Establishment of A Nomogram for Predicting the Prognosis of Soft Tissue Sarcoma Based on Seven Glycolysis-Related Gene Risk Score - Liu, Yuhang; Liu, Changjiang; Zhang, Hao; Yi, Xinzeyu; Yu, Aixi (Frontiers in Genetics, 2021)
Soft tissue sarcoma (STS) is a group of tumors with a low incidence and a complex type. Therefore, it is an arduous task to accurately diagnose and treat them. Glycolysis-related genes are closely related to tumor progression and metastasis. Hence, our study is dedicated to the development of risk characteristics and nomograms based on glycolysis-related genes to assess the survival possibility of patients with STS. All data sets used in our research include gene expression data and clinical medical characteristics in the Genomic Data Commons Data Portal (National Cancer Institute) Soft Tissue Sarcoma (TCGA SARC) and GEO database, gene sequence data of corresponding non-diseased human tissues in the Genotype Tissue Expression (GTEx).Next, transcriptome data in TCGA SARC was analyzed as the training set. By analyzing the expression data in training set, we constructed a glycolysis-related gene risk signature and nomogram, which were confirmed in external test set. Finally, we identified and verified the 7-glycolysis-related gene signature that is highly correlated with the overall survival of STS patients, which performed excellently in the evaluation of the size of AUC, and calibration curve. As well as, the results of the analysis of univariate and multivariate Cox regression demonstrated that this 7- glycolysis-related gene characteristic act independently as an influence predictor for STS patients. Therefore, a prognostic-related nomogram combing 7-gene signature with clinical influencing features was constructed to predict OS of patients with STS in the training set that demonstrated strong predictive values for survival. These results demonstrate that both glycolysis-related gene risk signature and nomogram were efficient prognostic indicators for patients with STS. These findings may contribute to make individualize clinical decisions on prognosis and treatment.