Table_3_Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks.docx

BackgroundAlthough advanced surgical and interventional treatments are available for advanced aortic valve calcification (AVC) with severe clinical symptoms, early diagnosis, and intervention is critical in order to reduce calcification progression and improve patient prognosis. The aim of this study was to develop therapeutic targets for improving outcomes for patients with AVC.
Materials and methodsWe used the public expression profiles of individuals with AVC (GSE12644 and GSE51472) to identify potential diagnostic markers. First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. Subsequently, artificial neural networks were employed to filter and model the diagnostic characteristics for AVC incidence. The diagnostic values were determined using the receiver operating characteristic (ROC) curves. Furthermore, CIBERSORT immune infiltration analysis was used to determine the expression of different immune cells in the AVC. Finally, the CMap database was used to predict candidate small compounds as prospective AVC therapeutics.
ResultsA total of 78 strong DEGs were identified. The leukocyte migration and pid integrin 1 pathways were highly enriched for AVC-specific DEGs. CXCL16, GPM6A, BEX2, S100A9, and SCARA5 genes were all regarded diagnostic markers for AVC. The model was effectively constructed using a molecular diagnostic score system with significant diagnostic value (AUC = 0.987) and verified using the independent dataset GSE83453 (AUC = 0.986). Immune cell infiltration research revealed that B cell naive, B cell memory, plasma cells, NK cell activated, monocytes, and macrophage M0 may be involved in the development of AVC. Additionally, all diagnostic characteristics may have varying degrees of correlation with immune cells. The most promising small molecule medicines for reversing AVC gene expression are Doxazosin and Terfenadine.
ConclusionIt was identified that CXCL16, GPM6A, BEX2, S100A9, and SCARA5 are potentially beneficial for diagnosing and treating AVC. A diagnostic model was constructed based on a molecular prognostic score system using machine learning. The aforementioned immune cell infiltration may have a significant influence on the development and incidence of AVC.

背景
尽管针对伴严重临床症状的晚期主动脉瓣钙化（aortic valve calcification, AVC）已有先进的外科及介入治疗手段，但早期诊断与干预对于延缓钙化进展、改善患者预后至关重要。本研究旨在探寻可改善主动脉瓣钙化患者预后的治疗靶点。

材料与方法
本研究使用主动脉瓣钙化患者的公共表达谱数据集（GSE12644与GSE51472）筛选潜在诊断标志物。首先，借助R软件识别差异表达基因（differentially expressed genes, DEGs）并开展功能富集分析；随后，整合生物信息学技术与随机森林算法、支持向量机等机器学习方法，筛选并鉴定主动脉瓣钙化的诊断标志物；继而采用人工神经网络对主动脉瓣钙化发病的诊断特征进行筛选与建模；通过受试者工作特征（receiver operating characteristic, ROC）曲线评估诊断效能；进一步利用CIBERSORT免疫浸润分析，明确主动脉瓣钙化组织中不同免疫细胞的表达情况；最终通过CMap数据库预测可作为主动脉瓣钙化潜在治疗药物的候选小分子化合物。

结果
本研究共鉴定得到78个显著差异表达基因。白细胞迁移与pid整合素1通路在主动脉瓣钙化特异性差异表达基因中显著富集。CXCL16、GPM6A、BEX2、S100A9及SCARA5基因均被认定为主动脉瓣钙化的诊断标志物。本研究基于分子诊断评分系统成功构建诊断模型，该模型具有良好的诊断效能（曲线下面积AUC=0.987），并通过独立数据集GSE83453完成验证（AUC=0.986）。免疫浸润分析显示，初始B细胞、记忆B细胞、浆细胞、活化自然杀伤细胞、单核细胞及M0型巨噬细胞可能参与主动脉瓣钙化的发生发展；此外，所有诊断特征均与免疫细胞存在不同程度的相关性。多沙唑嗪（Doxazosin）与特非那定（Terfenadine）是目前最具潜力的可逆转主动脉瓣钙化相关基因表达的小分子药物。

结论
本研究证实CXCL16、GPM6A、BEX2、S100A9及SCARA5有望用于主动脉瓣钙化的诊断与治疗。本研究基于机器学习构建了基于分子预后评分系统的诊断模型。上述免疫浸润特征可能对主动脉瓣钙化的发生与发展具有重要影响。