A potential prognostic prediction model for metastatic osteosarcoma based on bioinformatics analysis

Osteosarcoma (OS) is a malignant primary bone tumor with a high incidence. This study aims to construct a prognostic prediction model by screening the prognostic mRNA of metastatic OS. Data on four eligible expression profiles from the National Center for Biotechnology Information Gene Expression Omnibus repository were obtained based on inclusion criteria and defined as the training set or the validation set. The differentially expressed genres (DEGs) between meta- static and non-metastatic OS samples in the training set were first identified, and DEGs related to prognosis were screened by univariate Cox regression analysis. In total, 107 DEGs related to the prognosis of metastatic OS were identified. Then, 46 DEGs were isolated as the optimized prognostic gene signature, and a metastatic-OS discriminating classifier was constructed, which had a high accuracy in distinguishing metastatic from non-metastatic OS samples. Furthermore, four optimized prognostic gene signatures (ALOX5AP, COL21A1, HLA-DQB1, and LDHB) were further screened, and the prognostic prediction model for metastatic OS was constructed. This model possesses a relatively satisfying prediction ability both in the training set and validation set. The prognostic prediction model that was constructed based on the four prognostic mRNA signatures has a high predictive ability for the prognosis of metastatic OS.