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Original Research

Open Access

Identification of ferroptosis-related risk signature and correlation with the overall survival of ovarian cancer

  • Yibin Liu1,†
  • Xin Xu2,†
  • Jianlei Wu3,†
  • Zhongkang Li1
  • Ye Zhang4
  • Xiaoxiao Zhang4
  • Shike Shui4
  • Hui Li4
  • Tiantian Wang4
  • Juan Zhai4
  • Ruixia Guo4,*,
  • Yanpeng Tian4,*,

1Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, 050000 Shijiazhuang, Hebei, China

2Department of Gynecological Endocrinology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, 100026 Beijing, China

3Department of Gynecological Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250021 Jinan, Shandong, China

4Department of Obstetrics and Gynecology, The First Affiliated Hospital of Zhengzhou University, 450000 Zhengzhou, Henan, China

DOI: 10.22514/ejgo.2023.023 Vol.44,Issue 2,April 2023 pp.58-66

Submitted: 20 May 2022 Accepted: 10 June 2022

Published: 15 April 2023

*Corresponding Author(s): Ruixia Guo E-mail: grxcdxzzu@163.com
*Corresponding Author(s): Yanpeng Tian E-mail: tianyanp2020@163.com

† These authors contributed equally.

Abstract

Ovarian cancer is a lethal female reproductive system malignancy. However, the physiological roles of ferroptosis in ovarian cancer remains unclear. In this study, biological information databases were screened to characterize and examine the differentially expressed ferroptosis-related genes between ovarian cancer and normal ovarian tissue, and to further investigate a novel risk signature for predicting the prognosis of ovarian cancer. Molecular and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database. Based on these data, we identified differentially expressed ferroptosis-related genes, and construct a multigene risk signature by least absolute shrinkage and celection operator (LASSO) Cox regression to predict the prognosis of ovarian cancer. Univariate and multivariate Cox regression analysis were used to verify the prognostic value of the signature. We constructed a risk signature for ovarian cancer based on differentially expressed ferroptosis-related genes between normal ovarian samples and ovarian cancer samples. Referring to median risk score, patients were divided into high-risk group and low-risk group. We performed Cox regression analysis, principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) analysis, Kaplan-Meier Survival analysis and receiver operating characteristic (ROC) curve to verify the accuracy of the predicted value of the risk signature. The overall survival rates in low-risk group was significantly higher than that in high-risk group. In addition, the area under the curve (AUC) of the ROC curve reached 0.684 at 1 year, 0.682 at 2 years and 0.661 at 3 years. Functional analysis indicated differentially expressed ferroptosis-related genes were enriched in immune-related cells. The ferroptosis-related genes signature could predict the prognosis of ovarian cancer. These genes might be potential therapeutic targets.


Keywords

Ovarian cancer; Ferroptosis; Risk signature; Prognosis; Overall survival


Cite and Share

Yibin Liu,Xin Xu,Jianlei Wu,Zhongkang Li,Ye Zhang,Xiaoxiao Zhang,Shike Shui,Hui Li,Tiantian Wang,Juan Zhai,Ruixia Guo,Yanpeng Tian. Identification of ferroptosis-related risk signature and correlation with the overall survival of ovarian cancer. European Journal of Gynaecological Oncology. 2023. 44(2);58-66.

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