Machine learning-based selection of immune cell markers in osteosarcoma: prognostic determination and validation of CLK1 in disease progression.

Introduction: Osteosarcoma (OS) is a malignancy of the bone that mainly afflicts younger individuals. Despite existing treatment approaches, patients with metastatic or recurrent disease generally face poor prognoses. A greater understanding of the tumor microenvironment (TME) is critical for enhancing outcomes in OS patients. Methods: The clinical and RNA expression data of OS patients were extracted from the TARGET database. The single-cell RNA sequencing (scRNA-seq) data of 11 OS samples was retrieved from the GEO database, and analyzed using the Seurat package of R software. Copy number variation (CNV) was analyzed using the InferCNV software. The potential interactions between the different cells in the TME was analyzed with the CellChat package. A multi-algorithm-based computing framework was used to calculate the tumor-infiltrating immune cell (TIIC) scores. A prognostic model was constructed using 20 machine learning algorithms. Maftools R package was used to characterize the genomic variation landscapes in the patient groups stratified by TIIC score. The human OS cell lines MG63 and U2OS were used for the functional assays. Cell proliferation and migration were analyzed by the EdU assay and Transwell assay respectively. CLK1 protein expression was measured by immunoblotting. Results: We observed higher CNV in the OS cells compared to endothelial cells. In addition, there was distinct transcriptional heterogeneity across the OS cells, and cluster 1 was identified as the terminal differentiation state. S100A1, TMSB4X, and SLPI were the three most significantly altered genes along with the pseudo-time trajectory. Cell communication analysis revealed an intricate network between S100A1+ tumor cells and other TME cells. Cluster 1 exhibited significantly higher aggressiveness features, which correlated with worse clinical outcomes. A prognostic model was developed based on TIIC-related genes that were screened using machine learning algorithms, and validated in multiple datasets. Higher TIIC signature score was associated with lower cytotoxic immune cell infiltration and generally inferior immune response and survival rate. Moreover, TIIC signature score was further validated in the datasets of other cancers. CLK1 was identified as a potential oncogene that promotes the proliferation and migration OS cells. Conclusion: A TIIC-based gene signature was developed that effectively predicted the prognosis of OS patients, and was significantly associated with immune infiltration and immune response. Moreover, CLK1 was identified as an oncogene and potential therapeutic target for OS.