Yadalam Pradeep Kumar, Chatterjee Shubhangini, Natarajan Prabhu Manickam, Ardila Carlos M
Department of Periodontics, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
Department of Clinical Sciences, Center of Medical and Bio-allied Health and Research, College of Dentistry, Ajman University, Ajman, United Arab Emirates.
Front Oral Health. 2025 Mar 4;6:1463458. doi: 10.3389/froh.2025.1463458. eCollection 2025.
INTRODUCTION: and Treponema species have been found to invade the central nervous system through virulence factors, causing inflammation and influencing the host immune response. interacts with astrocytes, microglia, and neurons, leading to neuroinflammation. and may also play a role in the development of Alzheimer's disease. Interactomic hub genes, central to protein-protein interaction networks, are vulnerable to perturbations, leading to diseases such as cancer, neurodegenerative disorders, and cardiovascular diseases. Machine learning can identify differentially expressed hub genes in specific conditions or diseases, providing insights into disease mechanisms and developing new therapeutic approaches. This study compares the performance of light gradient boosting and logistic regression in identifying interactomic hub genes in and -induced periodontitis with those in Alzheimer's disease. METHODS: Using the GSE222136 dataset, we analyzed differential gene expression in periodontitis and Alzheimer's disease. The GEO2R tool was used to identify differentially expressed genes under different conditions, providing insights into molecular mechanisms. Bioinformatics tools such as Cytoscape and CytoHubba were employed to create gene expression networks to identify hub genes. Logistic regression and light gradient boosting were used to predict interactomic hub genes, with outliers removed and machine learning algorithms applied. RESULTS: The data were cross-validated and divided into training and testing segments. The top hub genes identified were TNFRSF9, LZIC, TNFRSF8, SLC45A1, GPR157, and SLC25A33, which are induced by and and are responsible for endothelial dysfunction in brain cells. The accuracy of logistic regression and light gradient boosting was 67% and 60%, respectively. DISCUSSION: The logistic regression model demonstrated superior accuracy and balance compared to the light gradient boosting model, indicating its potential for future improvements in predicting hub genes in periodontal and Alzheimer's diseases.
引言：已发现梅毒螺旋体属物种通过毒力因子侵入中枢神经系统，引发炎症并影响宿主免疫反应。[具体物质1]与星形胶质细胞、小胶质细胞和神经元相互作用，导致神经炎症。[具体物质1]和[具体物质2]也可能在阿尔茨海默病的发展中起作用。相互作用组中心基因是蛋白质 - 蛋白质相互作用网络的核心，易受干扰，从而导致癌症、神经退行性疾病和心血管疾病等疾病。机器学习可以识别特定条件或疾病中差异表达的中心基因，为疾病机制提供见解并开发新的治疗方法。本研究比较了轻梯度提升和逻辑回归在识别[具体物质1]和[具体物质2]诱导的牙周炎与阿尔茨海默病中相互作用组中心基因方面的性能。 方法：使用GSE222136数据集，我们分析了牙周炎和阿尔茨海默病中的差异基因表达。GEO2R工具用于识别不同条件下的差异表达基因，以深入了解分子机制。使用Cytoscape和CytoHubba等生物信息学工具创建基因表达网络以识别中心基因。使用逻辑回归和轻梯度提升来预测相互作用组中心基因，去除异常值并应用机器学习算法。 结果：数据进行了交叉验证并分为训练和测试部分。确定的顶级中心基因是TNFRSF9、LZIC、TNFRSF8、SLC45A1、GPR157和SLC25A33，它们由[具体物质1]和[具体物质2]诱导，并负责脑细胞中的内皮功能障碍。逻辑回归和轻梯度提升的准确率分别为67%和60%。 讨论：与轻梯度提升模型相比，逻辑回归模型表现出更高的准确率和平衡性，表明其在未来预测牙周病和阿尔茨海默病中心基因方面具有改进的潜力。
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