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Original Research

Open Access

Comprehensive analysis of hypoxia-related gene signature in cervical cancer

  • Tingting He1,2,†
  • Xiaoyu Tang1,†
  • Siru Chen3
  • Xin Chen1
  • Fuye Lin4
  • Minmin Yu5,*,
  • Changsong Lin4,*,

1Nanjing University of Chinese Medicine, 210023 Nanjing, Jiangsu, China

2Department of Gynecology, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, 210022 Nanjing, Jiangsu, China

3Department of Traditional Chinese Medicine, The Affiliated Huai’an Hospital of Xuzhou Medical University and The Second People’s Hospital of Huai’an, 223002 Huai’an, Jiangsu, China

4Department of Bioinformatics, Nanjing Medical University, 211166 Nanjing, Jiangsu, China

5Department of Gynecology, Nanjing Hospital Affiliated to Nanjing University of Chinese Medicine, 210003 Nanjing, Jiangsu, China

DOI: 10.22514/ejgo.2023.105 Vol.44,Issue 6,December 2023 pp.105-121

Submitted: 14 July 2023 Accepted: 14 August 2023

Published: 15 December 2023

*Corresponding Author(s): Minmin Yu E-mail:
*Corresponding Author(s): Changsong Lin E-mail:

† These authors contributed equally.


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.


Hypoxia; Cervical cancer; Prognosis; Prediction; Gene signature

Cite and Share

Tingting He,Xiaoyu Tang,Siru Chen,Xin Chen,Fuye Lin,Minmin Yu,Changsong Lin. Comprehensive analysis of hypoxia-related gene signature in cervical cancer. European Journal of Gynaecological Oncology. 2023. 44(6);105-121.


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