature genes. Consensus clustering analysis was used to identify robust molecular clusters associated with angiogenesis. The ssGSEA was used to analyze the correlation between ARGs and immune cell infiltration. In addition, we constructed miRNA-gene, transcription factor network, and targeted drug network of signature genes. RT-qPCR was used to verify the expression levels of signature genes.
Seven signature ARGs were identified based on the RF algorithm. Receiver operating characteristic curves confirmed the classification accuracy of the risk predictive model based on signature ARGs (area under the curve [AUC] = 0.9596 in the training cohort and AUC = 0.7773 in the external validation cohort). Subsequently, the ARG clusters were identified by consensus clustering. Cluster B had a more generalized high expression of ARGs and was significantly associated with immune infiltration. The miRNA and transcription factor network provided new ideas for finding potential upstream targets and biomarkers. Finally, the results of RT-qPCR were consistent with the bioinformatics analysis, further validating our results.
Angiogenesis is closely related to AMI, and characterizing the angiogenic features of patients with AMI can help to risk-stratify patients and provide personalized treatment.
The GSE66360 dataset was used as the training cohort, and the GSE48060 dataset was used as the external validation cohort. The random forest (RF) algorithm was used to identify the signature genes. Consensus clustering analysis was used to identify robust molecular clusters associated with angiogenesis. The ssGSEA was used to analyze the correlation between ARGs and immune cell infiltration. In addition, we constructed miRNA-gene, transcription factor network, and targeted drug network of signature genes. RT-qPCR was used to verify the expression levels of signature genes.
Seven signature ARGs were identified based on the RF algorithm. Receiver operating characteristic curves confirmed the classification accuracy of the risk predictive model based on signature ARGs (area under the curve [AUC] = 0.9596 in the training cohort and AUC = 0.7773 in the external validation cohort). Subsequently, the ARG clusters were identified by consensus clustering. Cluster B had a more generalized high expression of ARGs and was significantly associated with immune infiltration. The miRNA and transcription factor network provided new ideas for finding potential upstream targets and biomarkers. Finally, the results of RT-qPCR were consistent with the bioinformatics analysis, further validating our results.
Angiogenesis is closely related to AMI, and characterizing the angiogenic features of patients with AMI can help to risk-stratify patients and provide personalized treatment.
摘要：
背景：已发现血管生成是发展肿瘤和缺血性疾病的关键因素。然而,血管生成相关基因(ARGs)在急性心肌梗死(AMI)中的作用尚不清楚.
方法：使用GSE66360数据集作为训练组，GSE48060数据集用作外部验证队列。使用随机森林(RF)算法来鉴定特征基因。使用共有聚类分析来鉴定与血管生成相关的稳健分子簇。采用ssGSEA分析ARGs与免疫细胞浸润的相关性。此外,我们构建了miRNA基因，转录因子网络，和标记基因的靶向药物网络。RT-qPCR用于验证标记基因的表达水平。
结果：根据RF算法确定了7个特征ARG。受试者工作特征曲线证实了基于特征ARG的风险预测模型的分类准确性(训练队列中曲线下面积[AUC]=0.9596,外部验证队列中AUC=0.7773)。随后，通过共识聚类确定ARG聚类.B簇具有更广泛的ARGs高表达，并且与免疫浸润显着相关。miRNA和转录因子网络为寻找潜在的上游靶标和生物标志物提供了新思路。最后,RT-qPCR结果与生物信息学分析结果一致,进一步验证我们的结果。
结论：血管生成与AMI密切相关，和描述AMI患者的血管生成特征有助于对患者进行风险分层并提供个性化治疗。
方法：使用GSE66360数据集作为训练组，GSE48060数据集用作外部验证队列。使用随机森林(RF)算法来鉴定特征基因。使用共有聚类分析来鉴定与血管生成相关的稳健分子簇。采用ssGSEA分析ARGs与免疫细胞浸润的相关性。此外,我们构建了miRNA基因，转录因子网络，和标记基因的靶向药物网络。RT-qPCR用于验证标记基因的表达水平。
结果：根据RF算法确定了7个特征ARG。受试者工作特征曲线证实了基于特征ARG的风险预测模型的分类准确性(训练队列中曲线下面积[AUC]=0.9596,外部验证队列中AUC=0.7773)。随后，通过共识聚类确定ARG聚类.B簇具有更广泛的ARGs高表达，并且与免疫浸润显着相关。miRNA和转录因子网络为寻找潜在的上游靶标和生物标志物提供了新思路。最后,RT-qPCR结果与生物信息学分析结果一致,进一步验证我们的结果。
结论：血管生成与AMI密切相关，和描述AMI患者的血管生成特征有助于对患者进行风险分层并提供个性化治疗。
