内质网应激相关基因能预测骨肉瘤预后并与肿瘤免疫微环境相关

以内质网应激相关基因构建骨肉瘤患者的风险模型,探索其与肿瘤免疫微环境的关系。采用生物信息学分析法,训练集的转录组数据及临床数据下载于UCSC Xena数据库,验证集的相应数据下载于GEO数据库(GSE21257,GSE39058)。采用单因素COX回归分析、LASSO回归分析及多因素COX回归分析提取风险特征基因构建风险模型,使用决策曲线分析、受试者工作特征曲线分析验证模型的准确性,随后构建列线图进一步预测骨肉瘤患者预后;根据风险评分将患者分为高、低风险组,使用Kaplan-Meier生存曲线评估高、低风险组间的生存差异,对差异表达基因(Differentially expressed genes, DEGs)进行GO/KEGG联合富集分析、基因集富集分析(Gene set enrichment analysis, GSEA)及基因集变异分析(Gene set variation analysis, GSVA);采用ESTIMATE算法、微环境种群计数器(Microenvironment cell population counter, MCP counter)方法、单样本基因集富集分析(Single sample gene set enrichment analysis, ssGSEA)进行免疫分析;最终在验证集中验证上述结果。6个风险特征基因中VEGFA、PTGIS及SERPINH1与骨肉瘤患者的不良预后相关,而TMED10、MAPK10及TOR1B与与骨肉瘤患者的良好预后相关,高、低风险组患者之间具有显著生存差异;GO/KEGG联合富集分析、GSVA、GSEA结果表明DEGs与免疫状态相关;免疫分析显示高风险组具有更低的免疫评分及免疫景观;列线图进一步准确地预测了骨肉瘤患者的预后。内质网应激相关基因构建的风险模型能准确预测骨肉瘤患者预后,并与肿瘤免疫微环境相关。

Endoplasmic reticulum stress-related genes predict osteosarcoma prognosis and correlate with tumor immune microenvironment

In this article, endoplasmic reticulum stress (ERS) related genes are used to construct a risk model for osteosarcoma (OS) patients and to explore their relationship with tumor immune microenvironment (TIME). Method: Bioinformatics analysis was used in this study. Clinical information and corresponding RNA data of OS patients were downloaded from UCSC Xena database and GEO database. Univariate and multivariate COX regression analysis and LASSO-Cox algorithm were used to extract risk genes and to further construct a risk model. Decision curve analysis (DCA) and ROC analysis were used to verify the accuracy of this risk model. Integrating the risk scores and clinical features, the nomograph were constructed to further verify the accuracy of the risk model. Patients were divided into high risk group and low risk group based on the risk scores. In this paper, The authors used Kaplan-Meier survival curves to assess the survival differences between high and low risk groups, and performed combined GO/KEGG enrichment analysis, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) on Differentially Expressed Genes (DEGs).The immune analysis was performed using ESTIMATE method, MCP-counter and ssGSEA. The above results were eventually verified in the validation set. Among the six risk profile genes, VEGFA, PTGIS and SERPINH1 were correlated with the poor prognosis of OS patients, while TMED10, MAPK10 and TOR1B were correlated with the better prognosis of OS patients. Significant survival difference was observed between the high and low risk groups. The results of combined GO/KEGG enrichment analysis, GSVA, and GSEA indicated that the DEGs were related with the immune status. Immune analysis showed that the high risk group have the lower immune scores and immune landscape. The nomograph further accurately predicted the prognosis of patients with OS. Conclusion: The risk model based on ERS related genes can accurately predict the prognosis of OS patients and was correlated with TIME.