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 immuneârelated genes with changes in the tumor microenvironment (TME) and tumor prognosis of OS. We obtained multiâomics 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 immuneârelated DNA methylation patterns (IMPs), and evaluated prognosis and TME characteristics of the resulting patient subgroups. Subsequently, we used a machineâlearning approach to construct an IMPâassociated prognostic risk model incorporating the expression of a sixâgene 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 IMPâassociated 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 targetedâ and immune checkpoint inhibitor therapy in OS.