AUTHOR=Zhang Nan , Haizhen Zhou , Zhang Runqi , Li Xiaoju 

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

JOURNAL=Frontiers in Immunology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1468875

DOI=10.3389/fimmu.2024.1468875

ISSN=1664-3224

ABSTRACT=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 (TM E) is critical for enhancing outcomes in OS patients.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.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. M aftools R package was used to characterize the genomic variation landscapes in the patient groups stratified by TIIC score.The human OS cell lines M G63 and U2OS were used for the functional assays. to validate role of CLK1 in OS progression. Cell proliferation and migration were analyzed by was assessed using the EdU assay and .Transwell assay respectively. analysis was conducted to measure cell migration, and immunoblotting was performed to analyze CLK1 protein expression was measured by levelsimmunoblotting.We observed higher CNV in the OS osteosarcoma cells compared to endothelial cells. S100A1, TM SB4X, and SLPI were identified as the three most significantly altered genes along with the pseudo-time trajectory. Cell communication analysis revealed an intricate network of interactions between S100A1+ tumor cells and other TM E cells. Higher TIIC signature score was associated with lower cytotoxic immune cell infiltration and generally inferior immune response and survival rate. M oreover, TIIC s ignature score was further validated in the datasets of other types of cancers. CLK1 was identified as a potential oncogene that promotes with a pivotal role in regulating OS cell cycle progressionthe proliferation and migration OS cells.A TIIC-based gene signature score model was developed that effectively predicted the prognosis , displaying high efficacy inof OS patients, and prognosis stratification, which alsowas significantly associated with immune infiltration and immune response. for osteosarcoma patients.