Why the study?
Sudden cardiac death is a serious public health burden, prompting the identification of prognostic biomarkers using machine learning.
Can machine learning algorithms identify prognostic biomarkers and immune microenvironment changes associated with sudden cardiac death using myocardial samples?
Can machine learning algorithms identify prognostic biomarkers and immune microenvironment changes associated with sudden cardiac death using myocardial samples?
Machine learning analysis of myocardial samples identified five potential prognostic biomarkers (DEFA1B, BGN, SERPINE1, CCL2, HBB) and highlighted the role of immune infiltration in sudden cardiac death.
Requires validation before clinical use; leaves open prognostic role of these genes in sudden cardiac death.
OBJECTIVE: Sudden cardiac death (SCD) is a serious public health burden. This study aims to find prognostic biomarkers of SCD using machine learning. METHODS: The myocardial samples from 21 accidental death and 82 sudden death donors were compared to seek for differential genes. Enriched active genes were found according to the PPI interaction network. GSEA analyzed differences in function and pathway between control and experimental groups. Related diseases caused by active genes are mainly exhibited through DO enrichment. Prognostic biomarkers for SCD are identified via two machine learning algorithms. The CIBERSORT method was used to compare the immune microenvironment changes in patients with SCD. RESULTS: SCD was mainly associated with heart and kidney diseases caused by atherosclerosis. DEFA1B, BGN, SERPINE1, CCL2 and HBB are considered to be prognostic biomarkers for SCD after machine learning. And immune infiltration plays an important role in the process of SCD. CONCLUSION: We discovered 5 prognostic biomarkers for SCD. And immune microenvironment changes was also found in SCD. Moreover, atherosclerosis might be an important risk factor for SCD.
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Zhou et al. (2022) studied this question.
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