细胞衰老是癌症的一个新标志，与患者的预后和肿瘤免疫治疗有关。然而，目前尚无利用细胞衰老相关长非编码RNA（CSR-lncRNA）预测骨肉瘤患者生存的系统研究。在这项研究中，我们的目的是鉴定 CSR-lncRNA 特征，并评估其作为骨肉瘤生存预后标志物和免疫反应预测工具的潜在用途。我们从癌症基因组图谱 (TCGA) 中下载了一组骨肉瘤患者，并基因表达综合 (GEO) 数据库。我们进行了差异表达和共表达分析来鉴定 CSR-lncRNA。我们进行了单变量和多变量 Cox 回归分析以及随机森林算法，以识别与衰老显着相关的 lncRNA。随后，我们使用生存曲线、受试者工作特征曲线、列线图、C 指数和决策曲线分析来评估预测模型。基于该模型，骨肉瘤患者根据风险评分分为两组。然后，使用基因本体论和京都基因和基因组百科全书分析，我们比较了它们的临床特征以揭示功能差异。我们进一步进行了免疫浸润分析，使用表达数据（ESTIMATE）估计恶性肿瘤组织中的基质细胞和免疫细胞，通过估计RNA转录本的相对子集进行细胞类型鉴定（CIBERSORT），以及两者的单样本基因集富集分析组。我们还评估了免疫检查点抑制剂（ICIs）靶基因的表达。我们鉴定了六种与衰老显着相关的lncRNA，并相应地建立了包含这些lncRNA的新型细胞衰老相关lncRNA预后特征。列线图表明风险模型是可以预测骨肉瘤患者生存的独立预后因素。该模型经验证显示出较高的准确性。进一步分析显示，低风险组骨肉瘤患者表现出更好的临床结果，免疫浸润增强。6-CSR-lncRNA预后特征有效预测生存结果，低风险组患者的免疫浸润可能有所改善。2024转化癌症研究。版权所有。

Cellular senescence, a novel hallmark of cancer, is associated with patient outcomes and tumor immunotherapy. However, at present, there is no systematic study on the use of cellular senescence-related long non-coding RNAs (CSR-lncRNAs) to predict survival in patients with osteosarcoma. In this study, we aimed to identify a CSR-lncRNAs signature and to evaluate its potential use as a survival prognostic marker and predictive tool for immune response of osteosarcoma.We downloaded a cohort of patients with osteosarcoma from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We performed differential expression and co-expression analyses to identify CSR-lncRNAs. We performed univariate and multivariate Cox regression analyses along with the random forest algorithm to identify lncRNAs significantly correlated with senescence. Subsequently, we assessed the predictive models using survival curves, receiver operating characteristic curves, nomograms, C-index, and decision curve analysis. Based on this model, patients with osteosarcoma were divided into two groups according to their risk scores. Then, using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, we compared their clinical characteristics to uncover functional differences. We further conducted immune infiltration analyses using estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), cell-type identification by estimating relative subsets of rna transcripts (CIBERSORT), and single-sample gene set enrichment analysis for the two groups. We also evaluated the expression of the target genes of immune checkpoint inhibitors (ICIs).We identified six lncRNAs that were significantly correlated with senescence and accordingly established a novel cellular senescence-related lncRNA prognostic signature incorporating these lncRNAs. The nomogram indicated that the risk model was an independent prognostic factor that could predict the survival of patients with osteosarcoma. This model demonstrated high accuracy upon validation. Further analysis revealed that patients with osteosarcoma in the low-risk group exhibited better clinical outcomes and enhanced immune infiltration.The six-CSR-lncRNA prognostic signature effectively predicted survival outcomes and patients in the low-risk group might have improved immune infiltration.2024 Translational Cancer Research. All rights reserved.