Orthopedics, Hai'an People's Hospital, Nantong, China.
Orthopedics, Shanghai Zhongye Hospital, Shanghai, China.
J Gene Med. 2024 Jan;26(1):e3572. doi: 10.1002/jgm.3572. Epub 2023 Aug 1.
The physiological and immunological characteristics of the tumor microenvironment (TME) have a profound impact on the effectiveness of immunotherapy. The present study aimed to define the TME subtype of osteosarcoma according to the signatures representing the global TME of the tumor, as well as create a new prognostic assessment tool to monitor the prognosis, TME activity and immunotherapy response of patients with osteosarcoma.
The enrichment scores of 29 functional gene expression signatures in osteosarcoma samples were calculated by single sample gene set enrichment analysis (ssGSEA). TME classification of osteosarcoma was performed and a prognostic assessment tool was created based on 29 ssGSEA scores to comprehensively correlate them with TME components, immunotherapy efficacy and prognosis of osteosarcoma.
Three TME subtypes were generated that differed in survival, TME activity and immunotherapeutic response. Four differentially expressed genes between TME subtypes were involved in the development of prognostic assessment tools. The established prognosis assessment tool had strong performance in both training and verification cohorts, could be effectively applied to the survival prediction of samples of different ages, genders and transfer states, and could well distinguish the TME status of different samples.
The present study describes three different TME phenotypes in osteosarcoma, provides a risk stratification tool for osteosarcoma prognosis and TME status assessment, and provides additional information for clinical decision-making of immunotherapy.
肿瘤微环境(TME)的生理和免疫特性对免疫疗法的效果有深远影响。本研究旨在根据代表肿瘤整体 TME 的特征来定义骨肉瘤的 TME 亚型,并创建一种新的预后评估工具,以监测骨肉瘤患者的预后、TME 活性和免疫治疗反应。
通过单样本基因集富集分析(ssGSEA)计算骨肉瘤样本中 29 个功能基因表达特征的富集分数。对骨肉瘤进行 TME 分类,并基于 29 个 ssGSEA 分数创建预后评估工具,以全面将其与 TME 成分、免疫治疗效果和骨肉瘤预后相关联。
生成了三种 TME 亚型,它们在生存、TME 活性和免疫治疗反应方面存在差异。TME 亚型之间有四个差异表达基因参与预后评估工具的开发。建立的预后评估工具在训练和验证队列中均具有出色的性能,可有效应用于不同年龄、性别和转移状态的样本的生存预测,并能很好地区分不同样本的 TME 状态。
本研究描述了骨肉瘤中的三种不同 TME 表型,提供了骨肉瘤预后和 TME 状态评估的风险分层工具,并为免疫治疗的临床决策提供了额外信息。