Inflammation-driven prognostic model and immune landscape profiling in osteosarcoma

Osteosarcoma is the most common primary malignant bone tumor in adolescents and young adults, and its prognosis remains poor, particularly in metastatic cases. Chronic inflammation within the tumor microenvironment promotes disease progression and immune evasion, yet few prognostic models incorporate inflammation‑related molecular features.

Bulk RNA-seq data and clinical annotations of osteosarcoma patients were obtained from TCGA, and a curated inflammation gene set (top 500 genes by relevance) was defined. LASSO and Cox regression analyses identified prognostic genes, from which we built a risk‑scoring model; optimal cut‑offs were set by maximally selected rank statistics. Model performance was evaluated using Kaplan-Meier survival curves and time‑dependent ROC analysis. We then constructed and calibrated a nomogram combining key genes and metastasis status. Single‑cell RNA‑seq data (GSE1624554) were processed in Seurat to map inflammation gene expression across cell types. Immune infiltration differences between risk groups were assessed via ESTIMATE and ssGSEA. Differentially expressed genes underwent GO and KEGG enrichment analysis, and potential drug repurposing candidates were explored through cMap and molecular docking with Temozolomide.

The resulting 11‑gene signature stratified patients into high and low risk with markedly different overall survival (p < 0.001), achieving AUCs of 0.808, 0.883, and 0.879 at 1, 3, and 5 years, respectively. The nomogram demonstrated excellent calibration and discriminative ability. Single‑cell analysis revealed macrophage‑ and myeloid‑specific enrichment of CD163 and SAMHD1. Low‑risk tumors exhibited higher immune and stromal scores, increased CD8⁺ T‑cell and APC activity, and enrichment of cytokine‑related pathways. Pan‑cancer assessment highlighted context‑dependent roles for PPARG, TERT, and VEGFA. Molecular docking predicted a favorable binding energy (-6.8 kcal/mol) between TERT and Temozolomide.

This inflammation-related risk model provides a novel prognostic tool for osteosarcoma, elucidates the interplay between tumor inflammation and immune infiltration, and suggests potential therapeutic targets and drug repurposing strategies.