类风湿关节炎脂肪酸代谢相关基因的生物信息学鉴定与验证

背景:研究表明,脂肪酸代谢基因与类风湿关节炎发展紧密相关,因此基于脂肪酸代谢基因探索类风湿关节炎发病进展具有重要的临床意义。目的:探究脂肪酸代谢基因是否可以作为预测类风湿关节炎进展的可靠生物标志物。方法:从基因表达综合数据库(GEO)下载与滑膜组织相关的基因数据,应用STRING构建蛋白质-蛋白质相互作用网络分析,对其使用Cytoscape进行生物学注释(GO基因本体论)和信号通路富集分析(KEGG京都基因与基因组百科全书)。从分子特征数据库(MSigDB)筛选脂肪酸代谢相关基因,使用套索算法和支持向量机的递归特征消除算法筛选潜在生物标志物。通过CIBERSORT算法评估正常人和类风湿关节炎患者的免疫细胞浸润水平。最后,在GSE77298使用受试者工作特征曲线验证脂肪酸代谢相关基因的表达水平。结果与结论:①确定了361个类风湿关节炎差异表达基因,其中13个与报告的脂肪酸代谢相关基因重叠;②基于机器学习算法筛选出5个基因,受试者工作特征曲线显示有5个基因(PCK1、PDK1、PTGS2、PLA2G2D、DPEP2)可以预测类风湿关节炎的发展;③CIBERSORT算法结果表明上述5个基因和活化肥大细胞、中性粒细胞、静息肥大细胞、记忆性静息CD4^(+)T细胞浸润水平密切相关;④受试者工作特征曲线显示,PLA2G2D和PCK1具有较高的诊断价值;⑤提示脂肪酸代谢相关基因表达特征可作为预测类风湿关节炎临床结果的潜在生物标志物,可进一步提高类风湿关节炎预测的准确性。

Bioinformatics identification and validation of genes related to fatty acid metabolism in rheumatoid arthritis

BACKGROUND: Research has shown that fatty acid metabolism genes are closely related to the development of rheumatoid arthritis. Therefore, exploring the progression of rheumatoid arthritis based on fatty acid metabolism genes is of clinical significance. OBJECTIVE: To investigate whether fatty acid metabolism genes can serve as reliable biomarkers for predicting the progression of rheumatoid arthritis. METHODS: Gene data related to synovial tissue were downloaded from the Gene Expression Comprehensive Database (GEO). STRING was used to construct the protein-protein interaction network analysis. Cytoscape was utilized for biological annotation (gene ontology) and signaling pathway enrichment analysis (Kyoto Encyclopedia of Genes and Genomes). Fatty acid metabolism related genes were screened from the molecular feature database (MSigDB). Least absolute shrinkage and selection operator and support vector machine recursive feature elimination feature were used to screen for potential biomarkers. Immune cell infiltration levels in normal individuals and rheumatoid arthritis patients were assessed using the CIBERSORT algorithm. Finally, the expression levels of fatty acid metabolism related genes were verified using the receiver operating characteristic curve in GSE77298. RESULTS AND CONCLUSION: 361 differentially expressed genes in rheumatoid arthritis were identified, of which 13 overlapped with the reported fatty acid metabolism related genes. Based on machine learning algorithms, five genes were selected, and the receiver operating characteristic curve showed that five genes (PCK1, PDK1, PTGS2, PLA2G2D, and DPEP2) could predict the development of rheumatoid arthritis. The CIBERSORT algorithm results showed that five genes were associated with activated mast cells, neutrophils, resting mast cells, and memory resting CD4+ T cells. The receiver operating characteristic curve showed that PLA2G2D and PCK1 have high diagnostic value. To conclude, the expression characteristics of fatty acid metabolism related genes can serve as potential biomarkers for predicting clinical outcomes, which can further improve the accuracy of prediction in RA patients. © 2024, Chinese Journal of Tissue Engineering Research. All rights reserved.