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骨肉瘤中基于免疫基质评分的基因特征与分子亚型分析:对预后和肿瘤免疫微环境的意义

Analysis of Immune-Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment.

作者信息

Zheng Dingzhao, Yang Kaichun, Chen Xinjiang, Li Yongwu, Chen Yongchun

机构信息

Department of Rehabilitation Medicine, The Fifth Hospital of Xiamen, Xiamen, China.

Emergency Department, The Fifth Hospital of Xiamen, Xiamen, China.

出版信息

Front Genet. 2021 Sep 23;12:699385. doi: 10.3389/fgene.2021.699385. eCollection 2021.

Abstract

Infiltrating immune and stromal cells are essential for osteosarcoma progression. This study set out to analyze immune-stromal score-based gene signature and molecular subtypes in osteosarcoma. The immune and stromal scores of osteosarcoma specimens from the TARGET cohort were determined by the ESTIMATE algorithm. Then, immune-stromal score-based differentially expressed genes (DEGs) were screened, followed by univariate Cox regression analysis. A LASSO regression analysis was applied for establishing a prognostic model. The predictive efficacy was verified in the GSE21257 dataset. Associations between the risk scores and chemotherapy drug sensitivity, immune/stromal scores, PD-1/PD-L1 expression, immune cell infiltrations were assessed in the TARGET cohort. NMF clustering analysis was employed for characterizing distinct molecular subtypes based on immune-stromal score-based DEGs. High immune/stromal scores exhibited the prolonged survival duration of osteosarcoma patients. Based on 85 prognosis-related stromal-immune score-based DEGs, a nine-gene signature was established. High-risk scores indicated undesirable prognosis of osteosarcoma patients. The AUCs of overall survival were 0.881 and 0.849 in the TARGET cohort and GSE21257 dataset, confirming the well predictive performance of this signature. High-risk patients were more sensitive to doxorubicin and low-risk patients exhibited higher immune/stromal scores, PD-L1 expression, and immune cell infiltrations. Three molecular subtypes were characterized, with distinct clinical outcomes and tumor immune microenvironment. This study developed a robust prognostic gene signature as a risk stratification tool and characterized three distinct molecular subtypes for osteosarcoma patients based on immune-stromal score-based DEGs, which may assist decision-making concerning individualized therapy and follow-up project.

摘要

浸润性免疫细胞和基质细胞对骨肉瘤进展至关重要。本研究旨在分析骨肉瘤中基于免疫-基质评分的基因特征和分子亚型。通过ESTIMATE算法确定TARGET队列中骨肉瘤标本的免疫和基质评分。然后,筛选基于免疫-基质评分的差异表达基因(DEG),接着进行单变量Cox回归分析。应用LASSO回归分析建立预后模型。在GSE21257数据集中验证预测效能。在TARGET队列中评估风险评分与化疗药物敏感性、免疫/基质评分、PD-1/PD-L1表达、免疫细胞浸润之间的关联。采用非负矩阵分解(NMF)聚类分析基于免疫-基质评分的DEG来表征不同的分子亚型。高免疫/基质评分显示骨肉瘤患者的生存时间延长。基于85个与预后相关的基于基质-免疫评分的DEG,建立了一个九基因特征。高风险评分表明骨肉瘤患者预后不良。TARGET队列和GSE21257数据集中总生存的曲线下面积(AUC)分别为0.881和0.849,证实了该特征良好的预测性能。高风险患者对多柔比星更敏感,低风险患者表现出更高的免疫/基质评分、PD-L1表达和免疫细胞浸润。鉴定出三种分子亚型,具有不同的临床结局和肿瘤免疫微环境。本研究开发了一种强大的预后基因特征作为风险分层工具,并基于免疫-基质评分的DEG为骨肉瘤患者表征了三种不同的分子亚型,这可能有助于个性化治疗决策和随访方案的制定。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76e3/8495166/7755b873b58c/fgene-12-699385-g001.jpg

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