1.武汉大学中南医院 心血管内科, 湖北 武汉 430071
2.武汉大学中南医院 综合医疗科, 湖北 武汉 430071
凌懿,女,1998—,医学硕士,主要从事心力衰竭及心律失常相关研究,E⁃mail: 1083736834@qq.com
王扬淦,男,1963—,医学博士,主任医师,教授,博士生导师,主要从事心律失常、电生理及心力衰竭等方面的研究,E⁃mail:wb000813@whu.edu.cn
收稿:2024-03-12,
纸质出版:2025-10-25
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凌懿,梁晶晶,潘治豪等.基于B细胞相关基因构建心力衰竭多基因诊断列线图[J].武汉大学学报(医学版),2025,46(10):1282-1292.
LING Yi,LIANG Jingjing,PAN Zhihao,et al.Constructing a multigene diagnostic nomogram for heart failure based on B cell⁃related genes[J].Medical Journal of Wuhan University,2025,46(10):1282-1292.
凌懿,梁晶晶,潘治豪等.基于B细胞相关基因构建心力衰竭多基因诊断列线图[J].武汉大学学报(医学版),2025,46(10):1282-1292. DOI: 10.14188/j.1671-8852.2024.0161.
LING Yi,LIANG Jingjing,PAN Zhihao,et al.Constructing a multigene diagnostic nomogram for heart failure based on B cell⁃related genes[J].Medical Journal of Wuhan University,2025,46(10):1282-1292. DOI: 10.14188/j.1671-8852.2024.0161.
目的
2
运用生物信息学和分子生物学方法探究B细胞相关基因在心力衰竭(HF)中的诊断价值。
方法
2
应用CD19免疫荧光检测HF模型小鼠心脏B细胞浸润水平。对26例HF患者心脏组织单细胞测序结果分析确定B细胞相关基因。从GEO数据库下载6个HF相关microarray测序数据集及2个HF相关RNA⁃seq测序数据集,分别将其合并作为训练集和验证集。使用差异基因分析、PPI网络分析、逻辑回归、BORUTA算法、LASSO回归及SVM⁃RFE算法进行特征选择。利用多因素逻辑回归构建诊断列线图,并通过ROC、Calibration以及DCA分析其可信度。通过多重免疫荧光验证结论。
结果
2
与对照组相比,HF组心脏B细胞浸润水平上升(
P
0.05)。单细胞测序分析发现125个B细胞相关基因,其中86个在组间差异表达。通过多种算法分析,诊断列线图包括CCL5、CD74、ITGB2、KLRD1和SELL 5个基因。该列线图在训练集及验证集中均被证实为一个可靠的HF诊断工具。免疫荧光显示,HF组小鼠心肌组织具有更高水平CD19
+
KLRD1
+
B细胞浸润(
P
0.05)。
结论
2
基于B细胞相关基因构建了诊断HF的5基因列线图,为阐明HF的发病机制提供了一个潜在的工具和可能的切入点。
Objective
2
To comprehensively apply bioinformatics and molecular biology methods to explore the diagnostic value of B cell⁃related genes in heart failure (HF).
Methods
2
CD19 immunofluorescence staining was used to detect the B cell infiltration level in HF model mice's hearts. Single⁃cell RNA sequencing (scRNA⁃seq) data from the heart tissues of 26 HF patients were analyzed to determine B cell⁃related genes. Six microarray and two RNA⁃seq data sets related to HF were downloaded from the Gene Expression Omnibus (GEO) database and merged as training and validation sets
respectively. Differential gene analysis
protein⁃protein interaction (PPI) network analysis
logistic regression
Boruta algorithm
LASSO regression
and support vector machine recursive feature elimination (SVM⁃RFE) algorithms were used for feature selection. A diagnostic nomogram was constructed using multiple⁃factor logistic regression
and its reliability was assessed through receiver operating characteristic (ROC)
calibration
and decision curve analysis (DCA). The results were validated by multiplex immunofluorescence. The results were validated by multiplex immunofluorescence.
Results
2
Compared with that in the control group
the level of B cell infiltration in the heart of HF patients was increased (
P
0.05). Single⁃cell RNA sequencing analysis identified 125 B cell⁃related genes
of which 86 were differentially expressed between groups. Analysis using various algorithms revealed that the diagnostic nomogram included five genes: CCL5
CD74
ITGB2
KLRD1
and SELL. The nomogram was confirmed to be a reliable HF diagnostic tool in the training and validation sets. Immunofluorescence showed that HF model mice had higher CD19
+
KLRD1
+
B cell infiltration levels in myocardial tissue (
P
0.05).
Conclusion
2
A 5⁃gene diagnostic nomogram for HF was constructed based on B cell⁃related genes
providing a potential tool and a possible entry point for elucidating the pathogenesis of HF.
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