李威材, 秦刚, 何凯毅, 苏国威, 刘金富, 肖世富, 范以东, 吴广涛, 刘俊良. 骨肉瘤双硫死亡相关lncRNA预后预测模型的构建与验证[J]. 中国肿瘤临床, 2023, 50(15): 778-785. DOI: 10.12354/j.issn.1000-8179.2023.20230320
引用本文: 李威材, 秦刚, 何凯毅, 苏国威, 刘金富, 肖世富, 范以东, 吴广涛, 刘俊良. 骨肉瘤双硫死亡相关lncRNA预后预测模型的构建与验证[J]. 中国肿瘤临床, 2023, 50(15): 778-785. DOI: 10.12354/j.issn.1000-8179.2023.20230320
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Weicai Li, Gang Qin, Kaiyi He, Guowei Su, Jinfu Liu, Shifu Xiao, Yidong Fan, Guangtao Wu, Junliang Liu. Construction and validation of disulfidptosis-related lncRNAs prognostic prediction model for osteosarcoma[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2023, 50(15): 778-785. DOI: 10.12354/j.issn.1000-8179.2023.20230320
Citation: Weicai Li, Gang Qin, Kaiyi He, Guowei Su, Jinfu Liu, Shifu Xiao, Yidong Fan, Guangtao Wu, Junliang Liu. Construction and validation of disulfidptosis-related lncRNAs prognostic prediction model for osteosarcoma[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2023, 50(15): 778-785. DOI: 10.12354/j.issn.1000-8179.2023.20230320
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骨肉瘤双硫死亡相关lncRNA预后预测模型的构建与验证

Construction and validation of disulfidptosis-related lncRNAs prognostic prediction model for osteosarcoma

    摘要
  • 摘要:
      目的   构建双硫死亡相关lncRNA（disulfidptosis-related lncRNAs，DRLncs）的预后模型，以预测骨肉瘤（osteosarcoma，OS）的预后，提高患者生存率。
      方法   差异表达和皮尔逊（Pearson）相关性分析用于鉴定OS相关DRLncs，使用单变量Cox回归分析和最小绝对收缩选择算子（least absolute shrinkage and selection operator，LASSO）回归分析构建DRLncs的风险预后模型。通过生存分析、受试者工作特征（receiver operating characteristic，ROC）曲线、列线图和校准曲线以验证模型的可靠性。此外，探讨预后模型与免疫微环境和药物敏感性之间的关系。选取近10年自广西中医药大学第一附属医院OS患者的肿瘤组织及健康人的正常组织各30例，通过实时荧光定量-PCR（real-time quantitative PCR，qRT-PCR）验证DRLncs在OS组织中的表达。
      结果   成功构建包含3种DRLncs（RERG−IT1、AL035446.1和AC010894.1）的风险预后模型，该模型在预测OS患者的总体生存率方面表现出良好的性能。DRLncs预后模型与肿瘤微环境、免疫浸润细胞和药物敏感性之间具有显著性相关。qRT-PCR实验结果显示， RERG−IT1在OS组织中表达显著升高，而AL035446.1和AC010894.1则显著降低（P<0.01）。
      结论   本研究构建的DRLncs预后模型可准确预测OS患者的预后，为OS的个性化治疗提供了新的思路。

     

    Abstract:
      Objective   To construct a prognostic model of disulfidptosis-related lncRNAs (DRLncs) to predict the prognosis of osteosarcoma and improve the survival rate of patients.
      Methods   Differential expression and Pearson correlation analysis were used to identify osteosarcoma-associated DRLncs. The DRLncs risk prognosis model was constructed by univariate Cox, least absolute shrinkage and selection operator (LASSO) regression analyses. The reliability of the model was verified by survival analysis, receiver operating characteristic (ROC) curves, nomograms, and calibration curves. The relationships between prognostic models, the immune microenvironment, and drug sensitivity were explored. Select 30 cases of tumor tissue from osteosarcoma patients and 30 cases of normal tissue from healthy individuals from The First Affiliated Hospital of Guangxi University of Chinese Medicine in the past 10 years, real-time quantitative PCR (qRT-PCR) was used to verify the expression of DRLncs in osteosarcoma tissues.
      Results   A risk prognostic model was successfully constructed with three DRLncs (RERG−IT1, AL035446.1, and AC010894.1). The model showed good performance in predicting the overall survival of osteosarcoma patients. There was a significant correlation between the DRLncs prognostic model and the tumor microenvironment, immune infiltrating cells, and chemotherapy drug sensitivity. qRT-PCR revealed significantly increased expression of RERG−IT1 in osteosarcoma tissues, while AL035446.1 and AC010894.1 were significantly reduced (all P<0.01).
      Conclusions   The DRLncs prognostic model constructed in this study can accurately predict the prognosis of osteosarcoma patients, which provides a new idea for the personalized treatment of osteosarcoma.

     

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