Department of Cardiology, Wuxi People's Hospital Affiliated to Nanjing Medical University, No. 299, Qingyang Road, Wuxi, 214023, China.
BMC Med Genomics. 2022 Mar 19;15(1):64. doi: 10.1186/s12920-022-01212-0.
We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail.
Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using "limma" package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells.
129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells.
In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.
本研究旨在基于机器学习方法筛选房颤（AF）的生物标志物，并详细评估 AF 患者的免疫浸润程度。
从基因表达综合数据库（GEO）中下载了两个与 AF 相关的数据集（GSE41177 和 GSE79768），并对其进行合并以进一步分析。使用 R 软件中的“limma”包筛选差异表达基因（DEGs）。使用 LASSO 回归算法和 SVM-RFE 算法的机器学习方法识别 AF 的候选生物标志物。采用受试者工作特征（ROC）曲线评估生物标志物的诊断效能，并在另一个独立的 GSE14975 验证数据集中进行验证。此外，我们使用 CIBERSORT 研究了每个样本中浸润免疫细胞的比例，并采用 Spearman 方法探讨了生物标志物与免疫细胞之间的相关性。
共鉴定出 129 个 DEGs，通过 LASSO 回归和 SVM-RFE 算法，鉴定出 CYBB、CXCR2 和 S100A4 为 AF 的关键生物标志物。在训练数据集和验证数据集中，CYBB、CXCR2 和 S100A4 均显示出良好的诊断效能。免疫浸润分析表明，与窦性节律（SR）相比，AF 患者的心房样本中 T 细胞γδ、静止期中性粒细胞和肥大细胞的含量较高，而滤泡辅助性 T 细胞的含量较低。相关性分析表明，CYBB、CXCR2 和 S100A4 与浸润免疫细胞显著相关。
总之，本研究表明，CYBB、CXCR2 和 S100A4 是与浸润免疫细胞相关的 AF 的关键生物标志物，浸润免疫细胞在 AF 中发挥着重要作用。
