The hexosamine biosynthesis pathway-related gene signature correlates with immune infiltration and predicts prognosis of patients with osteosarcoma.

Objectives: Osteosarcoma is a malignant bone tumor with poor outcomes affecting the adolescents and elderly. In this study, we comprehensively assessed the metabolic characteristics of osteosarcoma patients and constructed a hexosamine biosynthesis pathway (HBP)-based risk score model to predict the prognosis and tumor immune infiltration in patients with osteosarcoma. Methods: Gene expression matrices of osteosarcoma were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. GSVA and univariate Cox regression analysis were performed to screen the metabolic features associated with prognoses. LASSO regression analysis was conducted to construct the metabolism-related risk model. Differentially expressed genes (DEGs) were identified and enrichment analysis was performed based on the risk model. CIBERSORT and ESTIMATE algorithms were executed to evaluate the characteristics of tumor immune infiltration. Comparative analyses for immune checkpoints were performed and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict immunotherapeutic response. Finally, hub genes with good prognostic value were comprehensive analyzed including drug sensitivity screening and immunohistochemistry (IHC) experiments. Results: Through GSVA and survival analysis, the HBP pathway was identified as the significant prognostic related metabolism feature. Five genes in the HBP pathway including GPI, PGM3, UAP1, OGT and MGEA5 were used to construct the HBP-related risk model. Subsequent DEGs and enrichment analyses showed a strong correlation with immunity. Further, CIBERSORT and ESTIMATE algorithms showed differential immune infiltration characteristics correlated with the HBP-related risk model. TIDE algorithms and immune checkpoint analyses suggested poor immunotherapeutic responses with low expression of immune checkpoints in the high-risk group. Further analysis revealed that the UAP1 gene can predict metastasis. IHC experiments suggested that UAP1 expression correlated significantly with the prognosis and metastasis of osteosarcoma patients. When screening for drug sensitivity, high UAP1 expression was suggestive of great sensitivity to antineoplastic drugs including cobimetinib and selumetinib. Conclusion: We constructed an HBP-related gene signature containing five key genes (GPI, PGM3, UAP1, OGT, MGEA5) which showed a remarkable prognostic value for predicting prognosis and can guide immunotherapy and targeted therapy for osteosarcoma.