Article Data

  • Views 1009
  • Dowloads 165

Original Research

Open Access

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

  • Wei Sheng1
  • Wen-pei Bai1,*,

1Department of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, 100038 Beijing, China

DOI: 10.31083/j.ejgo4302031 Vol.43,Issue 2,April 2022 pp.247-256

Submitted: 07 November 2021 Accepted: 17 December 2021

Published: 15 April 2022

*Corresponding Author(s): Wen-pei Bai E-mail:


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.


Ovarian cancer; Hypoxia; Tumor microenvironment; Immune cells

Cite and Share

Wei Sheng,Wen-pei Bai. Identification of hypoxia-related prognostic signature for ovarian cancer based on Cox regression model. European Journal of Gynaecological Oncology. 2022. 43(2);247-256.


[1] Webb PM, Jordan SJ. Epidemiology of epithelial ovarian cancer. Best Practice & Research Clinical Obstetrics & Gynaecology. 2017; 41: 3–14.

[2] Sauriol A, Simeone K, Portelance L, Meunier L, Leclerc-Desaulniers K, de Ladurantaye M, et al. Modeling the Diversity of Epithelial Ovarian Cancer through Ten Novel Well Characterized Cell Lines Covering Multiple Subtypes of the Disease. Cancers. 2020; 12: 2222.

[3] Lheureux S, Gourley C, Vergote I, Oza AM. Epithelial ovarian cancer. The Lancet. 2019; 393: 1240–1253.

[4] Lheureux S, Braunstein M, Oza AM. Epithelial ovarian cancer: Evolution of management in the era of precision medicine. CA: A Cancer Journal for Clinicians. 2019; 69: 280–304.

[5] Cobb LP, Gershenson DM. Treatment of Rare Epithelial Ovarian Tumors. Hematology/Oncology Clinics of North America. 2018; 32: 1011–1024.

[6] Konstantinopoulos PA, Norquist B, Lacchetti C, Armstrong D, Grisham RN, Goodfellow PJ, et al. Germline and Somatic Tumor Testing in Epithelial Ovarian Cancer: ASCO Guideline. Journal of Clinical Oncology. 2020; 38: 1222–1245.

[7] Zheng F, Zhang Y, Chen S, Weng X, Rao Y, Fang H. Mechanism and current progress of Poly ADP-ribose polymerase (PARP) inhibitors in the treatment of ovarian cancer. Biomedicine & Pharmacotherapy. 2020; 123: 109661.

[8] Elies A, Rivière S, Pouget N, Becette V, Dubot C, Donnadieu A, et al. The role of neoadjuvant chemotherapy in ovarian cancer. Expert Review of Anticancer Therapy. 2018; 18: 555 566.

[9] Yeung T, Leung CS, Yip K, Au Yeung CL, Wong STC, Mok SC. Cellular and molecular processes in ovarian cancer metastasis. a Review in the Theme: Cell and Molecular Processes in Cancer Metastasis. American Journal of Physiology. Cell Physiology. 2015; 309: C444–C456.

[10] Bookman MA. Can we predict who lives long with ovarian cancer? Cancer. 2019; 125: 4578–4581.

[11] Thibault B, Castells M, Delord J, Couderc B. Ovarian cancer microenvironment: implications for cancer dissemination and chemoresistance acquisition. Cancer Metastasis Reviews. 2014; 33: 17–39.

[12] Chien J, Kuang R, Landen C, Shridhar V. Platinum-sensitive recurrence in ovarian cancer: the role of tumor microenvironment. Frontiers in Oncology. 2013; 3: 251.

[13] Nowak M, Glowacka E, Kielbik M, Kulig A, Sulowska Z, Klink M. Secretion of cytokines and heat shock protein (HspA1a) by ovarian cancer cells depending on the tumor type and stage of disease. Cytokine. 2017; 89: 136–142.

[14] Nowak M, Glowacka E, Szpakowski M, Szyllo K, Malinowski A, Kulig A, et al. Proinflammatory and immunosuppressive serum, ascites and cyst fluid cytokines in patients with early and advanced ovarian cancer and benign ovarian tumors. Neuro Endocrinology Letters. 2010; 31: 375–383.

[15] Zhang H, Yang Q, Lian X, Jiang P, Cui J. Hypoxia-Inducible Factor-1α (HIF-1α) Promotes Hypoxia-Induced Invasion and Metastasis in Ovarian Cancer by Targeting Matrix Metallopep-tidase 13 (MMP13). Medical Science Monitor. 2019; 25: 7202–7208.

[16] Riera-Domingo C, Audigé A, Granja S, Cheng W, Ho P, Baltazar F, et al. Immunity, Hypoxia, and Metabolism–the Ménage à Trois of Cancer: Implications for Immunotherapy. Physiological Reviews. 2020; 100: 1–102.

[17] Jing X, Yang F, Shao C, Wei K, Xie M, Shen H, et al. Role of hypoxia in cancer therapy by regulating the tumor microenvironment. Molecular Cancer. 2019; 18: 157.

[18] Manoochehri Khoshinani H, Afshar S, Najafi R. Hypoxia: a Double-Edged Sword in Cancer Therapy. Cancer Investigation. 2016; 34: 536–545.

[19] Macklin PS, McAuliffe J, Pugh CW, Yamamoto A. Hypoxia and HIF pathway in cancer and the placenta. Placenta. 2017; 56: 8–13.

[20] Lossos IS, Czerwinski DK, Alizadeh AA, Wechser MA, Tibshirani R, Botstein D, et al. Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. The New England Journal of Medicine. 2004; 350: 1828–1837.

[21] Keith B, Simon MC. Hypoxia-inducible factors, stem cells, and cancer. Cell. 2007; 129: 465–472.

[22] Levine AJ, Puzio-Kuter AM. The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science. 2010; 330: 1340–1344.

[23] Vaupel P, Schmidberger H, Mayer A. The Warburg effect: es-ential part of metabolic reprogramming and central contributor to cancer progression. International Journal of Radiation Biology. 2019; 95: 912–919.

[24] Nath A, Chan C. Genetic alterations in fatty acid transport and metabolism genes are associated with metastatic progression and poor prognosis of human cancers. Scientific Reports. 2016; 6: 18669.

[25] Nath A, Li I, Roberts LR, Chan C. Elevated free fatty acid uptake via CD36 promotes epithelial-mesenchymal transition in hepatocellular carcinoma. Scientific Reports. 2015; 5: 14752.

[26] de Gonzalo-Calvo D, López-Vilaró L, Nasarre L, Perez-Olabarria M, Vázquez T, Escuin D, et al. Intratumor cholesteryl ester accumulation is associated with human breast cancer proliferation and aggressive potential: a molecular and clinicopathological study. BMC Cancer. 2015; 15: 460.

[27] Semenza GL. Targeting HIF-1 for cancer therapy. Nature Reviews. Cancer. 2003; 3: 721–732.

[28] Zhu H, Jin Q, Li Y, Ma Q, Wang J, Li D, et al. Melatonin protected cardiac microvascular endothelial cells against oxidative stress injury via suppression of IP3R-[Ca2+]c/VDAC-[Ca2+]m axis by activation of MAPK/ERK signaling pathway. Cell Stress and Chaperones. 2018; 23: 101–113.

[29] Chen Z, Zhang W, Zhang N, Zhou Y, Hu G, Xue M, et al. Down‐regulation of insulin‐like growth factor binding protein 5 is involved in intervertebral disc degeneration via the ERK signalling pathway. Journal of Cellular and Molecular Medicine. 2019; 23: 6368–6377.

[30] Hsu F, Chiang I, Wang W. Induction of apoptosis through extrinsic/intrinsic pathways and suppression of ERK/NF‐κB signalling participate in anti‐glioblastoma of imipramine. Journal of Cellular and Molecular Medicine. 2020; 24: 3982–4000.

[31] Worzfeld T, Pogge von Strandmann E, Huber M, Adhikary T, Wagner U, Reinartz S, et al. The Unique Molecular and Cellular Microenvironment of Ovarian Cancer. Frontiers in Oncology. 2017; 7: 24.

[32] Cheng H, Wang Z, Fu L, Xu T. Macrophage Polarization in the Development and Progression of Ovarian Cancers: an Overview. Frontiers in Oncology. 2019; 9: 421.

[33] Cai DL, Jin L. Immune Cell Population in Ovarian Tumor Microenvironment. Journal of Cancer. 2017; 8: 2915–2923.

[34] Adhikary T, Wortmann A, Finkernagel F, Lieber S, Nist A, Stiewe T, et al. Interferon signaling in ascites-associated macrophages is linked to a favorable clinical outcome in a sub-group of ovarian carcinoma patients. BMC Genomics. 2017; 18: 243.

[35] Liu R, Hu R, Zeng Y, Zhang W, Zhou H. Tumour immune cell infiltration and survival after platinum-based chemotherapy in high-grade serous ovarian cancer subtypes: a gene expression-based computational study. EBioMedicine. 2020; 51: 102602.

[36] Zhang M, He Y, Sun X, Li Q, Wang W, Zhao A, et al. A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients. Journal of Ovarian Research. 2014; 7: 19.

[37] Le Page C, Marineau A, Bonza PK, Rahimi K, Cyr L, Labouba I, et al. BTN3A2 expression in epithelial ovarian cancer is associated with higher tumor infiltrating T cells and a better prognosis. PLoS ONE. 2012; 7: e38541.

Abstracted / indexed in

Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,500 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.

Biological Abstracts Easily discover critical journal coverage of the life sciences with Biological Abstracts, produced by the Web of Science Group, with topics ranging from botany to microbiology to pharmacology. Including BIOSIS indexing and MeSH terms, specialized indexing in Biological Abstracts helps you to discover more accurate, context-sensitive results.

Google Scholar Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.

JournalSeek Genamics JournalSeek is the largest completely categorized database of freely available journal information available on the internet. The database presently contains 39226 titles. Journal information includes the description (aims and scope), journal abbreviation, journal homepage link, subject category and ISSN.

Current Contents - Clinical Medicine Current Contents - Clinical Medicine provides easy access to complete tables of contents, abstracts, bibliographic information and all other significant items in recently published issues from over 1,000 leading journals in clinical medicine.

BIOSIS Previews BIOSIS Previews is an English-language, bibliographic database service, with abstracts and citation indexing. It is part of Clarivate Analytics Web of Science suite. BIOSIS Previews indexes data from 1926 to the present.

Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.

Submission Turnaround Time