Abstract
Ovarian cancer (OC) is the most common malignant cancer in the female reproductive system. Hypoxia is an important part of tumor immune microenvironment (TIME), which is closely related to cancer progression and could significantly affect cancer metastasis and prognosis. However, the relationship between hypoxia and OC remained unclear. OCs were molecularly subtyped by consensus clustering analysis based on the expression characteristics of hypoxia-related genes. Kaplan-Meier (KM) survival was used to determine survival characteristics across subtypes. Immune infiltration analysis was performed by using Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) and microenvironment cell populations-counter (MCP-Counter). Differential expression analysis was performed by using limma package. Next, univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to build a hypoxia-related risk score model (HYRS). Mutational analysis was applied to determine genomic variation across the HYRS groups. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to compare the effectiveness of HYRS in immunotherapy prediction. We divided OC samples into two molecular subtypes (C1 and C2 subtypes) based on the expression signature of hypoxia genes. Compared with C1 subtype, there was a larger proportion of poor prognosis genotypes in the C2 subtype. And most immune cells scored higher in the C2 subtype. Next, we obtained a HYRS based on 7 genes. High HYRS group had a higher gene mutation rate, such as TP53. Moreover, HYRS performed better than TIDE in predicting immunotherapy effect. Combined with clinicopathological features, the nomogram showed that HYRS had the greatest impact on survival prediction and a strong robustness.