基于机器学习和生物信息学分析的脂肪酸代谢相关基因在牙周炎中的作用研究
Role of fatty acid metabolism-related genes in periodontitis based on machine learning and bioinformatics analysis
作者：陈宇翔, 赵安娜, 杨浩然, 杨霞, 程婷婷, 饶先琦, 李自良
Author：Chen Yuxiang, Zhao Anna, Yang Haoran, Yang Xia, Cheng Tingting, Rao Xianqi, Li Ziliang
收稿日期：2024-06-02 年卷（期）页码：2024,42(6):735-735-747
期刊名称：华西口腔医学杂志
Journal Name：West China Journal of Stomatology
关键字：牙周炎,脂肪酸代谢,生物信息学,机器学习,免疫浸润,
Key words：periodontitis,fatty acid metabolism,bioinformatics,machine learning,immune infiltration,
基金项目：国家自然科学基金(82360185)
中文摘要
目的 通过机器学习和生物信息学方法研究脂肪酸代谢相关基因在牙周炎中的作用。 方法 从GEO数据库下载牙周炎数据集GSE10334和GSE16134，GeneCards数据库下载脂肪酸代谢相关基因集。通过R语言“limma”包筛选牙周炎中差异表达的脂肪酸代谢相关基因（DEFAMRGs），并进行功能富集和通路分析。进一步用递归特征消除、最小绝对收缩和选择算子和Boruta算法确定枢纽DEFAMRGs，并用其构建诊断模型且进行内部和外部验证。利用一致性聚类分析构建枢纽DEFAMRGs相关的牙周炎亚型。利用CIBERSORT软件分析牙龈组织的免疫细胞浸润，并探究枢纽DEFAMRGs和免疫细胞之间的相关性。 结果 共筛选出113个牙周炎DEFAMRGs。富集分析结果表明，DEFAMRGs主要和免疫炎症反应以及免疫细胞趋化相关。最终确定8个枢纽DEFAMRGs（BTG2、CXCL12、FABP4、CLDN10、PPBP、RGS1、LGALSL和RIF1）并构建了诊断模型（AUC=0.967），基于此将牙周炎分为两个亚型。此外，枢纽DEFAMRGs与不同免疫细胞群体之间存在显著的相关性，其中相关性较高的免疫细胞是肥大细胞和树突状细胞。 结论 该研究为牙周炎的发生发展机制提供新的见解和思路，基于枢纽DEFAMRGs构建的诊断模型可为牙周炎的诊断和治疗提供新的方向。
英文摘要
ObjectiveThis study aims to investigate the role of genes related to fatty acid metabolism in periodontitis through machine learning and bioinformatics methods.MethodsPeriodontitis datasets GSE10334 and GSE-16134 were downloaded from the GEO database, and the fatty acid metabolism-related gene sets were obtained from the GeneCards database. Differentially expressed fatty acid metabolism-related genes (DEFAMRGs) in periodontitis were screened using the “limma” R package. Functional enrichment and pathway analyses were conducted. Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta algorithm were used to determine hub DEFAMRGs and construct diagnostic models with internal and external validation. Subtypes of periodontitis related to hub DEFAMRGs were constructed using consistency clustering analysis. CIBERSORT was used to analyze immune cell infiltration in gingival tissues and explore the correlation between hub DEFAMRGs and immune cells.ResultsA total of 113 periodontitis DEFAMRGs were screened out as a result. The enrichment analysis results indicate that DEFAMRGs are mainly associated with immune inflammatory responses and immune cell chemotaxis.Finally, 8 hub DEFAMRGs (BTG2, CXCL12, FABP4, CLDN10, PPBP, RGS1, LGALSL, and RIF1) were identified and a diagnostic model (AUC=0.967) was constructed, based on which periodontitis was divided into two subtypes. In addition, there is a significant correlation between hub DEFAMRGs and different immune cell populations, with mast cells and dendritic cells showing higher correlation.ConclusionThis study provides new insights and ideas for the occurrence and development mechanism of periodontitis and proposes a diagnostic model based on hub DEFAMRGs to provide new directions for diagnosis and treatment.
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