A novel predictive model associated with osteosarcoma metastasis

Abstract

Purpose: Long non-coding RNAs (lncRNAs) have diverse roles in modulating gene expression on both transcriptional and translational levels, but their involvement in osteo-sarcoma (OS) metastasis remains unknown. Patients and Methods: Transcriptional and clinical data were downloaded from TARGET datasets. A total of seven lncRNAs screened by univariate cox regression, lasso regression, and multivariate cox regression analysis were used to establish the OS metastasis model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model. Results: The established model showed exceptional predictive performance (1 year: AUC = 0.92, 95% Cl = 0.83–0.99; 3 years: AUC = 0.87, 95% Cl = 0.79–0.96; 5 years: AUC = 0.86, 95% Cl = 0.76–0.96). Patients in the high group had a poor survival outcome than those in the low group (p < 0.0001). GSEA analysis revealed that “NOTCH_SIGNALING” and “WNT_BETA_CATENIN_SIGNALING” were significantly enriched and that resting dendritic cells were associated with AL512422.1, AL357507.1, and AC006033.2 (p < 0.05). Conclusion: Based on seven prognosis-related lncRNAs, we constructed a novel model with high reliability and accuracy for predicting metastasis in OS patients.