Identification of a seven-gene prognostic signature using the gene expression profile of osteosarcoma

BACKGROUND: Osteosarcoma is a malignant bone tumor that typically occurs in adolescents or children under 20 years of age. Developing efficient clinical prognostic markers is crucial for improving the treatment of osteosarcoma patients. METHODS: Three datasets related to osteosarcoma were acquired from the Gene Expression Omnibus (GEO) database. A gene signature model was established using the Limma package in the R software, univariate and multivariate survival analyses, and least absolute shrinkage and selection operator (LASSO) algorithms. The gene signature was then verified using external datasets. RESULTS: From the GEO database, 242 differentially expressed genes were identified. A total of 590 unique genes, including 380 genes from the human protein interaction network, were found to be related to biological processes such as bone development and bone cell development. Univariate Cox survival analyses revealed 43 genes that were associated with the prognosis of osteosarcoma patients. A seven-gene signature [retinitis pigmentosa 2 (RP2), polyhydroxybutyrate (PHB), myosin VI (MYO6), mutL homolog 1 (MLH1), Casein kinase 2 beta (CSNK2B), ribosomal protein L37A (RPL37A), and CCAAT/enhancer binding protein alpha (CEBPA)] was developed using LASSO regression analysis and multivariate regression analysis. This gene signature could stratify the prognostic risk of sample cases in the training set, the test set, and the external verification set (P<0.01). The area under the receiver operating characteristic curve for the 5-year survival was higher than 0.72 in both the training and verification groups. CONCLUSIONS: In this study, a seven-gene signature was developed that is highly efficient at predicting the prognosis of patients with osteosarcoma, and therefore, this signature may be a crucial guide in the treatment of these patients.