生物骨科材料与临床研究2024，Vol.21Issue(5)：1-8,8.DOI:10.3969/j.issn.1672-5972.2024.05.001
基于机器学习筛选骨关节炎坏死性凋亡的特征基因RHOB及实验验证
Machine learning-based screening of signature genes RHOB for necrotic apoptosis in osteoarthritis and experimental validation
摘要
Abstract
Objective A machine learning approach was used to screen and validate RHOB,a gene that characterizes necrotic apoptosis in osteoarthritis,with the aim of providing new ideas and methods for OA.Methods Genes related to necrotic apoptosis were obtained by downloading GSE55235,GSE1919,GSE82107,GSE98918 microarray datasets from the GEO database and GeneCard website,and the OA data were batch corrected,OA necrotic apoptosis genes extracted,and variance analyzed by using R.Functional GO analysis was performed on DEGs and KEGG signaling the DEGs were subjected to functional GO analysis and KEGG signaling pathway analysis,and machine learning(LASSO regression,SVM-RFE,random Forest)was applied to screen the characteristic genes of OA necrotic apoptosis,which were further validated by fluorescence quantitative PCR assay and analyzed by immune infiltration.Results A total of 8 492 OA genes were obtained after batch correction and PCA analysis,and 657 necrotic apoptosis-related genes were obtained at the same time.Forty-eight OA necrotic apoptosis DEGs were obtained after analysis,including 18 upregulated genes and 30 downregulated genes;mainly involved in the regulation of inflammatory response,leukocyte intercellular adhesion and other biological processes;involved in the cellular components,such as membrane rafts,membrane microregions,and other cellular components;involved in cytokine activity,integrin binding and other molecular functions;also related to TNF,IL-17,AGE-RAGE signaling pathway and other signaling pathways.Machine learning(LASSO regression,SVM-RFE,Random Forest)was applied to screen 8 genes,11 genes and 8 genes.Respectively,the feature gene RHOB was obtained after intersection,and the validation revealed that RHOB was more accurate as the feature gene of necrotic apoptosis in OA(AUC>0.5),and at the same time,the expression of RHOB of the test group was higher than that of the control group in the synovial tissue(P=0.36),while the expression of RHOB in meniscus tissues was higher than that of the control group(P=0.033).The expression of RHOB mRNA in the test group was higher than that of the control group(P=0.001),which was further confirmed by using in vitro chondrocyte cell culture and fluorescence quantitative PCR assay.Conclusion Using machine learning methods to obtain the characteristic genes and potential factors of necrotic apoptosis in OA provides a new direction for elucidating its pathogenesis and better treatment of OA in the clinic from the perspective of novel cell death.关键词
机器学习/骨关节炎/坏死性凋亡/特征基因Key words
Machine learning/Osteoarthritis/Necrotic apoptosis/Signature genes分类
医药卫生引用本文复制引用
徐文飞,明春玉,段戡,袁长深,郭锦荣,胡琪,曾超,梅其杰..基于机器学习筛选骨关节炎坏死性凋亡的特征基因RHOB及实验验证[J].生物骨科材料与临床研究,2024,21(5):1-8,8.基金项目
2023年广西中医药大学青年科学基金项目(2022QN012) （2022QN012）
2023年广西中医药大学第一附属医院青年科学基金项目(院字[2023]29号) （院字[2023]29号）
国家自然科学基金(82160912) （82160912）
国家自然科学基金(82060875) （82060875）
