A risk signature‐based on metastasis‐associated genes to predict survival of patients with osteosarcoma. Issue 7 (2nd January 2020) - Shi, Yi; He, Ronghan; Zhuang, Ze; Ren, Jianhua; Wang, Zhe; Liu, Yuangao; Wu, Jiajun; Jiang, Shihai; Wang, Kun
Abstract: Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients. In this study, a prognosis risk signature was constructed based on metastasis‐associated genes. Four microarrays datasets with clinical information were downloaded from Gene Expression Omnibus, and 256 metastasis‐associated genes were identified by limma package. Further, a protein‐protein interaction network was constructed, and survival analysis was performed using data from the Therapeutically Applicable Research to Generate Effective Treatments data matrix, identifying 19 genes correlated with prognosis. Six genes were selected by the least absolute shrinkage and selection operator regression for multivariate cox analysis. Finally, a three‐gene (MYC, CPE, and LY86) risk signature was constructed, and datasets GSE21257 and GSE16091 were used to validate the prediction efficiency of the signature. The survival times of low‐ and high‐risk groups were significantly different in the training set and validation set. Additionally, gene set enrichment analysis revealed that the genes in the signature may affect the cell cycle, gap junctions, and interleukin‐6 production. Therefore, the three‐gene survival risk signature could potentially predict the prognosis of patients with OS. Further, proteins encoded by CPE and LY86 may provide novel insights into the prediction of OS prognosis and therapeutic targets. Abstract : Osteosarcoma is the most common primary solid malignant bone tumor, metastasis of which affect prognosis poorly. We identified 256 genes associated with metastasis and constructed a three‐gene prognosis risk signature‐based on data of Gene Expression Omnibus datasets and Therapeutically Applicable Research to Generate Effective Treatment data matrix separately. Further, the risk signature was validated using GSE21257 and GSE16091 datasets, which presenting relatively satisfactory predictive accuracy.