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

Hereditas

Research

Lingyan Jin¹  Qiaozhu Wang¹  Peng Liu¹  Qiangsun Zheng¹  Yixin Wang¹  Xinghua Qin² 

[1] Department of Cardiology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China;Xi’an Key Laboratory of Special Medicine and Health Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China;

关键词: Atrial fibrillation;    Cuproptosis;    Diagnostic signature;    Bioinformatics;    Ensemble learning;   

DOI  :  10.1186/s41065-023-00297-6

 received in 2023-03-22, accepted in 2023-08-02,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAtrial 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.ResultsTwo 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.ConclusionsThis 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.

【 授权许可】

CC BY   
© Mendelian Society of Lund and BioMed Central Ltd. 2023

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