Construction and validation of a novel prognostic signature that correlates with immune infiltration based on glycolysis related lncRNAs in osteosarcoma

BACKGROUND: Osteosarcoma is the most frequent malignancy for adolescents in bone system, which has caused huge disease burdens for both patients and society. Aberrant activation of glycolysis has been proven to promote osteosarcoma progression. But the prognostic roles of glycolysis-related lncRNAs still remain obscure. We aim to establish a glycolysis-related lncRNA-based signature to predict the prognosis of osteosarcoma. METHODS: Glycolysis-related genes were retrieved from GSEA website. Differentially expressed genes between osteosarcoma samples and the normal samples were determined in GSE36001. Glycolysis-related lncRNA were identified by correlation analysis. The prognosis model was constructed by Cox regression analysis and LASSO analysis. Independent prognostic factors of osteosarcoma were determined by multivariate Cox regression analysis. A prognostic nomogram was further developed with estimation of ROC curves, calibration curves and DCA curves. GO and KEGG enrichment analyses and immune infiltration analyses (ssGSEA, xCELL and CIBERSORT) were conducted to characterize intrinsic features of risk group. RESULTS: A 11-glycolysis-related lncRNA prognosis model was constructed and validated in validation cohort and several clinical subgroups. ROC curve results showed that the AUC values of training set and validation set were 0.935 and 0.750. Risk score and metastasis status were identified as independent prognostic factors whereby the nomogram was built on them. The robust discriminability of the nomogram was verified. Functional and immunological characterization identified that some cancer-related signaling pathways and mutiple immune infiltrating cells are significantly associated with the signature. CONCLUSIONS: This study revealed the prognostic value of glycolytic-associated lncRNAs in osteosarcoma. The novel glycolysis-associated lncRNA-based model may offer new insights into prognostic evaluation and clinical risk decision for patients with osteosarcoma.