Identification of a 3-gene signature based on differentially expressed invasion genes related to cancer molecular subtypes to predict the prognosis of osteosarcoma patients

Abstract

Invasion is a critical pathway leading to tumor metastasis. This study constructed an invasion-related polygenic signature to predict osteosarcoma prognosis. We initially determined two molecular subtypes of osteosarcoma, Cluster1 (C1) and Cluster2 (C2). A 3 invasive-gene signature was established by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis of the differentially expressed genes (DEGs) between the two subtypes, and was validated in internal and two external data sets (GSE21257 and GSE39058). Patients were divided into high- and low-risk groups by their signature, and the prognosis of osteosarcoma patients in the high-risk group was poor. Based on the time-independent receiver operating characteristic (ROC) curve, the area under the curve (AUC) for 1-year and 2-year OS were higher than 0.75 in internal and external cohorts. This signature also showed a high accuracy and independence in predicting osteosarcoma prognosis and a higher AUC in predicting 1-year osteosarcoma survival than other four existing models. In a word, a 3 invasive gene-based signature was developed, showing a high performance in predicting osteosarcoma prognosis. This signature could facilitate clinical prognostic analysis of osteosarcoma.