Zhang Hao, Wang Ting, Gong Haiyi, Jiang Runyi, Zhou Wang, Sun Haitao, Huang Runzhi, Wang Yao, Wu Zhipeng, Xu Wei, Li Zhenxi, Huang Quan, Cai Xiaopan, Lin Zaijun, Hu Jinbo, Jia Qi, Ye Chen, Wei Haifeng, Xiao Jianru
Department of Orthopedic Oncology, Shanghai Changzheng Hospital, Naval Military Medical University, Shanghai, 200003, China.
Musculoskeletal Laboratory, Institute of Biotechnology, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Bone Res. 2023 Jan 2;11(1):1. doi: 10.1038/s41413-022-00233-w.
Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients. However, the highly complex cell origin involved in osteosarcoma (OS) limits the utility of traditional bulk RNA sequencing for OS subclassification. Single-cell RNA sequencing (scRNA-seq) holds great promise for identifying cell heterogeneity. However, this technique has rarely been used in the study of tumor subclassification. By analyzing scRNA-seq data for six conventional OS and nine cancellous bone (CB) samples, we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell (CSC)-like subset, which allowed us to classify OS samples into three groups. The classification model was further examined using the TARGET dataset. Each subgroup of OS had different prognoses and possible drug sensitivities, and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment. In addition, we verified the classification model through IHC staining in 138 OS samples, revealing a worse prognosis for Group B patients. Furthermore, we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS. These findings provide a novel subclassification method based on scRNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.
基于分子特征对肿瘤进行亚分类可能有助于治疗选择并提高癌症患者的缓解率。然而，骨肉瘤（OS）所涉及的高度复杂的细胞起源限制了传统批量RNA测序在骨肉瘤亚分类中的应用。单细胞RNA测序（scRNA-seq）在识别细胞异质性方面具有巨大潜力。然而，这项技术很少用于肿瘤亚分类研究。通过分析六个传统骨肉瘤样本和九个松质骨（CB）样本的scRNA-seq数据，我们在骨肉瘤和松质骨样本中识别出29个细胞簇，并从癌症干细胞（CSC）样亚群中发现了三条分化轨迹，这使我们能够将骨肉瘤样本分为三组。使用TARGET数据集对分类模型进行了进一步检验。骨肉瘤的每个亚组都有不同的预后和可能的药物敏感性，并且三个分化分支中的骨肉瘤细胞在骨肉瘤微环境中与其他细胞簇表现出不同的相互作用。此外，我们通过对138例骨肉瘤样本进行免疫组化染色验证了分类模型，结果显示B组患者预后较差。此外，我们描述了癌症干细胞的新转录程序，并强调了骨肉瘤癌症干细胞中EZH2的激活。这些发现提供了一种基于scRNA-seq的新亚分类方法，为骨肉瘤癌症干细胞的分子特征提供了新的见解，并可能为骨肉瘤的精准治疗和治疗开发提供有价值的参考。
