Abstract
ObjectivesOsteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.MethodsClinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.ResultsTwo molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.ConclusionExpression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.
Highlights
Osteosarcoma is the most common primary malignant bone tumor in children and adolescent, which is characterized by poor prognosis and high metastasis rate (Niu et al, 2020; Song et al, 2020)
Expression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately
The consensus clustering approach was conducted to divide the osteosarcoma patients in the training cohort into subgroups based on 74 prognostic genes generated from univariable Cox analysis (Supplementary Table 1)
Summary
Osteosarcoma is the most common primary malignant bone tumor in children and adolescent, which is characterized by poor prognosis and high metastasis rate (Niu et al, 2020; Song et al, 2020). The 5-year survival rate of osteosarcoma patients with or without lung metastasis is 60–70%, and 20%, respectively, which has remained stagnant over the past 35 years and is far from satisfaction (Kansara et al, 2014; Negri et al, 2019). Tracing back to the source, the main reason for the poor prognosis in osteosarcoma is the high extent of tumor heterogeneity caused by significant genomic instability (Whelan and Davis, 2018; Wang et al, 2019). It is necessary to develop a risk stratification method and identify prognostic genes for personalized targeted therapy of osteosarcoma patients
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