Why the study?
The study was conducted to identify significant biomarkers associated with sudden death.
Can machine learning identify characteristic genes associated with sudden death from whole blood samples?
Can machine learning identify characteristic genes associated with sudden death from whole blood samples?
Machine learning identified MYL2 and TNNT3 as characteristic genes associated with sudden death, which may help identify high-risk patients and speculate the cause of death.
Objective: To search for significant biomarkers associated with sudden death (SD). Methods: 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. Results: 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. Conclusion: Detecting characteristic genes is helpful to identify patients at high risk of SD and speculate the cause of death.
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Zhou et al. (2022) studied this question.
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