Comprehensive analysis of hypoxia-related gene signature in cervical cancer

Abstract

Hypoxia significantly influences the growth, metastasis and treatment resistance of cervical cancer (CC), thereby affecting patient prognosis. However, accurately predicting CC survival remains challenging, and the potential of hypoxia-related genes as prognostic markers remains uncertain. In this study, using CC single-cell transcriptional data from the Gene Expression Omnibus database, we employed the InferCNV package to identify tumor cells and used CellChat to confirm stronger intercellular interactions in tumor cells with high-hypoxia status. Next, we identified differentially expressed hypoxia-related genes (DEHLGs) by analyzing data from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Molecular Signature Database, which were further screened using univariate Cox regression and lasso regression analyses, based on which we constructed a hypoxia prognosis model comprising nine prognosis-related genes. Risk scores were generated using multivariate Cox regression analysis. The prognosis model revealed that the overall survival rate was higher in the low-risk than in the high-risk group. The model's performance was assessed using the area under the time-dependent receiver operator characteristic curve, which yielded values of 0.836 and 0.804 for the training and test groups, respectively, indicating a robust prognostic capability of the model. A nomogram based on the nine hypoxia-related genes and training groups exhibited a favorable discriminatory ability for CC. Additionally, using CIBERSORT, we estimated the proportion of immune cells in patients with high-and low-hypoxia risk, revealing a higher proportion of macrophages (M0) and activated mast cells in the high-risk group. We successfully established a prognostic model for CC based on nine hypoxia-related genes to accurately predict the prognosis of affected patients.