PDF(Pubmed)
Abstract：
UNASSIGNED: The implication of necroptosis in cardiovascular disease was already recognized. However, the molecular mechanism of necroptosis has not been extensively studied in coronary heart disease (CHD).
UNASSIGNED: The differentially expressed genes (DEGs) between CHD and control samples were acquired in the GSE20681 dataset downloaded from the GEO database. Key necroptosis-related DEGs were captured and ascertained by bioinformatics analysis techniques, including weighted gene co-expression network analysis (WGCNA) and two machine learning algorithms, while single-gene gene set enrichment analysis (GSEA) revealed their molecular mechanisms. The diagnostic biomarkers were selected via receiver operating characteristic (ROC) analysis. Moreover, an analysis of immune elements infiltration degree was carried out. Authentication of pivotal gene expression at the mRNA level was investigated in vitro utilizing quantitative real-time PCR (qRT-PCR).
UNASSIGNED: A total of 94 DE-NRGs were recognized here, among which, FAM166B, NEFL, POLDIP3, PRSS37, and ZNF594 were authenticated as necroptosis-related biomarkers, and the linear regression model based on them presented an acceptable ability to different sample types. Following regulatory analysis, the ascertained biomarkers were markedly abundant in functions pertinent to blood circulation, calcium ion homeostasis, and the MAPK/cAMP/Ras signaling pathway. Single-sample GSEA exhibited that APC co-stimulation and CCR were more abundant, and aDCs and B cells were relatively scarce in CHD patients. Consistent findings from bioinformatics and qRT-PCR analyses confirmed the upregulation of NEFL and the downregulation of FAM166B, POLDIP3, and PRSS37 in CHD.
UNASSIGNED: Our current investigation identified 5 necroptosis-related genes that could be diagnostic markers for CHD and brought a novel comprehension of the latent molecular mechanisms of necroptosis in CHD.
UNASSIGNED: The differentially expressed genes (DEGs) between CHD and control samples were acquired in the GSE20681 dataset downloaded from the GEO database. Key necroptosis-related DEGs were captured and ascertained by bioinformatics analysis techniques, including weighted gene co-expression network analysis (WGCNA) and two machine learning algorithms, while single-gene gene set enrichment analysis (GSEA) revealed their molecular mechanisms. The diagnostic biomarkers were selected via receiver operating characteristic (ROC) analysis. Moreover, an analysis of immune elements infiltration degree was carried out. Authentication of pivotal gene expression at the mRNA level was investigated in vitro utilizing quantitative real-time PCR (qRT-PCR).
UNASSIGNED: A total of 94 DE-NRGs were recognized here, among which, FAM166B, NEFL, POLDIP3, PRSS37, and ZNF594 were authenticated as necroptosis-related biomarkers, and the linear regression model based on them presented an acceptable ability to different sample types. Following regulatory analysis, the ascertained biomarkers were markedly abundant in functions pertinent to blood circulation, calcium ion homeostasis, and the MAPK/cAMP/Ras signaling pathway. Single-sample GSEA exhibited that APC co-stimulation and CCR were more abundant, and aDCs and B cells were relatively scarce in CHD patients. Consistent findings from bioinformatics and qRT-PCR analyses confirmed the upregulation of NEFL and the downregulation of FAM166B, POLDIP3, and PRSS37 in CHD.
UNASSIGNED: Our current investigation identified 5 necroptosis-related genes that could be diagnostic markers for CHD and brought a novel comprehension of the latent molecular mechanisms of necroptosis in CHD.
摘要：
■坏死在心血管疾病中的意义已经被认识到。然而,在冠心病（CHD）中尚未广泛研究坏死的分子机制。
■在从GEO数据库下载的GSE20681数据集中获得CHD和对照样品之间的差异表达基因(DEGs)。通过生物信息学分析技术捕获并确定与坏死相关的关键DEGs，包括加权基因共表达网络分析(WGCNA)和两种机器学习算法，而单基因基因集富集分析(GSEA)揭示了它们的分子机制。通过接受者操作特征(ROC)分析选择诊断性生物标志物。此外，对免疫因子浸润程度进行了分析。使用定量实时PCR（qRT-PCR）在体外研究了mRNA水平上关键基因表达的鉴定。
■这里总共识别了94个DE-NRG，其中,FAM166B,NEFL,POLDIP3、PRSS37和ZNF594被鉴定为坏死性凋亡相关生物标志物，基于它们的线性回归模型对不同样本类型具有可接受的能力。在监管分析之后，确定的生物标志物在与血液循环相关的功能上明显丰富，钙离子稳态，MAPK/cAMP/Ras信号通路。单样本GSEA显示APC共刺激和CCR更丰富，冠心病患者的aDC和B细胞相对缺乏。生物信息学和qRT-PCR分析的一致发现证实了NEFL的上调和FAM166B的下调，冠心病中的POLDIP3和PRSS37。
■我们目前的研究确定了5个与坏死相关的基因，这些基因可能是冠心病的诊断标志物，并带来了对冠心病坏死的潜在分子机制的新理解。
■在从GEO数据库下载的GSE20681数据集中获得CHD和对照样品之间的差异表达基因(DEGs)。通过生物信息学分析技术捕获并确定与坏死相关的关键DEGs，包括加权基因共表达网络分析(WGCNA)和两种机器学习算法，而单基因基因集富集分析(GSEA)揭示了它们的分子机制。通过接受者操作特征(ROC)分析选择诊断性生物标志物。此外，对免疫因子浸润程度进行了分析。使用定量实时PCR（qRT-PCR）在体外研究了mRNA水平上关键基因表达的鉴定。
■这里总共识别了94个DE-NRG，其中,FAM166B,NEFL,POLDIP3、PRSS37和ZNF594被鉴定为坏死性凋亡相关生物标志物，基于它们的线性回归模型对不同样本类型具有可接受的能力。在监管分析之后，确定的生物标志物在与血液循环相关的功能上明显丰富，钙离子稳态，MAPK/cAMP/Ras信号通路。单样本GSEA显示APC共刺激和CCR更丰富，冠心病患者的aDC和B细胞相对缺乏。生物信息学和qRT-PCR分析的一致发现证实了NEFL的上调和FAM166B的下调，冠心病中的POLDIP3和PRSS37。
■我们目前的研究确定了5个与坏死相关的基因，这些基因可能是冠心病的诊断标志物，并带来了对冠心病坏死的潜在分子机制的新理解。
