中国医科大学学报2023，Vol.52Issue(12)：1092-1097,1105,7.DOI:10.12007/j.issn.0258-4646.2023.12.007
基于综合生物信息学和机器学习算法构建衰老相关分泌表型的骨关节炎预测模型
A predictive model of aging-related secretion phenotype for osteoarthritis constructed using integrated bioinformatics and machine learning
摘要
Abstract
Objective To explore the predictive markers of senescence-associated secretory phenotype(SASP)in osteoarthritis(OA).Methods OA datasets were screened by the Gene Expression Omnibus(GEO)database,while SASP-related genes were collected by PubMed.Three machine learning algorithms,including least absolute shrinkage and selection operator(LASSO),support vector machines recursive feature elimination(SVM-RFE),and random forest(RF),were used to screen the candidate predictive markers of SASP genes in OA,and the OA prediction model was constructed using the overlapping genes identified by the machine learning algo-rithms.CIBERSORT was used to explore the degree of peripheral blood immune cell infiltration in OA versus normal samples.The miRNA-transcription factor-mRNA regulatory network of the model genes was predicted using Cytoscape.The most valuable genes of the predic-tion model were experimentally verified by real-time quantitative polymerase chain reaction(RT-qPCR)in OA rats and normal control rats(n= 6 per group).Results One OA dataset was screened by the GEO database,and 125 OA-related SASP genes were isolated.A total of seven intersection genes were obtained by the three machine learning algorithms.The area under the curve of the prediction model was 0.891.The CIBERSORT immune infiltration results showed a significant difference in plasma cell infiltration level between OA and normal samples(P= 0.001 3).The RT-qPCR results showed that the expression level of TNFRSF1Awas significantly higher in the OA versus normal group(P<0.0001).Conclusion TNFRSF1Ais highly expressed in OA and may be a potential predictive marker for it.关键词
骨关节炎/衰老相关分泌表型/免疫浸润/机器学习算法/预测模型Key words
osteoarthritis/senescence-associated secretory shenotype/immunoinfiltration/machine learning algorithm/prediction model分类
医药卫生引用本文复制引用
刘孝生,魏东升,何信用,方策..基于综合生物信息学和机器学习算法构建衰老相关分泌表型的骨关节炎预测模型[J].中国医科大学学报,2023,52(12):1092-1097,1105,7.基金项目
中国博士后科学基金(2021MD703841) （2021MD703841）
