Exploration of s new biomarker in osteosarcoma and association with clinical outcomes: cancer associated fibroblasts.

Background: Osteosarcoma (OS) is the leading malignant primary bone tumor in young adults and children and has a high mortality rate. Cancer-associated fibroblasts (CAFs) are major components of the tumor microenvironment, influencing cancer progression and metastasis. However, there is no systematic study on the role of CAF in OS.

Methods: We collected six OS patients' single-cell RNA sequencing data from the TISCH database, which was processed using the Seurat package. We selected gene sets from the well-known MSigDB database and resorted to the clusterprofiler package for gene set enrichment analysis (GSEA). The least absolute shrinkage and selection operator (LASSO) regression model was used for identification of the variables. Receiver operating characteristic and decision curve analyses were utilized for determining the efficacy of the monogram model.

Results: CAFs was recognized as the carcinogenic CAFs subset, given its intense interaction with OS malignant cells and association with the critical cancer driver pathway. We intersected the differentially expressed genes of CAFs with the prognostic genes selected from 88 OS samples. The acquired gene set was selected using the LASSO regression model and integrated with clinical factors to obtain a monogram model of high prognosis predicting power (area under the curve of 5 year survival at 0.883). Functional enrichment analysis revealed the detailed difference between two risk groups.

Conclusion: We identified CAFs as a subset of oncogenic CAFs in OS. Based on differentially expressed genes derived from CAFs, combined with bulk transcriptome prognostic genes, we constructed a risk model that can efficiently predict OS prognosis. Collectively, our study may provide new insights for future studies to elucidate the role of CAF in OS.