Department of Osteonecrosis and Hip Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
Department of Orthopedics, Hebei Chest Hospital, Shijiazhuang, China.
Front Immunol. 2024 Jan 5;14:1280945. doi: 10.3389/fimmu.2023.1280945. eCollection 2023.
Osteosarcoma (OSA), the most common primary mesenchymal bone tumor, is a health threat to children and adolescents with a dismal prognosis. While cuproptosis and mitochondria dysfunction have been demonstrated to exert a crucial role in tumor progression and development, the mechanisms by which they are regulated in OSA still await clarification.
Two independent OSA cohorts containing transcriptome data and clinical information were collected from public databases. The heterogeneity of OSA were evaluated by single cell RNA (scRNA) analysis. To identify a newly molecular subtype, unsupervised consensus clustering was conducted. Cox relevant regression methods were utilized to establish a prognostic gene signature. Wet lab experiments were performed to confirm the effect of model gene in OSA cells.
We determined 30 distinct cell clusters and assessed OSA heterogeneity and stemness scRNA analysis. Then, univariate Cox analysis identified 24 candidate genes which were greatly associated with the prognosis of OSA. Based on these prognostic genes, we obtained two molecular subgroups. After conducting step Cox regression, three model genes were selected to construct a signature showing a favorable performance to forecast clinical outcome. Our proposed signature could also evaluate the response to chemotherapy and immunotherapy of OSA cases.
We generated a novel risk model based on cuproptosis and mitochondria-related genes in OSA with powerful predictive ability in prognosis and immune landscape.
骨肉瘤(OSA)是最常见的原发性间充质骨肿瘤,对儿童和青少年的健康构成威胁,预后不良。虽然铜死亡和线粒体功能障碍已被证明在肿瘤的进展和发展中发挥关键作用,但它们在 OSA 中的调控机制仍有待阐明。
从公共数据库中收集了包含转录组数据和临床信息的两个独立的 OSA 队列。通过单细胞 RNA(scRNA)分析评估 OSA 的异质性。为了确定新的分子亚型,进行了无监督共识聚类。使用 Cox 相关回归方法建立了预后基因特征。进行了湿实验室实验以确认模型基因在 OSA 细胞中的作用。
我们确定了 30 个不同的细胞簇,并评估了 OSA 异质性和干性 scRNA 分析。然后,单变量 Cox 分析确定了 24 个候选基因,这些基因与 OSA 的预后密切相关。基于这些预后基因,我们获得了两个分子亚群。在进行逐步 Cox 回归后,选择了三个模型基因来构建一个能够很好地预测临床结局的特征。我们提出的特征还可以评估 OSA 病例对化疗和免疫治疗的反应。
我们基于 OSA 中的铜死亡和线粒体相关基因生成了一个新的风险模型,该模型在预后和免疫景观方面具有强大的预测能力。