Author
Listed:
- Jin-Huan Wei
(First Affiliated Hospital, Sun Yat-sen University)
- Ahmed Haddad
(University of Texas Southwestern Medical Center at Dallas)
- Kai-Jie Wu
(First Affiliated Hospital of Xi’an Jiaotong University)
- Hong-Wei Zhao
(Affiliated Yantai Yuhuangding Hospital, Qingdao University Medical College)
- Payal Kapur
(University of Texas Southwestern Medical Center at Dallas)
- Zhi-Ling Zhang
(Cancer Center, Sun Yat-sen University)
- Liang-Yun Zhao
(Affiliated Hospital of Kunming University of Science and Technology)
- Zhen-Hua Chen
(First Affiliated Hospital, Sun Yat-sen University)
- Yun-Yun Zhou
(Quantitive Biomedical Research Center, University of Texas Southwestern Medical Center at Dallas)
- Jian-Cheng Zhou
(University of Texas Southwestern Medical Center at Dallas)
- Bin Wang
(University of Texas Southwestern Medical Center at Dallas)
- Yan-Hong Yu
(Affiliated Hospital of Kunming University of Science and Technology)
- Mu-Yan Cai
(Cancer Center, Sun Yat-sen University)
- Dan Xie
(Cancer Center, Sun Yat-sen University)
- Bing Liao
(First Affiliated Hospital, Sun Yat-sen University)
- Cai-Xia Li
(School of Mathematics and Computational Science, Sun Yat-sen University)
- Pei-Xing Li
(School of Mathematics and Computational Science, Sun Yat-sen University)
- Zong-Ren Wang
(First Affiliated Hospital, Sun Yat-sen University)
- Fang-Jian Zhou
(Cancer Center, Sun Yat-sen University)
- Lei Shi
(Affiliated Yantai Yuhuangding Hospital, Qingdao University Medical College)
- Qing-Zuo Liu
(Affiliated Yantai Yuhuangding Hospital, Qingdao University Medical College)
- Zhen-Li Gao
(Affiliated Yantai Yuhuangding Hospital, Qingdao University Medical College)
- Da-Lin He
(First Affiliated Hospital of Xi’an Jiaotong University)
- Wei Chen
(First Affiliated Hospital, Sun Yat-sen University)
- Jer-Tsong Hsieh
(University of Texas Southwestern Medical Center at Dallas)
- Quan-Zhen Li
(University of Texas Southwestern Medical Center at Dallas)
- Vitaly Margulis
(University of Texas Southwestern Medical Center at Dallas)
- Jun-Hang Luo
(First Affiliated Hospital, Sun Yat-sen University)
Abstract
Clear cell renal cell carcinomas (ccRCCs) display divergent clinical behaviours. Molecular markers might improve risk stratification of ccRCC. Here we use, based on genome-wide CpG methylation profiling, a LASSO model to develop a five-CpG-based assay for ccRCC prognosis that can be used with formalin-fixed paraffin-embedded specimens. The five-CpG-based classifier was validated in three independent sets from China, United States and the Cancer Genome Atlas data set. The classifier predicts the overall survival of ccRCC patients (hazard ratio=2.96−4.82; P=3.9 × 10−6−2.2 × 10−9), independent of standard clinical prognostic factors. The five-CpG-based classifier successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome in respective clinical stages and individual ‘stage, size, grade and necrosis’ scores. Moreover, methylation at the five CpGs correlates with expression of five genes: PITX1, FOXE3, TWF2, EHBP1L1 and RIN1. Our five-CpG-based classifier is a practical and reliable prognostic tool for ccRCC that can add prognostic value to the staging system.
Suggested Citation
Jin-Huan Wei & Ahmed Haddad & Kai-Jie Wu & Hong-Wei Zhao & Payal Kapur & Zhi-Ling Zhang & Liang-Yun Zhao & Zhen-Hua Chen & Yun-Yun Zhou & Jian-Cheng Zhou & Bin Wang & Yan-Hong Yu & Mu-Yan Cai & Dan Xi, 2015.
"A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma,"
Nature Communications, Nature, vol. 6(1), pages 1-11, December.
Handle:
RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9699
DOI: 10.1038/ncomms9699
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