RESUMO
BACKGROUND: Osteosarcoma (OS) is the most prevalent orthopedic malignancy with a dismal prognosis. Disulfidptosis-related lncRNAs (DRLncs) may be related to the progression of OS, but their potential molecular regulatory role is still unclear. METHODS: Based on the data collected from The Cancer Genome Atlas (TCGA), we conducted correlation analysis and the univariate Cox analysis to screen prognosis-related DRLncs, followed by developing genotyping patterns and corresponding classifier. Subsequently, the survival analysis, enrichment analysis, drug sensitivity analysis and immune infiltration analysis were performed. Afterward, multivariate Cox regression was used to construct a risk model, which was further validated by the receiver operating characteristic (ROC) curve. The aberrant expression of hub DRLncs in OS was validated using the Reverse Transcription Polymerase Chain Reaction (RT-qPCR) assay. RESULTS: We identified 262 DRLncs and eleven prognosis-related DRLncs through filtering. We then constructed two distinct expression patterns of prognosis-related DRLncs and developed a classifier. We obtained 393 differentially expressed genes (DEGs) between different subtypes, which were significantly enriched in biological processes related to the extracellular matrix, integrin binding, focal adhesion, and Wnt signaling pathways. Through immune infiltration analysis, the activated CD4 memory T cells, resting natural killer (NK) cells, M1 macrophages, and resting dendritic cells (DC) were observed to exhibit different abundance in distinct subtypes. In the drug sensitivity analysis, tamoxifen showed a promising effect for drug-resistant OS. Furthermore, we identified five hub DRLncs and constructed a risk model. The RT-qPCR confirmed the aberrant expression of five hub DRLncs in OS. CONCLUSIONS: The present study identified DRLncs in OS, and conducted a comprehensive investigation of DRLncs-related expression patterns, survival status, immune landscape and drug sensitivity to reveal the difference in prognostic, pharmacological and immunological phenotype characteristics between distinct subtypes. Additionally, we developed a risk model to predict the prognosis, and constructed a genotyping classifier to predict the above phenotype characteristics in OS.
