Background: Oxidative stress plays a critical role in tumorigenesis, tumor development, and resistance to therapy. A systematic analysis of the interactions between antioxidant gene expression and the prognosis of patients with sarcoma is lacking but urgently needed.

Methods: Gene expression and clinical data of patients with sarcoma were derived from The Cancer Genome Atlas Sarcoma (training cohort) and Gene Expression Omnibus (validation cohorts) databases. Least absolute shrinkage, selection operator regression, and Cox regression were used to develop prognostic signatures for overall survival (OS) and disease-free survival (DFS). Based on the signatures and clinical features, two nomograms for predicting 2-, 4-, and 6-year OS and DFS were established.

Results: On the basis of the training cohort, we identified five-gene (CHAC2, GPX5, GSTK1, PXDN, and S100A9) and six-gene (GGTLC2, GLO1, GPX7, GSTK1, GSTM5, and IPCEF1) signatures for predicting OS and DFS, respectively, in patients with sarcoma. Kaplan–Meier survival analysis of the training and validation cohorts revealed that patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group. On the basis of the signatures and other independent risk factors, we established two models for predicting OS and DFS that showed excellent calibration and discrimination. For the convenience of clinical application, we built web-based calculators (OS: https://quankun.shinyapps.io/sarcOS/; DFS: https://quankun.shinyapps.io/sarcDFS/).

Conclusions: The antioxidant gene signature models established in this study can be novel prognostic predictors for sarcoma.