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基于随机森林和人工神经网络构建种植体周炎的诊断模型
杨浩然, 陈宇翔, 赵安娜, 程婷婷, 周建忠, 李自良
PDF(3904 KB)
PDF(3904 KB)
基于随机森林和人工神经网络构建种植体周炎的诊断模型
), 陈宇翔1,2, 赵安娜1,2, 程婷婷1,2, 周建忠1,2, 李自良1,2()Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis
), Chen Yuxiang1,2, Zhao Anna1,2, Cheng Tingting1,2, Zhou Jianzhong1,2, Li Ziliang1,2()目的 本研究旨在揭示种植体周炎发生发展过程中参与调控的关键基因，并通过随机森林（RF）和人工神经网络（ANN）构建种植体周炎的诊断模型。 方法 本研究从GEO数据库中获取GSE33774、GSE106090和GSE57631数据集。对GSE33774和GSE106090数据集进行差异表达和功能富集分析，通过蛋白质互作网络（PPI）和RF筛选出关键基因，利用ANN建立种植体周炎的诊断模型，并在GSE33774和GSE57631数据集中进行验证。同时，构建转录因子-基因相互作用网络和转录因子-微小RNA（miRNA）调控网络。 结果 本研究共筛选出124个参与调控种植体周炎的差异表达基因（DEGs）。富集分析结果表明，DEGs主要和免疫受体活性蛋白及细胞因子受体活性相关，主要参与白细胞和中性粒细胞迁移的过程。PPI和RF筛选出6个关键基因，分别为CD38、CYBB、FCGR2A、SELL、TLR4和CXCL8。受试者操作特征曲线（ROC）表明ANN模型具有较好的诊断性。本研究还发现FOXC1、GATA2和NF-κB1可能是种植体周炎中重要的转录因子，hsa-miR-204可能是关键的miRNA。 结论 RF和ANN构建的种植体周炎的诊断模型可信度高，CD38、CYBB、FCGR2A、SELL、TLR4和CXCL8是潜在的诊断标志物。FOXC1、GATA2和NF-κB1可能是种植体周炎中重要的转录因子，hsa-miR-204作为关键的miRNA在其中扮演着重要角色。
Objective This study aimed to reveal critical genes regulating peri-implantitis during its development and construct a diagnostic model by using random forest (RF) and artificial neural network (ANN). Methods GSE-33774, GSE106090, and GSE57631 datasets were obtained from the GEO database. The GSE33774 and GSE106090 datasets were analyzed for differential expression and functional enrichment. The protein-protein interaction networks (PPI) and RF screened vital genes. A diagnostic model for peri-implantitis was established using ANN and validated on the GSE33774 and GSE57631 datasets. A transcription factor-gene interaction network and a transcription factor-micro-RNA (miRNA) regulatory network were also established. Results A total of 124 differentially expressed genes (DEGs) involved in the regulation of peri-implantitis were screened. Enrichment analysis showed that DEGs were mainly associated with immune receptor activity and cytokine receptor activity and were mainly involved in processes such as leukocyte and neutrophil migration. The PPI and RF screened six essential genes, namely, CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8. The receiver operating characteristic curve (ROC) indicated that the ANN model had an excellent diagnostic performance. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 may be a key miRNA. Conclusion The diagnostic model of peri-implantitis constructed by RF and ANN has high confidence, and CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8 are potential diagnostic markers. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 plays a vital role as a critical miRNA.
种植体周炎 / 生物信息学 / 随机森林 / 人工神经网络 / 诊断模型
peri-implantitis / bioinformatics / random forest / artificial neural network / diagnostic model
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