Identification of hypoxia-related prognostic signature for ovarian cancer based on Cox regression model

Abstract

Objective : The purpose of this study is to establish a good prognostic risk assessment model of hypoxia genes to evaluate the 3-year and 5-year survival rates of patients with high-grade serous ovarian cancer. Methods : We performed differential analysis of hypoxia genes in the GSE26712 data set. The differential genes were then, analyzed in the TCGA ovarian cancer data set for risk regression analysis and verified in the GSE26712 data set. In addition, we performed a functional enrichment analysis on the genes in the signature of hypoxia, and further analyzed the level of hypoxia risk and immune infiltration. Finally, a nomogram combining the hypoxia risk score, clinical stage, pathological grade, 3-year and 5-year survival rate was constructed. Results : A signature containing 12 hypoxia-related genes was identified as a Cox regression model for predicting the prognosis of ovarian cancer, and verified it in an independent data set. Subsequent enrichment analysis revealed that the signature is related to the immune system. We have also demonstrated a significant relationship between the signature of hypoxia and the infiltration of certain immune cells. Finally, the nomogram shows the accuracy of hypoxia characteristics in predicting ovarian cancer prognosis. Conclusion : We have established a good prognostic risk assessment model for ovarian cancer related to hypoxia risk, which provides personalized survival predictions and possible targeted treatment strategies.