Deciphering programmed cell death mechanisms in osteosarcoma for prognostic modeling

Osteosarcoma (OS), known for its high recurrence and metastasis rates, poses a significant challenge in oncology. Our research investigates the role of programmed cell death (PCD) genes in OS and develops a prognostic model using advanced bioinformatics. We analyzed single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify subpopulations, distinguish malignant from non-malignant cells, assess cell cycle phases, and map PCD gene distribution. Additionally, we applied consistency clustering to bulk sequencing data from GEO and TARGET (Therapeutically Applicable Research to Generate Effective Treatments) databases, facilitating survival analysis across clusters with the Kaplan-Meier method. We calculated PCD scores for each cluster using the Single-sample Gene Set Enrichment Analysis (ssGSEA), which enabled a detailed examination of PCD-related gene expression and pathway scores. Our study also explored drug sensitivity differences and conducted comprehensive immune cell infiltration analyses using various algorithms. We identified differentially expressed genes, leading to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses that provided insights into relevant biological processes and pathways. The prognostic model, based on five pivotal genes (BAMBI, TMCC2, NOX4, DKK1, and CBS), was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and validated in the TARGET-OS and GSE16091 datasets, showing significant predictive accuracy. This research enhances our understanding of PCD in OS and supports the development of effective treatments.