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EPMA Journal

pp 1–11 | Cite as

Development of a membrane lipid metabolism–based signature to predict overall survival for personalized medicine in ccRCC patients

  • Maode Bao
  • Run Shi
  • Kai Zhang
  • Yanbo Zhao
  • Yanfang WangEmail author
  • Xuanwen BaoEmail author
Research
  • 5 Downloads

Abstract

Background

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma and is characterized by a dysregulation of changes in cellular metabolism. Altered lipid metabolism contributes to ccRCC progression and malignancy.

Method

Associations among survival potential and each gene ontology (GO) term were analyzed by univariate Cox regression. The results revealed that membrane lipid metabolism had the greatest hazard ratio (HR). Weighted gene co-expression network analysis (WGCNA) was applied to determine the key genes associated with membrane lipid metabolism. Consensus clustering was used to identify novel molecular subtypes based on the key genes. LASSO Cox regression was performed to build a membrane lipid metabolism–based signature. The random forest algorithm was applied to find the most important mutations associated with membrane lipid metabolism. Decision trees and nomograms were constructed to quantify risks for individual patients.

Result

Membrane lipid metabolism stratified ccRCC patients into high- and low-risk groups. Key genes were identified by WGCNA. Membrane lipid metabolism–based signatures exhibited higher prediction efficiency than other clinicopathological traits in both whole cohort and subgroup analyses. The random forest algorithm revealed high associations among the membrane lipid metabolism–based signature and BAP1, PBRM1 and VHL mutations. Decision trees and nomograms indicated high efficiency for risk stratification.

Conclusion

Our study might contribute to the optimization of risk stratification for survival and personalized management of ccRCC patients.

Keywords

Clear cell renal cell carcinoma (ccRCC) Membrane lipid metabolism Gene signature Somatic mutations von Hippel-Lindau (VHL) Risk assessment Overall survival Patient stratification Decision tree Algorithm Gene co-expression network analysis Predictive preventive personalized medicine (PPPM) 

Abbreviations

ccRCC

clear cell renal cell carcinoma

TCGA

The Cancer Genome Atlas

tROC

time-dependent receiver operating characteristic

GSEA

Gene Set Enrichment Analysis

WGCNA

Weighted gene co-expression network analysis

OS

Overall survival

LASSO

Least Absolute Shrinkage and Selection Operator

GO

gene ontology

VHL

von Hippel-Lindau, HR: hazard ratio

Notes

Acknowledgements

We would like to thank Dr. Michael Rosemann for helpful discussions and suggestions.

Authors’ contributions

XW B and R S conceived and designed the experiments. XW B and YF W analyzed the data. XW B, MD B and YF W wrote the paper. YB Z and K Z revised the paper. All authors read and approved the final manuscript.

Funding information

This work was supported by the Zhejiang Provincial Natural Science Foundation (NO. LY16H020005).

Compliance with ethical standards

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

13167_2019_189_MOESM1_ESM.csv (32 kb)
ESM 1 (CSV 31 kb)
13167_2019_189_MOESM2_ESM.csv (1 kb)
ESM 2 (CSV 888 bytes)
13167_2019_189_MOESM3_ESM.csv (9 kb)
ESM 3 (CSV 8 kb)

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Copyright information

© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2019

Authors and Affiliations

  1. 1.Dongyang Chinese Medicine HospitalJinhua CityChina
  2. 2.Ludwig-Maximilians-Universität München (LMU)MunichGermany
  3. 3.Department of Cardiology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina
  4. 4.Technical University Munich (TUM)MunichGermany

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