Jiang Ye, Yuan Huiqi, Cao Yongping
Department of Orthopedics, Peking University First Hospital, Beijing, People's Republic of China.
School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China.
Discov Oncol. 2025 Jul 31;16(1):1449. doi: 10.1007/s12672-025-03214-7.
The prognostic outcome of osteosarcoma, as the most common primary malignancy in children and adolescents, has not improved with the development of modern medical care, and the aim of this study was to investigate the role of the coagulation system in the diagnosis and development of osteosarcoma.
TRGET and GEO databases were used to acquire clinical information and matching RNA data from osteosarcoma patients. To find novel molecular groupings based on coagulation systems, shared clustering was used. TIMER, SSGSEA, CIBERSORT, QUANTISEQ, XCELL, EPIC, and MCPCOUNTER analyses were used to identify the immunological status of the identified subgroups and tumor immune microenvironment (TIME). To understand the underlying processes, functional studies such as GO, KEGG, and protein-protein interaction (PPI) network analysis were used. Prognostic risk models were built using the LASSO technique and multivariate Cox regression analysis.
The two molecular subgroups exhibited significantly different overall survival outcomes. Patients in one group demonstrated markedly better survival, suggesting the prognostic relevance of the molecular classification. This favorable prognosis was linked to a more active anti-tumor immune microenvironment, characterized by higher immune scores, lower tumor purity, and increased immune cell infiltration. Differential gene expression analysis between the two subgroups revealed a strong enrichment in immune-related and extracellular matrix (ECM)-associated pathways, as shown by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. These findings suggest that both immune activity and ECM remodeling may contribute to the prognostic differences between subgroups. Furthermore, a prognostic risk model constructed using coagulation system-related genes (CRGs) demonstrated solid predictive ability for patient survival. Patients classified into high- and low-risk groups by this model also exhibited distinct survival curves. Finally, we developed a nomogram by integrating the CRG-based risk score with key clinical variables. This nomogram showed good predictive performance and could serve as a clinically applicable tool for estimating survival in patients with osteosarcoma.
In patients with osteosarcoma, the expression of genes associated to the coagulation system is strongly related to the immunological milieu and can be utilized to correctly predict the prognosis of osteosarcoma.
骨肉瘤是儿童和青少年中最常见的原发性恶性肿瘤，尽管现代医疗有所发展，但其预后并未得到改善。本研究旨在探讨凝血系统在骨肉瘤诊断和发展中的作用。
利用TRGET和GEO数据库获取骨肉瘤患者的临床信息和匹配的RNA数据。采用共享聚类法寻找基于凝血系统的新型分子分组。使用TIMER、SSGSEA、CIBERSORT、QUANTISEQ、XCELL、EPIC和MCPCOUNTER分析来确定所识别亚组的免疫状态和肿瘤免疫微环境（TIME）。为了解潜在机制，采用了功能研究，如GO、KEGG和蛋白质-蛋白质相互作用（PPI）网络分析。使用LASSO技术和多变量Cox回归分析构建预后风险模型。
两个分子亚组的总生存结果存在显著差异。一组患者的生存情况明显更好，表明分子分类与预后相关。这种良好的预后与更活跃的抗肿瘤免疫微环境有关，其特征是免疫评分更高、肿瘤纯度更低和免疫细胞浸润增加。两个亚组之间的差异基因表达分析显示，基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析表明，免疫相关和细胞外基质（ECM）相关途径有强烈富集。这些发现表明，免疫活性和ECM重塑都可能导致亚组之间的预后差异。此外，使用凝血系统相关基因（CRG）构建的预后风险模型对患者生存具有可靠的预测能力。通过该模型分为高风险和低风险组的患者也表现出不同的生存曲线。最后，我们通过将基于CRG的风险评分与关键临床变量相结合，开发了一个列线图。该列线图显示出良好的预测性能，可作为评估骨肉瘤患者生存情况的临床适用工具。
在骨肉瘤患者中，与凝血系统相关的基因表达与免疫环境密切相关，可用于准确预测骨肉瘤的预后。
