Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis - Chen, Xingyu; Lan, Hua; He, Dong; Xu, Runshi; Zhang, Yao; Cheng, Yaxin; Chen, Haotian; Xiao, Songshu; Cao, Ke (Frontiers in Immunology, 2021)
Background: Ovarian cancer (OC) has the highest mortality rate in any gynecologic malignancy. Hypoxia is a driver of the malignant progression in OC, which result in poor prognosis. We herein aimed to develop a validated model that was based on the hypoxia genes to systematically evaluate its prognosis in tumor immune microenvironment(TIM). Results: Here, we identified 395 hypoxia-immune genes using weighted gene co-expression network analysis (WGCNA). We then established a nine hypoxia-related genes risk model using least absolute shrinkage and selection operator (LASSO) Cox regression, which efficiently distinguished high-risk patients from low-risk ones. We found that patients in high-risk group were significantly related to poor prognosis. The high-risk group showed a unique immunosuppressive microenvironment, lower antigen presentation, and higher levels of inhibitory cytokines. There were also significant differences in copy-number variation and somatic mutation between the high- and low-risk groups, indicating that immune escape may be associated with the high-risk group. Tumor immune dysfunction and exclusion (TIDE) and submap algorithms showed that low-risk patients are significantly responsive to programmed cell death protein-1 (PD-1) inhibitors . Conclusions: In this study , we highlighted the clinical significance of hypoxia in OC, established a hypoxia-related model for predicting prognosis and providing potential immunotherapy strategies.