Development and Validation of a Hypoxia-related Prognostic Model for Ovarian Cancer

Abstract

Background: The high heterogeneity of ovarian cancer (OC) brings great difficulties to its early diagnosis and prognostic forecast. There is an urgent need to establish a prognostic model of OC based on clinicopathological features and genomics.

Methods: We identified hypoxia-related differentially expressed genes (DEGs) between OC tissues from The Cancer Genome Atlas (TCGA) and normal tissues from the Genotype-Tissue Expression (GTEx). LASSO Cox regression analysis was applied for building a prognostic model in the TCGA-GTEx cohorts, and its predictive value was validated in the GEO-OC cohort. Functional enrichment analysis was performed to investigate the underlying mechanisms. By constructing a hypoxia model of the SKOV3 cell line and applying qRT-PCR, we investigated the relationship between hypoxia with two novel genes in the prognostic model (ISG20 and ANGPTL4).

Results: Twelve prognostic hypoxia-related DEGs were identified, and nine of them were selected to establish a prognostic model. OC patients were stratified into two risk groups, and the high-risk group showed reduced survival time compared to the low-risk group upon survival analysis. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent risk factor for overall survival. The biological function of the identified prognostic hypoxia-related gene signature was involved in immune cell infiltration. Low expression of ISG20 was observed in the CoCl₂-mimicked hypoxic SKOV3 cell line and negatively correlated with HIF-1α.

Conclusion: Our findings showed that this hypoxia-related gene signature could serve as a satisfactory prognostic classifier for OC and will be beneficial to the research and development of targeted therapeutic strategies.

Keywords: Hypoxia; LASSO cox regression analysis.; differentially expressed genes (DEGs); immune cells infiltration; ovarian cancer; prognostic model.