Integrative analysis of immune‐related multi‐omics profiles identifies distinct prognosis and tumor microenvironment patterns in osteosarcoma

Abstract

Osteosarcoma (OS) is the most common primary malignancy of bone. Epigenetic regulation plays a pivotal role in cancer development in various aspects, including immune response. In this study, we studied the potential association of alterations in the DNA methylation and transcription of immune‐related genes with changes in the tumor microenvironment (TME) and tumor prognosis of OS. We obtained multi‐omics data for OS patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. By referring to curated immune signatures and using a consensus clustering method, we categorized patients based on immune‐related DNA methylation patterns (IMPs), and evaluated prognosis and TME characteristics of the resulting patient subgroups. Subsequently, we used a machine‐learning approach to construct an IMP‐associated prognostic risk model incorporating the expression of a six‐gene signature (MYC, COL13A1, UHRF2, MT1A, ACTB, and GBP1), which was then validated in an independent patient cohort. Furthermore, we evaluated TME patterns, transcriptional variation in biological pathways, somatic copy number alteration, anticancer drug sensitivity, and potential responsiveness to immune checkpoint inhibitor therapy with regard to our IMP‐associated signature scoring model. By integrative IMP and transcriptomic analysis, we uncovered distinct prognosis and TME patterns in OS. Finally, we constructed a classifying model, which may aid in prognosis prediction and provide a potential rationale for targeted‐ and immune checkpoint inhibitor therapy in OS.

Keywords

• DNA methylation
• osteosarcoma
• prognostic risk model
• transcriptomics
• tumor immunology
• tumor microenvironment