Development of a membrane lipid metabolism–based signature to predict overall survival for personalized medicine in ccRCC patients
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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.
KeywordsClear 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
Least Absolute Shrinkage and Selection Operator
von Hippel-Lindau, HR: hazard ratio
We would like to thank Dr. Michael Rosemann for helpful discussions and suggestions.
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.
This work was supported by the Zhejiang Provincial Natural Science Foundation (NO. LY16H020005).
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The authors declare that they have no competing interests.
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