Identification of amino acid metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical drug treatment in osteosarcoma.
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BACKGROUND: Osteosarcoma (OS) is the most common bone malignancy in children and adolescents. The 5-year survival rate is only approximately 20% in patients with metastatic and recurrent OS. There is a critical necessity to ascertain prognostic markers that can predict the survival outcomes of OS patients, as well as exploring new therapeutic targets for the treatment of these patients. We aimed to establish and validate an amino acid metabolism-associated gene signature for stratifying OS risk and informing personalized OS management. METHODS: In this study, the RNA sequencing profile of OS patients and a total of 101 genes relevant to amino acid metabolism were subjected to analysis. Cox analysis and the non-negative matrix factorization algorithm were utilized to identify clusters based on genes involved in amino acid metabolism. Then, we performed the least absolute shrinkage and selection operator regression analysis based on these genes to construct a prognostic signature and a nomogram to predict the prognosis of OS patients. Additionally, tumor microenvironment, tumor mutation burden, tumor immunity responses and drug sensitivity were analyzed by R packages. The expression of these genes in OS and normal tissues was determined by immunohistochemistry assays. RESULTS: Four clusters of OS were identified based on genes related to amino acid metabolism, revealing distinct clinical characteristics, tumor microenvironment features, immunotherapy-related profiles, and drug sensitivity patterns among the four clusters. Therefore, we also developed a classifier score calculator based on the amino acid metabolism-related genes. A prognostic signature containing three risk genes and five protective genes and a nomogram were developed to predict the prognosis of OS patients, which had excellent predictive power. Immunohistochemistry assays illustrated that ATF4 and DIO1 were highly expressed in OS tissues while SLC7A7 was suppressed in OS tissues. CONCLUSIONS: In conclusion, multiple databases and analytical techniques were utilized to develop a robust eight-gene signature model for predicting OS outcomes. Based on gene signatures related to amino acid metabolism, researchers identified four subtypes of OS and revealed the heterogeneity of these four subtypes in immunotherapy and chemotherapy. This model has the potential to enhance clinical decision-making for the treatment of OS patients.