Background: The dysregulated expression of aerobic glycolysis-related genes is closely related to prostate cancer progression and metastasis. However, reliable prognostic signatures based on aerobic glycolysis have not been well established.
Methods: We screened aerobic glycolysis-related gene modules by weighted gene co-expression network analysis (WGCNA) and established the aerobic glycolysis-related prognostic risk score (AGRS) by univariate Cox and lasso-Cox. In addition, enriched pathways, genomic mutation, and tumor-infiltrating immune cells were analyzed in AGRS subgroups and compared to each other. We also assessed chemotherapeutic drug sensitivity and immunotherapy response among two subgroups.
Results: An aerobic glycolysis-related 14-gene prognostic model has been established. This model has good predictive prognostic performance both in the training dataset and in two independent validation datasets. Higher AGRS group patients had better immunotherapy response. Different AGRS patients were also associated with sensitivity of multiple prostate cancer chemotherapeutic drugs. We also predicted eight aerobic glycolysis-related small-molecule drugs by differentially expressed genes.
Conclusion: In summary, the aerobic glycolysis-derived signatures are promising biomarkers to predict clinical outcomes and therapeutic responses in prostate cancer.
Keywords: aerobic glycolysis; chemotherapy response; immune microenvironment; prognosis model; prostate cancer.
Copyright © 2022 He, He and Li.