通过机器学习发现的心脏性猝死潜在预后生物标志物。

Potential prognostic biomarkers of sudden cardiac death discovered by machine learning.

作者信息

Zhou Kena, Cai Congbo, He Yi, Chen Zhihua

机构信息

Gastroenterology Department of Ningbo No.9 Hospital, Ningbo, Zhejiang, 315000, China.

Emergency Department of Yinzhou No.2 Hospital, Ningbo, Zhejiang, 315000, China.

出版信息

Comput Biol Med. 2022 Nov;150:106154. doi: 10.1016/j.compbiomed.2022.106154. Epub 2022 Sep 29.

DOI:10.1016/j.compbiomed.2022.106154

PMID:36208596

Abstract

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.

摘要

目的

心源性猝死（SCD）是一项严重的公共卫生负担。本研究旨在利用机器学习寻找SCD的预后生物标志物。

方法

比较21例意外死亡供体和82例猝死供体的心肌样本以寻找差异基因。根据蛋白质-蛋白质相互作用（PPI）网络找到富集的活性基因。基因集富集分析（GSEA）分析对照组和实验组之间功能和通路的差异。活性基因引起的相关疾病主要通过疾病本体（DO）富集展示。通过两种机器学习算法鉴定SCD的预后生物标志物。使用CIBERSORT方法比较SCD患者的免疫微环境变化。