Identifying immune‐related prognostic biomarkers in osteosarcoma: Development and validation of the tumor immune microenvironment risk model based on analysis of TCGA

Abstract

AbstractObjectiveOsteosarcoma is a rare and aggressive malignancy with limited effective therapeutic options. This study aimed to identify immune‐related prognostic biomarkers and develop a prognostic model for osteosarcoma.MethodsWe performed integrated analysis of transcriptomic data and immune cell infiltration profiles of 84 osteosarcoma samples from the Cancer Genome Atlas (TCGA) database. Time‐dependent receiver operating characteristic (ROC) curve analysis was used to assess the prognostic value of the TIMErisk model. We also performed functional annotation and pathway enrichment analyses to explore the potential mechanisms underlying the TIMErisk model.ResultsWe identified a seven‐gene TIMErisk model (C2, APBB1IP, BST2, TRPV2, CCL5, GBP1, and F13A1) that was independently associated with overall survival of osteosarcoma patients. The TIMErisk model showed significant associations with immune cell infiltration and immunosuppressive gene expression. In addition, the TIMErisk model was associated with drug sensitivity, and we found that several immune checkpoint genes were significantly differentially expressed between high‐ and low‐TIMErisk groups. Functional annotation and pathway enrichment analyses revealed that the TIMErisk model was associated with multiple immune‐related pathways, including antigen processing and presentation, cytokine‐cytokine receptor interaction, and T cell receptor signaling pathway.ConclusionOur study identified a novel TIME‐based prognostic model for osteosarcoma that incorporates immune‐related genes and can be used to predict patient prognosis and response to immunotherapy. Our findings highlight the importance of the TIME microenvironment in osteosarcoma progression and suggest that immune‐related biomarkers may have clinical significance in the management of osteosarcoma.