DataSheet2_Potential biomarkers and immune cell infiltration involved in aortic valve calcification identified through integrated bioinformatics analysis.PDF

Background: Calcific aortic valve disease (CAVD) is the most common valvular heart disease in the aging population, resulting in a significant health and economic burden worldwide, but its underlying diagnostic biomarkers and pathophysiological mechanisms are not fully understood.
Methods: Three publicly available gene expression profiles (GSE12644, GSE51472, and GSE77287) from human Calcific aortic valve disease (CAVD) and normal aortic valve samples were downloaded from the Gene Expression Omnibus database for combined analysis. R software was used to identify differentially expressed genes (DEGs) and conduct functional investigations. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify key feature genes as potential biomarkers for Calcific aortic valve disease (CAVD). Receiver operating characteristic (ROC) curves were used to evaluate the discriminatory ability of key genes. The CIBERSORT deconvolution algorithm was used to determine differential immune cell infiltration and the relationship between key genes and immune cell types. Finally, the Expression level and diagnostic ability of the identified biomarkers were further validated in an external dataset (GSE83453), a single-cell sequencing dataset (SRP222100), and immunohistochemical staining of human clinical tissue samples, respectively.
Results: In total, 34 identified DEGs included 21 upregulated and 13 downregulated genes. DEGs were mainly involved in immune-related pathways such as leukocyte migration, granulocyte chemotaxis, cytokine activity, and IL-17 signaling. The machine learning algorithm identified SCG2 and CCL19 as key feature genes [area under the ROC curve (AUC) = 0.940 and 0.913, respectively; validation AUC = 0.917 and 0.903, respectively]. CIBERSORT analysis indicated that the proportion of immune cells in Calcific aortic valve disease (CAVD) was different from that in normal aortic valve tissues, specifically M2 and M0 macrophages. Key genes SCG2 and CCL19 were significantly positively correlated with M0 macrophages. Single-cell sequencing analysis and immunohistochemical staining of human aortic valve tissue samples showed that SCG2 and CCL19 were increased in Calcific aortic valve disease (CAVD) valves.
Conclusion: SCG2 and CCL19 are potential novel biomarkers of Calcific aortic valve disease (CAVD) and may play important roles in the biological process of Calcific aortic valve disease (CAVD). Our findings advance understanding of the underlying mechanisms of Calcific aortic valve disease (CAVD) pathogenesis and provide valuable information for future research into novel diagnostic and immunotherapeutic targets for Calcific aortic valve disease (CAVD).

背景：钙化性主动脉瓣疾病（Calcific aortic valve disease, CAVD）是老年人群中最常见的瓣膜性心脏病，在全球范围内造成了沉重的健康与经济负担，但其潜在的诊断生物标志物及病理生理机制尚未完全阐明。
方法：本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）下载了3套公开的人类钙化性主动脉瓣疾病（Calcific aortic valve disease, CAVD）与正常主动脉瓣样本的基因表达谱（GSE12644、GSE51472及GSE77287）进行联合分析。使用R软件筛选差异表达基因（differentially expressed genes, DEGs）并开展功能富集分析。采用两种机器学习算法——最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）与支持向量机-递归特征消除（support vector machine-recursive feature elimination, SVM-RFE），以识别关键特征基因作为钙化性主动脉瓣疾病（CAVD）的潜在生物标志物。通过受试者工作特征（Receiver operating characteristic, ROC）曲线评估关键基因的诊断区分能力。运用CIBERSORT反卷积算法分析免疫细胞浸润差异，并明确关键基因与免疫细胞类型间的关联。最后，分别在外部数据集GSE83453、单细胞测序数据集SRP222100以及人类临床组织样本的免疫组化染色结果中，对所鉴定的生物标志物的表达水平与诊断效能进行验证。
结果：最终共筛选得到34个差异表达基因，其中21个上调基因、13个下调基因。差异表达基因主要富集于白细胞迁移、粒细胞趋化、细胞因子活性、IL-17信号通路等免疫相关通路。经机器学习算法鉴定，SCG2与CCL19为关键特征基因，其ROC曲线下面积（area under the ROC curve, AUC）分别为0.940与0.913；验证集AUC分别为0.917与0.903。CIBERSORT分析显示，钙化性主动脉瓣疾病（CAVD）组织与正常主动脉瓣组织的免疫细胞浸润比例存在显著差异，具体表现为M2型与M0型巨噬细胞的异常分布。关键基因SCG2与CCL19与M0型巨噬细胞呈显著正相关。单细胞测序分析及人类主动脉瓣组织样本的免疫组化染色结果显示，SCG2与CCL19在钙化性主动脉瓣疾病（CAVD）瓣膜组织中表达上调。
结论：SCG2与CCL19可作为钙化性主动脉瓣疾病（CAVD）潜在的新型生物标志物，并可能在该病的生物学进程中发挥重要作用。本研究加深了对钙化性主动脉瓣疾病（CAVD）发病机制的理解，为未来探索该病的新型诊断靶点与免疫治疗策略提供了有价值的参考依据。