基于跨独立数据集的综合分析鉴定新型房颤风险 4 基因诊断模型。

Identification of a Novel 4-gene Diagnostic Model for Atrial Fibrillation Risk Based on Integrated Analysis Across Independent Data Sets.

机构信息

Department of Cardiology, Shandong Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012,China.

Department of Cardiovascular Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, 250012,China.

出版信息

Comb Chem High Throughput Screen. 2022;25(2):229-240. doi: 10.2174/1386207324666210121103304.

DOI:10.2174/1386207324666210121103304

PMID:33475065

Abstract

BACKGROUND

Atrial fibrillation (AF) is the most common persistent arrhythmia and an important factor leading to cardiovascular morbidity and mortality. Several key genes and diagnostic markers have been discovered with the development of advanced modern molecular biology techniques, but the etiology and pathogenesis of AF remained unknown.

METHODS

In this study, three-chip-seq data sets and an RNA-seq data set were integrated as a comprehensive network for pathway analysis of the biological functions of related genes in AF, hoping to provide a better understanding of the etiology and pathogenesis of AF.

RESULTS

Differential co-expression analysis identified 360 genes with specific expression in AF, and functional enrichment analysis further revealed that these genes were significantly correlated with focal expression (p <0.01), autophagy (p <0.01), and thyroid cancer. In addition, Af-specific proteinprotein interaction (PPI) networks were constructed based on AF-specific expression genes. Network topology analysis identified PLEKHA7, YWHAQ, PPP1CB, WDR1, AKT1, IGF1R, CANX, MAPK1, SRPK2 and SRSF10 genes as hub genes of the networks, and they were considered as potential biomarkers of AF because they were found to participate in the development of AF through Oocyte meiosis and focal expression. Finally, a diagnostic model for AF established with a support vector machine (SVM) demonstrated excellent predictive performance in internal and external data sets (AUC>0.9) and different platform data sets (mean AUC>0.75).

CONCLUSION

Finally, a diagnostic model for AF was established, thus showing its potential in the early identification and prediction of AF.

摘要

背景

心房颤动（AF）是最常见的持续性心律失常，也是导致心血管发病率和死亡率的重要因素。随着先进的现代分子生物学技术的发展，已经发现了几个关键基因和诊断标志物，但 AF 的病因和发病机制仍不清楚。

方法

本研究整合了三个芯片-seq 数据集和一个 RNA-seq 数据集，作为 AF 相关基因生物功能通路分析的综合网络，希望能更好地了解 AF 的病因和发病机制。

结果

差异共表达分析确定了 360 个在 AF 中具有特定表达的基因，功能富集分析进一步表明，这些基因与局灶性表达（p<0.01）、自噬（p<0.01）和甲状腺癌显著相关。此外，还基于 AF 特异性表达基因构建了 Af 特异性蛋白质-蛋白质相互作用（PPI）网络。网络拓扑分析确定了 PLEKHA7、YWHAQ、PPP1CB、WDR1、AKT1、IGF1R、CANX、MAPK1、SRPK2 和 SRSF10 等基因是网络的枢纽基因，它们被认为是 AF 的潜在生物标志物，因为它们通过卵母细胞减数分裂和局灶性表达参与了 AF 的发展。最后，使用支持向量机（SVM）建立的 AF 诊断模型在内部和外部数据集（AUC>0.9）以及不同平台数据集（平均 AUC>0.75）中表现出优异的预测性能。