Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning.

IF 2.7 3区 生物学
Yixin Wang, Qiaozhu Wang, Peng Liu, Lingyan Jin, Xinghua Qin, Qiangsun Zheng
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引用次数: 0

Abstract

Background: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations suggest that cuproptosis may also contribute to AF. This investigation aimed to identify a novel diagnostic gene signature associated with cuproptosis for AF using ensemble learning methods and discover the connection between AF and cuproptosis.

Results: Two genes connected to cuproptosis, including solute carrier family 31 member 1 (SLC31A1) and lipoic acid synthetase (LIAS), were selected by integration of random forests and eXtreme Gradient Boosting algorithms. Subsequently, a diagnostic model was constructed that includes the two genes for AF using the Light Gradient Boosting Machine (LightGBM) algorithm with good performance (the area under the curve value > 0.75). The microRNA-transcription factor-messenger RNA network revealed that homeobox A9 (HOXA9) and Tet methylcytosine dioxygenase 1 (TET1) could target SLC31A1 and LIAS in AF. Functional enrichment analysis indicated that cuproptosis might be connected to immunocyte activities. Immunocyte infiltration analysis using the CIBERSORT algorithm suggested a greater level of neutrophils in the AF group. According to the outcomes of Spearman's rank correlation analysis, there was a negative relation between SLC31A1 and resting dendritic cells and eosinophils. The study found a positive relationship between LIAS and eosinophils along with resting memory CD4+ T cells. Conversely, a negative correlation was detected between LIAS and CD8+ T cells and regulatory T cells.

Conclusions: This study successfully constructed a cuproptosis-related diagnostic model for AF based on the LightGBM algorithm and validated its diagnostic efficacy. Cuproptosis may be regulated by HOXA9 and TET1 in AF. Cuproptosis might interact with infiltrating immunocytes in AF.

Abstract Image

Abstract Image

Abstract Image

基于集成学习的房颤房颤相关诊断基因签名的构建与验证。
背景:房颤是最常见的心律失常类型。尽管如此,当依靠传统的诊断技术时,这种情况的准确诊断仍然是一个挑战。细胞死亡是房颤发病的一个关键因素。现有研究表明,房颤铜倾也可能与房颤有关。本研究旨在利用集成学习方法识别与房颤铜倾相关的新的诊断基因特征,并发现房颤与铜倾之间的联系。结果:通过随机森林整合和极端梯度增强算法,筛选到溶质载体家族31成员1 (SLC31A1)和硫辛酸合成酶(LIAS)两个与铜原生化相关的基因。随后,利用性能较好的光梯度增强机(Light Gradient Boosting Machine, LightGBM)算法(曲线下面积> 0.75)构建了包含两个AF基因的诊断模型。microrna -转录因子-信使RNA网络显示同源盒A9 (HOXA9)和Tet甲基胞嘧啶双加氧酶1 (TET1)可以靶向AF中的SLC31A1和LIAS。功能富集分析表明cuprotosis可能与免疫细胞活性有关。使用CIBERSORT算法进行免疫细胞浸润分析表明,AF组中性粒细胞水平较高。Spearman秩相关分析结果显示,SLC31A1与静息树突状细胞和嗜酸性粒细胞呈负相关。研究发现LIAS与嗜酸性粒细胞以及静息记忆CD4+ T细胞呈正相关。相反,LIAS与CD8+ T细胞和调节性T细胞呈负相关。结论:本研究成功构建了基于LightGBM算法的房颤畸形相关诊断模型,并验证了其诊断效果。房颤中的铜倾可能受HOXA9和TET1的调控,铜倾可能与浸润性免疫细胞相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Hereditas
Hereditas Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
3.80
自引率
3.70%
发文量
0
期刊介绍: For almost a century, Hereditas has published original cutting-edge research and reviews. As the Official journal of the Mendelian Society of Lund, the journal welcomes research from across all areas of genetics and genomics. Topics of interest include human and medical genetics, animal and plant genetics, microbial genetics, agriculture and bioinformatics.
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