Department of Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR China.
Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR China.
Aging (Albany NY). 2020 Nov 16;12(22):22906-22926. doi: 10.18632/aging.104017.
The purpose of this study is to establish the prognosis of osteosarcoma patients based on the characteristics of immune-related gene pairs. We used the lasso Cox regression model to construct and verify the signature consisting of 14 immune-related gene pairs. This signature can accurately predict the overall survival of osteosarcoma patients and is an independent prognostic factor for osteosarcoma patients. For this we constructed a signature-based nomogram. The results of the nomogram show that our signature can bring clinical net benefits. We then assessed the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. The result of gene set enrichment analysis shows the strong relationship between signature and immune system. Finally, we evaluated the relationship between signature and immunotherapy efficiency using algorithms such as TIMI and SubMap to explore patients who might benefit from immunotherapy. In conclusion, our signature can predict the overall survival rate of osteosarcoma patients and provide potential guidance for exploring patients who may benefit from immunotherapy.
本研究旨在基于免疫相关基因对的特征来建立骨肉瘤患者的预后模型。我们使用lasso Cox 回归模型构建并验证了由 14 对免疫相关基因对组成的特征。该特征可以准确预测骨肉瘤患者的总生存率，是骨肉瘤患者的独立预后因素。为此，我们构建了一个基于特征的列线图。列线图的结果表明，我们的特征可以带来临床净效益。然后，我们评估了每个样本中浸润免疫细胞的丰度，并结合单个样本的基因集富集分析结果，探索 IRPG 特征组之间免疫微环境的差异。基因集富集分析的结果表明特征与免疫系统之间存在很强的关系。最后，我们使用 TIMI 和 SubMap 等算法评估特征与免疫治疗效率之间的关系，以探索可能受益于免疫治疗的患者。总之，我们的特征可以预测骨肉瘤患者的总生存率，并为探索可能受益于免疫治疗的患者提供潜在的指导。
