Single-cell and bulk transcriptomic characterization of the osteosarcoma microenvironment reveals lineage-specific programs and inflammatory differences associated with metastasis.

Abstract

Background: Osteosarcoma is a heterogeneous bone malignancy in which malignant, immune, stromal, and bone-lineage cells form a complex microenvironment. Clinical activity of immune-checkpoint blockade has remained limited in advanced osteosarcoma, motivating cell-type-resolved investigation of the tumor microenvironment while avoiding mechanistic overinterpretation of cross-sectional transcriptomic data.

Methods: Single-cell RNA-sequencing data from primary osteosarcoma were obtained from GEO (GSE162454; six patients) and analyzed with Scanpy using quality filtering, normalization, highly variable gene selection, principal component analysis, neighborhood graph construction, UMAP, Leiden clustering, and marker-based cell-type annotation. Seven transcriptional programs and ligand-receptor communication scores were evaluated. An independent bulk osteosarcoma cohort with metastatic status and overall survival annotation (GSE21257; n = 53) was used for differential expression, cross-platform comparison, and survival analysis. RT-qPCR was performed in primary human osteoblasts exposed to MG-63 osteosarcoma-conditioned medium.

Results: After quality control, 44,189 cells with 25,473 detected genes were retained. Leiden clustering resolved 23 clusters annotated into 11 cell types; myeloid cells (34.0%) and osteoblasts (16.6%) were the largest populations. Osteoblasts showed the highest osteogenic program scores, whereas myeloid cells showed the highest inflammatory and immune-evasion scores. Reclustering of osteoblasts, osteocytes, and mesenchymal stromal cells resolved seven stromal subclusters, but pairwise co-expression between representative osteogenic and inflammatory genes was generally weak. Ligand-receptor analysis highlighted MIF-CD74, SPP1-CD44, and SPP1-ITGAV as the strongest scored interactions. In GSE21257, 333 genes differed between metastatic and non-metastatic tumors at FDR<0.05; the inflammatory module was lower in metastatic tumors, whereas the osteogenic module was not significantly different. The single-cell osteoblast effect showed only modest concordance with the bulk metastatic effect (Spearman rho = 0.254), and a 20-gene osteoblast signature did not stratify overall survival (log-rank p = 0.968). In vitro, osteosarcoma-conditioned medium reduced RUNX2 and SP7 and increased SOST and RANKL expression in primary osteoblasts.

Conclusion: Integrated single-cell and bulk transcriptomic analyses identify lineage-specific osteogenic, inflammatory, and communication programs in osteosarcoma but also demonstrate limited cross-platform generalizability of osteoblast-centered signatures. The RT-qPCR experiment supports an osteoblast transcriptional response to soluble factors from MG-63 cells, but it does not establish a specific immunosuppressive mechanism or prognostic biomarker. These findings should therefore be considered hypothesis-generating and require independent single-cell, spatial, and functional validation.