A novel prognostic signature related to programmed cell death in osteosarcoma - Jiang, Yu-Chen; Xu, Qi-Tong; Wang, Hong-Bin; Ren, Si-Yuan; Zhang, Yao (Frontiers in Immunology, 2024)
Background: Osteosarcoma primarily affects children and adolescents, often resulting in poor prognosis with current treatments. Evidence links programmed cell death (PCD) to tumor progression. This study aims to improve osteosarcoma (OS) prognosis assessment by identifying PCD-related prognostic genes, constructing a PCD-based risk model, and characterizing the function of these genes. Method: Patient samples were retrieved from TARGET and GEO databases. We summarized 15 forms of PCD and collated 1621 PCD genes from literature and databases such as KEGG and GSEA. We integrated ten machine learning methods to construct the Osteosarcoma Programmed Cell Death Score (OS-PCDS) model, selecting the optimal model based on the average C-index. Validation was performed using three independent GEO datasets. We also assessed mRNA and protein levels of the model's genes and their effects on proliferation, migration, and apoptosis in osteosarcoma cells via gene knockdown experiments. Result: Thirty PCD-related prognostic genes were identified. The random survival forest (RSF) algorithm-based model demonstrated superior predictive performance. Five key genes (MTM1, MLH1, CLTCL1, EDIL3, and SQLE) were identified. These genes showed significant expression differences between osteosarcoma and normal cells, indicating their critical role in tumor progression. Knockdown experiments in U2OS cells showed substantial changes in proliferation, migration, and apoptosis, validating these genes as potential therapeutic targets. The OS-PCDS model, incorporating these five genes, effectively predicts osteosarcoma prognosis, emphasizing their importance as prognostic markers and therapeutic targets.