Liu Yang, Liu Liwei, Wei Xianpeng, Xiong Yan, Han Qifang, Gong Tianhui, Tang Fuzhou, Xia Kaide, Zheng Shuguang
Guizhou University of Traditional Chinese Medicine, Guiyang, China.
The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China.
J Cancer. 2025 Feb 28;16(6):1873-1887. doi: 10.7150/jca.104855. eCollection 2025.
Identification of effective biomarkers is crucial to improve the efficacy of immunotherapy in patients with osteosarcoma. Tumor-associated M2 macrophages, an important immune cell type in the tumor immune microenvironment, are closely related to the formation and progression of tumors. However, the relationships of M2 macrophages and prognosis and the immunotherapy response to osteosarcoma remain unclear. In this study, we obtained single-cell RNA sequencing (scRNA-seq) data of osteosarcoma from the gene expression omnibus (GEO) database and performed trajectory analysis and cell communication analysis. We then identified M2 macrophage marker genes based on scRNA-seq data of osteosarcoma, and constructed a risk-score model using these genes. Next, we compared the survival status and immune features of patients with high and low risk scores. Based on scRNA-seq data, we found that macrophages were the major immune cell type in the osteosarcoma microenvironment, and the high proportion of M2 macrophages might result from the transition of macrophages M1 to M2. M2 macrophages communicated with osteoblastic cells via the APP, MIF, and SPP1 signaling pathways, facilitating osteosarcoma development. Moreover, we identified 189 osteosarcoma-related M2 macrophage marker genes and screened out 10 key genes used for model constrcution. These 10 genes consisted of two known M2 macrophage markers and eight novel M2 macrophage marker genes. Low-risk patients have a statistically significant survival advantage, which was verified in the four GEO datasets. Low-risk patients also displayed a high abundance of tumor-infiltrating immune cells, indicative of an "hot" immune phenotype, while high-risk patients displayed an opposite immunologic feature. Notably, our analysis of two independent immunotherapy cohorts revealed that low-risk patients had good immunotherapy responses and outcomes. Additionally, we determined 32 evidently correlated pairs between risk score and drug sensitivity. This study reveals a new prognostic signature based on M2 macrophage marker genes that can help optimize personalized prognosis and improve immunotherapy outcomes in patients with osteosarcoma and also provides a method for identifying effective biomarkers based on integrated analysis of single-cell and bulk RNA sequencing.
鉴定有效的生物标志物对于提高骨肉瘤患者免疫治疗的疗效至关重要。肿瘤相关的M2巨噬细胞是肿瘤免疫微环境中的一种重要免疫细胞类型,与肿瘤的形成和进展密切相关。然而,M2巨噬细胞与骨肉瘤预后及免疫治疗反应之间的关系仍不清楚。在本研究中,我们从基因表达综合数据库(GEO)中获取了骨肉瘤的单细胞RNA测序(scRNA-seq)数据,并进行了轨迹分析和细胞通讯分析。然后,我们基于骨肉瘤的scRNA-seq数据鉴定了M2巨噬细胞标记基因,并使用这些基因构建了一个风险评分模型。接下来,我们比较了高风险评分和低风险评分患者的生存状况和免疫特征。基于scRNA-seq数据,我们发现巨噬细胞是骨肉瘤微环境中的主要免疫细胞类型,M2巨噬细胞比例高可能是由于巨噬细胞从M1向M2转变所致。M2巨噬细胞通过APP、MIF和SPP1信号通路与成骨细胞通讯,促进骨肉瘤发展。此外,我们鉴定了189个与骨肉瘤相关的M2巨噬细胞标记基因,并筛选出10个用于模型构建的关键基因。这10个基因包括两个已知的M2巨噬细胞标记物和八个新的M2巨噬细胞标记基因。低风险患者具有统计学上显著的生存优势,这在四个GEO数据集中得到了验证。低风险患者还表现出肿瘤浸润免疫细胞的高丰度,表明是“热”免疫表型,而高风险患者则表现出相反的免疫特征。值得注意的是,我们对两个独立免疫治疗队列的分析表明,低风险患者具有良好的免疫治疗反应和结果。此外,我们确定了风险评分与药物敏感性之间32对明显相关的组合。本研究揭示了一种基于M2巨噬细胞标记基因的新的预后特征,有助于优化骨肉瘤患者的个性化预后并改善免疫治疗结果,同时还提供了一种基于单细胞和批量RNA测序综合分析来鉴定有效生物标志物的方法。