DNA methylation composition related to death risk of coronary heart disease patients and screening method and application thereof
A methylation and coronary heart disease technology, applied in the field of epigenetics, can solve problems such as reducing gene expression levels, and achieve the effects of reducing model dimensions, reducing costs, and improving accuracy
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Embodiment 1
[0038] This embodiment provides a method for screening methylation sites for prognosis diagnosis of coronary heart disease, including the following steps (flow process as follows: figure 1 shown):
[0039] (1) Data of patients with coronary heart disease
[0040] The selected subjects were patients with stable coronary heart disease and non-acute ACS coronary heart disease diagnosed by Guangdong Provincial People's Hospital who received PCI treatment. This protocol was approved by the Ethics Committee of Guangdong Provincial People's Hospital, and each subject signed an informed consent. The cases were collected from January 2010 to December 2013.
[0041] (2) Clinical endpoints and follow-up plan of the study
[0042] The clinical endpoint of this study was death. After the patients are selected, they will be followed up regularly by telephone every 6 months, and they will be asked in detail and recorded whether the patients have adverse cardiovascular events and so on. T...
Embodiment 2
[0063] Example 2 Test data set to evaluate model prediction effect
[0064] The AUC (area under the curve) of the time-dependent ROC curve (Receiver Operating Characteristic curve, receiver operating curve) is used to evaluate the predictive effect of the model constructed in Example 1, and the results are as follows image 3 .
[0065] The value range of AUC is between 0-1, and the larger the AUC, the better the prediction effect of the model. like image 3 , LassoCox regression model AUC in the training set 1年 =0.869, AUC 3年 =0.902, AUC 5年 = 0.879, AUC in the test set 1年 =0.706, AUC 3年 =0.767, AUC 5年 =0.928, indicating that the model predicts the prognosis of patients better.
Embodiment 3
[0066] Example 3 Centrally evaluate the prediction effect of the model through the test data
[0067] According to the M value of these 16 methylation sites and the regression coefficient in the Lasso Cox model, the methylation risk score of the test set samples can be obtained. Whether in the training data set or the test data set, the methylation risk score can well predict the prognosis of patients with coronary heart disease. The MRS of patients with death events is significantly higher than that of patients without events (p Figure 4 . According to the median of the methylation risk score as the cut-off value (median of 5.598 in this example), the patients were divided into high risk group (MRS>5.598) and low risk group with roughly equal number of observations. Risk group (MRS≤5.598). The Kaplan-Meier survival curves of the two groups were drawn respectively, and the Log-rank test was used to test whether there was a significant difference in the survival period between t...
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