Integrated bioinformatics analysis and machine learning approach for the identification of immune-related genes in the diagnosis of aortic valve calcification with periodontitis

Duolikun Mutailifu, Abudousaimi Aini, Abudunaibi Maimaitiaili
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引用次数: 0

Abstract

Background

Aortic valve calcification (AVC) represents a progressive, age-associated disorder characterized by substantial mortality, yet effective early diagnostic markers for AVC complicated by periodontitis, a common inflammatory condition linked to systemic inflammation, remain elusive. Our investigation sought to uncover immune-specific molecular indicators for AVC in patients with periodontitis using bioinformatics and machine learning.

Methods

Gene expression data for AVC (utilizing datasets GSE153555, GSE148219, GSE51472) and periodontitis (from dataset GSE16134) underwent analysis. We identified differentially expressed genes (DEGs) and determined the overlapped genes between AVC and periodontitis. The study included functional enrichment, protein-protein interaction (PPI) network construction, and immune infiltration analyses. To screen potential target genes, four machine learning models were developed (SVM, RF, XGB, GLM), with validation performed using an external dataset and clinical specimens via qRT-PCR.

Results

A total of 30 intersecting genes between AVC and periodontitis were identified. Four key genes—CXCL12, HCST, ITGA4, and GZMK—were selected through machine learning. The nomogram model combining these genes demonstrated high diagnostic accuracy, with an AUC of 0.985 in the training set and AUC values of 0.8, 0.72, 0.88, and 0.76 for HCST, ITGA4, CXCL12, and GZMK, respectively, in the external validation using the GSE51472 dataset. qRT-PCR validation in clinical samples confirmed that these genes were significantly upregulated in AVC patients with periodontitis. These genes were also correlated with immune cell infiltration, suggesting their potential role in AVC pathogenesis.

Conclusion

These findings provide new clinical molecular diagnostics, treatment related molecular markers for AVC in patients with periodontitis and may facilitate further basic research into biological functions.
综合生物信息学分析和机器学习方法在牙周炎主动脉瓣钙化诊断中的免疫相关基因鉴定
主动脉瓣钙化(AVC)是一种进行性、与年龄相关的疾病,其特点是死亡率高,但AVC合并牙周炎(一种与全身性炎症相关的常见炎症)的有效早期诊断标志物仍然难以找到。我们的研究旨在利用生物信息学和机器学习揭示牙周炎患者AVC的免疫特异性分子指标。方法分析AVC(数据集GSE153555、GSE148219、GSE51472)和牙周炎(数据集GSE16134)的基因表达数据。我们鉴定了差异表达基因(DEGs),并确定了AVC和牙周炎之间的重叠基因。研究包括功能富集、蛋白相互作用(PPI)网络构建和免疫浸润分析。为了筛选潜在的靶基因,开发了四种机器学习模型(SVM, RF, XGB, GLM),并通过qRT-PCR使用外部数据集和临床标本进行验证。结果共鉴定出30个AVC与牙周炎的交叉基因。通过机器学习选择4个关键基因:cxcl12、HCST、ITGA4和gzmk。结合这些基因的nomogram model显示出较高的诊断准确率,在训练集中的AUC为0.985,在使用GSE51472数据集进行外部验证时,HCST、ITGA4、CXCL12和GZMK的AUC分别为0.8、0.72、0.88和0.76。临床样本的qRT-PCR验证证实,这些基因在伴有牙周炎的AVC患者中显著上调。这些基因也与免疫细胞浸润相关,提示它们在AVC发病机制中的潜在作用。结论本研究结果为牙周炎患者AVC提供了新的临床分子诊断和治疗相关分子标志物,并为进一步开展AVC生物学功能的基础研究奠定了基础。
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