Research Paper Volume 12, Issue 14 pp 14933—14948

A new survival model based on ferroptosis-related genes for prognostic prediction in clear cell renal cell carcinoma

Guangzhen Wu1, *, , Qifei Wang1, *, , Yingkun Xu2, , Quanlin Li1, , Liang Cheng3,4, ,

  • 1 Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
  • 2 Department of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
  • 3 Department of Pathology and Laboratory Medicine, Indianapolis, IN 46202, USA
  • 4 Department of Urology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
* Equal contribution

Received: March 17, 2020       Accepted: June 4, 2020       Published: July 20, 2020
How to Cite

Copyright © 2020 Wu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


In this study, we analyzed the clinical significance of ferroptosis-related genes (FRGs) in 32 cancer types in the GSCA database. We detected a 2-82% mutation rate among 36 FRGs. In clear cell renal cell carcinoma (ccRCC; n=539) tissues from the The Cancer Genome Atlas database, 30 of 36 FRGs were differentially expressed (up- or down-regulated) compared to normal kidney tissues (n=72). Consensus clustering analysis identified two clusters of FRGs based on similar co-expression in ccRCC tissues. We then used LASSO regression analysis to build a new survival model based on five risk-related FRGs (CARS, NCOA4, FANCD2, HMGCR, and SLC7A11). Receiver operating characteristic curve analysis confirmed good prognostic performance of the new survival model with an area under the curve of 0.73. High FANCD2, CARS, and SLC7A11 expression and low HMGCR and NCOA4 expression were associated with high-risk ccRCC patients. Multivariate analysis showed that risk score, age, stage, and grade were independent risk factors associated with prognosis in ccRCC. These findings demonstrate that this five risk-related FRG-based survival model accurately predicts prognosis in ccRCC patients, and suggest FRGs are potential prognostic biomarkers and therapeutic targets in several cancer types.


CNV: Copy number variation; KIRC: Kidney renal clear cell carcinoma; UCEC: Uterine corpus endometrial carcinoma; ccRCC: Clear cell renal cell carcinoma; FRGs: Ferroptosis-related genes; PPI: Protein-protein interaction; SNV: Single nucleotide variation; PCA: Principal component analysis; LUAD: lung adenocarcinoma; LUSC: squamous cell carcinoma; CPTAC: Clinical Proteomic Tumor Analysis Consortium; OS: overall survival; BLCA: Bladder Urothelial Carcinoma; CHOL: Cholangiocarcinoma; SKCM: Skin Cutaneous Melanoma.