Integrative analysis of bone-formation associated genes and immune cell infiltration in osteoporosis, and the prediction of active ingredients in targeted traditional Chinese medicine

骨质疏松症骨形成相关基因与免疫细胞浸润的分析及靶向中药活性成分预测

Objective To explore the differential expression and mechanisms of bone formation-related genes in osteoporosis(OP)leveraging bioinformatics and machine learning methodologies;and to predict the active ingredients of targeted traditional Chinese medicine(TCM)herbs.Methods The Gene Expression Omnibus(GEO)and GeneCards databases were employed to conduct a comprehensive screening of genes and disease-associated loci pertinent to the pathogenesis of OP.The R package was utilized as the analytical tool for the identification of differentially expressed genes.Least absolute shrinkage and selection operator(LASSO)logis-tic regression analysis and support vector machine-recursive feature elimination(SVM-RFE)algorithm were employed in defining the genetic signature specific to OP.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses for the selected pivotal genes were conducted.The cell-type identification by estimating rela-tive subsets of RNA transcripts(CIBERSORT)algorithm was leveraged to examine the infiltra-tion patterns of immune cells;with Spearman’s rank correlation analysis utilized to assess the relationship between the expression levels of the genes and the presence of immune cells.Coremine Medical Database was used to screen out potential TCM herbs for the treatment of OP.Comparative Toxicogenomics Database(CTD)was employed for forecasting the TCM ac-tive ingredients targeting the key genes.AutoDock Vina 1.2.2 and GROMACS 2020 softwares were employed to conclude analysis results;facilitating the exploration of binding affinity and conformational dynamics between the TCM active ingredients and their biological targets.Results Ten genes were identified by intersecting the results from the GEO and GeneCards databases.Through the application of LASSO regression and SVM-RFE algorithm;four piv-otal genes were selected:coat protein(CP);kallikrein 3(KLK3);polymeraseγ(POLG);and transient receptor potential vanilloid 4(TRPV4).GO and KEGG pathway enrichment analy-ses revealed that these trait genes were predominantly engaged in the regulation of defense response activation;maintenance of cellular metal ion balance;and the production of chemokine ligand 5.These genes were notably associated with signaling pathways such as ferroptosis;porphyrin metabolism;and base excision repair.Immune infiltration analysis showed that key genes were highly correlated with immune cells.Macrophage M0;M1;M2;and resting dendritic cell were significantly different between groups;and there were signifi-cant differences between different groups(P<0.05).The interaction counts of resveratrol;curcumin;and quercetin with KLK3 were 7;3;and 2;respectively.It shows that the interac-tions of resveratrol;curcumin;and quercetin with KLK3 were substantial.Molecular docking and molecular dynamics simulations further confirmed the robust binding affinity of these bioactive compounds to the target genes.Conclusion Pivotal genes including CP;KLK3;POLG;and TRPV4;exhibited commendable significant prognostic value;and played a crucial role in the diagnostic assessment of OP.Resveratrol;curcumin;and quercetin;natural compounds found in TCM;showed promise in their potential to effectively modulate the bone-forming gene KLK3.This study provides a sci-entific basis for the interpretation of the pathogenesis of OP and the development of clinical drugs.

目的基于生物信息学技术与机器学习探讨骨形成相关基因在骨质疏松症(OP)中的差异表达及作用机制,并预测靶向中药活性成分。方法从高通量基因表达(GEO)与GeneCards数据库获取疾病靶点和骨形成相关基因,使用R语言包筛选差异表基因。最小绝对收缩和选择算子(LASSO)逻辑回归分析和支持向量机-递归特征消除(SVM-RFE)算法筛选OP的特征基因,选择关键基因进行基因本体(GO)与京都基因与基因组百科全书(KEGG)通路富集分析。使用估算RNA转录物相对子集的细胞类型识别(CIBER-SORT)算法进行免疫细胞浸润分析,并对基因表达量以及免疫细胞含量进行Spearman相关性分析。CoremineMedical数据库获取治疗OP的潜在有效中药,比较毒理学数据库(CTD)预测作用于关键基因的靶向中药活性成分,AutoDockVina1.2.2与GROMACS2020软件分析活性成分与目标靶标之间的结合能及相作模式。结果从GEO和GeneCards数据库获得交集基因10个,LASSO逻辑回归分析和SVM-RFE算法筛选出4个关键基因:外壳蛋白(CP)、激肽释放酶3(KLK3)、聚合酶γ(POLG)和瞬时受体电位香草酸受体4(TRPV4)。GO功能与KEGG通路富集分析显示,靶基因主要通过铁死亡、卟啉代谢和碱基切除修复信号通路参与调控防御反应的正调控、细胞金属离子稳态、调控趋化因子配体5的生成等过程而发挥生物学效应。免疫浸润分析显示关键基因与免疫细胞高度相关,巨噬细胞M0、M1、M2和静息树突状细胞在组间具有显著差异,不同组间均表现出显著差异(P<0.05)。白藜芦醇、姜黄素和槲皮素与KLK3的相互作用数分别为7、3和2,表明白藜芦醇、姜黄素和槲皮素与KLK3有明显的相互作用。分子对接与动力学模拟显示活性成分与靶基因之间具有良好的结合能力。结论关键基因CP、KLK3、POLG和TRPV4在OP诊断中具有良好的预测效能,白藜芦醇、姜黄素以及槲皮素可能是靶向骨形成基因KLK3干预OP的潜在中药活性成分。这项研究为OP发病机制的阐释和临床药物的研发提供了科学依据。