同济大学学报（医学版）2023，Vol.44Issue(6)：792-804,13.DOI:10.12289/j.issn.1008-0392.23018
基于整合生物信息学分析识别骨肉瘤干性相关基因
Identification of stemness-related genes in osteosarcoma based on integrated bioinformatics analysis
摘要
Abstract
Objective To identify stemness-related genes(SRGs)in osteosarcoma(OS)by bioinformatics analysis and to explore their clinical implication.Methods RNA sequencing(RNA-seq)data were obtained from TARGET database,and normalized using"edgeR"package.The mRNA expression-based stemness index(mRNAsi)was obtained by using a one-class logistic regression machine learning algorithm based on RNA-seq data of OS patients.SRGs were identified with Pearson correlation analysis.GSE152048 dataset were downloaded from the Gene Expression Omnibus(GEO)database,which were used to explore and tumor heterogeneity of OS and to validate the expression of key genes.The potential cellular communication patterns and cell cycle status were predicted.Results The mRNAsi was significantly different among OS samples.Specifically,mRNAsi of the death group was significantly higher than the survival group;mRNAsi of the metastatic group was significantly higher than the non-metastatic group.By taking the intersection of the results of Cox regression analysis(1 640 risk-related genes),Kaplan-Meier survival analysis(7 066 survival-related genes),and Pearson correlation analysis(5 131 SRGs),finally 658 key genes were identified.NDUFB9 showed the strongest positive correlation with mRNAsi,while EHD2 showed the strongest negative correlation with mRNAsi.OS cells were divided into 10 cell subsets by dimension reduction analysis.NDUFB9 was highly expressed in stem cell-like cell subsets,including mesenchymal stem cells and osteoblasts,whereas the expression of EHD2 in OS cell subsets was extremely low.Conclusion This study investigated the stemness features and single cell transcriptome landscape of OS,and identified prognostic SRGs,which provided information for revealing the regulatory mechanism of OS stemness and potential therapeutic targets for OS.关键词
骨肉瘤/癌症干细胞/干细胞指数/单细胞RNA测序Key words
osteosarcoma/cancer stem cells/stemness index/single cell RNA sequencing分类
医药卫生引用本文复制引用
符晴,王思乔,缐述源,黄润之,张杰..基于整合生物信息学分析识别骨肉瘤干性相关基因[J].同济大学学报（医学版）,2023,44(6):792-804,13.基金项目
国家自然科学基金(81501203) （81501203）
