Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

免疫系统 比例危险模型 肿瘤科 医学 免疫疗法 恶性肿瘤 卵巢癌 内科学 生物信息学 生物 肿瘤微环境 癌症 缺氧(环境) 免疫学 化学 有机化学 氧气
作者
Xingyu Chen,Hua Lan,Dong He,Runshi Xu,Yao Zhang,Ying Cheng,Haotian Chen,Songshu Xiao,Ke Cao
出处
期刊:Frontiers in Immunology [Frontiers Media]
卷期号:12 被引量:16
标识
DOI:10.3389/fimmu.2021.645839
摘要

Background Ovarian cancer (OC) has the highest mortality rate among gynecologic malignancy. Hypoxia is a driver of the malignant progression in OC, which results 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 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 high-risk patients were significantly related to poor prognosis. The high-risk group showed unique immunosuppressive microenvironment, lower antigen presentation, and higher levels of inhibitory cytokines. There were also significant differences in somatic copy number alterations (SCNAs) and mutations between the high- and low-risk groups, indicating immune escape in 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 and established a hypoxia-related model for predicting prognosis and providing potential immunotherapy strategies.
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