未经授权:寻找与猝死(SD)相关的重要生物标志物。
UNASSIGNED:通过比较15例意外死亡(AD)和88例SD的全血样本来筛选差异基因。蛋白质-蛋白质相互作用(PPI)网络选择最频繁相互作用的核心基因。机器学习应用于寻找与SD相关的特征基因。采用CIBERSORT方法探讨免疫微环境的变化。
UNASSIGNED:总共10个核心基因(MYL1,TNNC2,TNNT3,TCAP,获得TNNC1,TPM2,MYL2,TNNI1,ACTA1,CKM),它们主要与心肌炎有关,肥厚性心肌炎和扩张型心肌病(DCM)。通过机器学习建立了与SD相关的MYL2和TNNT3的特征基因。SD前后免疫微环境无明显变化。
UNASSIGNED:检测特征基因有助于识别SD高危患者并推测死亡原因。
UNASSIGNED: To search for significant biomarkers associated with sudden death (SD).
UNASSIGNED: Differential genes were screened by comparing the whole blood samples from 15 cases of accidental death (AD) and 88 cases of SD. The protein-protein interaction (PPI) network selects core genes that interact most frequently. Machine learning is applied to find characteristic genes related to SD. The CIBERSORT method was used to explore the immune-microenvironment changes.
UNASSIGNED: A total of 10 core genes (MYL1, TNNC2, TNNT3, TCAP, TNNC1, TPM2, MYL2, TNNI1, ACTA1, CKM) were obtained and they were mainly related to myocarditis, hypertrophic myocarditis and dilated cardiomyopathy (DCM). Characteristic genes of MYL2 and TNNT3 associated with SD were established by machine learning. There was no significant change in the immune-microenvironment before and after SD.
UNASSIGNED: Detecting characteristic genes is helpful to identify patients at high risk of SD and speculate the cause of death.