EPMA Journal

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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



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.


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.


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.


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


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) 



clear cell renal cell carcinoma


The Cancer Genome Atlas


time-dependent receiver operating characteristic


Gene Set Enrichment Analysis


Weighted gene co-expression network analysis


Overall survival


Least Absolute Shrinkage and Selection Operator


gene ontology


von Hippel-Lindau, HR: hazard ratio



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

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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)


  1. 1.
    Mickley A, Kovaleva O, Kzhyshkowska J, Gratchev A. Molecular and immunologic markers of kidney cancer—potential applications in predictive, preventive and personalized medicine. EPMA J. 2015;6(1):20.CrossRefGoogle Scholar
  2. 2.
    Guo G, Gui Y, Gao S, Tang A, Hu X, Huang Y, et al. Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma. Nat Genet. 2012;44(1):17.CrossRefGoogle Scholar
  3. 3.
    Varela I, Tarpey P, Raine K, Huang D, Ong CK, Stephens P, et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature. 2011;469(7331):539.CrossRefGoogle Scholar
  4. 4.
    Dalgliesh GL, Furge K, Greenman C, Chen L, Bignell G, Butler A, et al. Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature. 2010;463(7279):360.CrossRefGoogle Scholar
  5. 5.
    Du W, Zhang L, Brett-Morris A, Aguila B, Kerner J, Hoppel CL, et al. HIF drives lipid deposition and cancer in ccRCC via repression of fatty acid metabolism. Nat Commun. 2017;8(1):1769.CrossRefGoogle Scholar
  6. 6.
    Doberstein K, Wieland A, Lee SBB, Blaheta RAA, Wedel S, Moch H, et al. L1-CAM expression in ccRCC correlates with shorter patients survival times and confers chemoresistance in renal cell carcinoma cells. Carcinogenesis. 2010;32(3):262–70.CrossRefGoogle Scholar
  7. 7.
    Wang Y, Zhang Q, Gao Z, Xin S, Zhao Y, Zhang K, et al. A novel 4-gene signature for overall survival prediction in lung adenocarcinoma patients with lymph node metastasis. Cancer Cell Int. 2019;19(1):100.CrossRefGoogle Scholar
  8. 8.
    Wang Y, Deng H, Xin S, et al. Prognostic and predictive value of three DNA methylation signatures in lung adenocarcinoma[J]. Frontiers in genetics, 2019;10:349.Google Scholar
  9. 9.
    Li N, Zhan X. Identification of clinical trait–related lncRNA and mRNA biomarkers with weighted gene co-expression network analysis as useful tool for personalized medicine in ovarian cancer[J]. EPMA Journal. 2019;10(3):273–90.CrossRefGoogle Scholar
  10. 10.
    Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J. 2018;9(1):77–102.CrossRefGoogle Scholar
  11. 11.
    Fröhlich H, Patjoshi S, Yeghiazaryan K, Kehrer C, Kuhn W, Golubnitschaja O. Premenopausal breast cancer: potential clinical utility of a multi-omics based machine learning approach for patient stratification. EPMA J. 2018;9(2):175–86.CrossRefGoogle Scholar
  12. 12.
    Berliner L, Lemke HU, van Sonnenberg E, Ashamalla H, Mattes MD, Dosik D, et al. Model-guided therapy for hepatocellular carcinoma: a role for information technology in predictive, preventive and personalized medicine. EPMA J. 2014;5(1):16.CrossRefGoogle Scholar
  13. 13.
    Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14(1):7.CrossRefGoogle Scholar
  14. 14.
    Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559.CrossRefGoogle Scholar
  15. 15.
    Wang Y, Xin S, Zhang K, Shi R, Bao X. Low GAS5 levels as a predictor of poor survival in patients with lower-grade gliomas. J Oncol. 2019;2019:15.Google Scholar
  16. 16.
    Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26(12):1572–3.CrossRefGoogle Scholar
  17. 17.
    Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol. 1996;58(1):267–88.Google Scholar
  18. 18.
    Lendahl U, Lee KL, Yang H, Poellinger L. Generating specificity and diversity in the transcriptional response to hypoxia. Nat Rev Genet. 2009;10(12):821.CrossRefGoogle Scholar
  19. 19.
    Bubnov R, Polivka J, Zubor P, Konieczka K, Golubnitschaja O. “Pre-metastatic niches” in breast cancer: are they created by or prior to the tumour onset? “Flammer Syndrome” relevance to address the question. EPMA J. 2017;8(2):141–57.CrossRefGoogle Scholar
  20. 20.
    Clausson C-M, Grundberg I, Weibrecht I, Nilsson M, Söderberg O. Methods for analysis of the cancer microenvironment and their potential for disease prediction, monitoring and personalized treatments. EPMA J. 2012;3(1):7.CrossRefGoogle Scholar
  21. 21.
    Josifova T, Plestina-Borjan I, Henrich PB. Proliferative diabetic retinopathy: predictive and preventive measures at hypoxia induced retinal changes. EPMA J. 2010;1(1):73–7.CrossRefGoogle Scholar
  22. 22.
    Kwon TJ, Ro JY, Mackay B. Clear-cell carcinoma: an ultrastructural study of 57 tumors from various sites. Ultrastruct Pathol. 1996;20(6):519–27.CrossRefGoogle Scholar
  23. 23.
    Gameiro PA, Yang J, Metelo AM, Pérez-Carro R, Baker R, Wang Z, et al. In vivo HIF-mediated reductive carboxylation is regulated by citrate levels and sensitizes VHL-deficient cells to glutamine deprivation. Cell Metab. 2013;17(3):372–85.CrossRefGoogle Scholar
  24. 24.
    Papandreou I, Cairns RA, Fontana L, Lim AL, Denko NC. HIF-1 mediates adaptation to hypoxia by actively downregulating mitochondrial oxygen consumption. Cell Metab. 2006;3(3):187–97.CrossRefGoogle Scholar
  25. 25.
    Bensaad K, Favaro E, Lewis CA, Peck B, Lord S, Collins JM, et al. Fatty acid uptake and lipid storage induced by HIF-1α contribute to cell growth and survival after hypoxia-reoxygenation. Cell Rep. 2014;9(1):349–65.CrossRefGoogle Scholar
  26. 26.
    Sena CM, Bento CF, Pereira P, Seiça R. Diabetes mellitus: new challenges and innovative therapies. EPMA J. 2010;1(1):138–63.CrossRefGoogle Scholar
  27. 27.
    Zhou H, Tang K, Liu H, Zeng J, Li H, Yan L, et al. Regulatory network of two tumor-suppressive noncoding RNAs interferes with the growth and metastasis of renal cell carcinoma. Mol Ther Nucleic Acids. 2019;16:554–65.CrossRefGoogle Scholar
  28. 28.
    Zhang C, Kuang M, Li M, Feng L, Zhang K, Cheng S. SMC4, which is essentially involved in lung development, is associated with lung adenocarcinoma progression. Sci Rep. 2016;6:34508.CrossRefGoogle Scholar
  29. 29.
    Zhang C, Zhu C, Chen H, Li L, Guo L, Jiang W, et al. Kif18A is involved in human breast carcinogenesis. Carcinogenesis. 2010;31(9):1676–84.CrossRefGoogle Scholar
  30. 30.
    Nagahara M, Nishida N, Iwatsuki M, Ishimaru S, Mimori K, Tanaka F, et al. Kinesin 18A expression: clinical relevance to colorectal cancer progression. Int J Cancer. 2011;129(11):2543–52.CrossRefGoogle Scholar
  31. 31.
    Ye L, Li F, Song Y, Yu D, Xiong Z, Li Y, et al. Overexpression of CDCA7 predicts poor prognosis and induces EZH2-mediated progression of triple-negative breast cancer. Int J Cancer. 2018;143(10):2602–13.CrossRefGoogle Scholar
  32. 32.
    Network CGAR. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;499(7456):43.CrossRefGoogle Scholar

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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