Construction of an immune prediction model for osteosarcoma based on coagulation-related genes - Ye Jiang; Huiqi Yuan; Yongping Cao (Discover Oncology)
Abstract Objectives 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. Methods 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. Results 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. Conclusion 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.