Development of Personalized Signature Based on the Immune Landscape to Predict the Prognosis of Osteosarcoma and the Response to Immunotherapy and Targeted Therapy

Abstract

As a heterogeneous and aggressive disease, osteosarcoma (OS) faces great challenges to prognosis and individualized treatment. Hence, we explore the role of immune-related genes in predicting prognosis and responsiveness to immunotherapy and targeted therapies in patients with OS based on the immunological landscape of osteosarcoma. Based on the database of the Therapeutical Applicable Research to Generate Effective Treatments (TARGET), single-sample gene set enrichment analysis (ssGSEA) was used to obtain the enrichment scores of 29 immune characteristics. A series of bioinformatics methods were performed to construct the immune-related prognostic signature (IRPS). Gene set enrichment analysis and gene set variation analysis were used to explore the biological functions of IRPS. We also analyzed the relationship between IRPS and tumor microenvironment. Lastly, the reactivity of IRPS to immune checkpoint therapy and targeted drugs was explored. The ssGSEA algorithm was used to define two immune subtypes, namely Immunity_High and Immunity_Low. Immunity_High was associated with a good prognosis and was an independent prognostic factor of OS. The IRPS containing 7 genes was constructed by the least absolute shrinkage and selection operator Cox regression. The IRPS can divide patients into low- and high-risk patients. Compared with high-risk patients, low-risk patients had a better prognosis and were positively correlated with immune cell infiltration and immune function. Low-risk patients benefited more from immunotherapy, and the sensitivity of targeted drugs in high- and low-risk groups was determined. IRPS can be used to predict the prognosis of OS patients, and provide therapeutic responsiveness to immunotherapy and targeted therapy.