ArticleThe EPMA journal2019
Development of a membrane lipid metabolism-based signature to predict overall survival for personalized medicine in ccRCC patients.
Article in The EPMA journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 45 citations in OpenAlex.
- Exploration of prognosis and immune infiltration characteristics in glioblastoma multiforme based on lipid metabolism related genes.Discover oncology · 2025Article
- Lipid Metabolism Related Gene ACSL3 as a Biomarker for Predicting Immunotherapy Outcomes in Lung Adenocarcinoma.Cancer research and treatment · 2025Article
- Metabolic reprogramming in clear cell renal cell carcinoma: core pathways and targeted therapeutic strategies.Frontiers in genetics · 2025Review
- Identification of two novel lipid metabolism-related long non-coding RNAs (SNHG17 and LINC00837) as potential signatures for osteosarcoma prognosis and precise treatment.BMC medical genomics · 2023Article
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- Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma.Frontiers in cell and developmental biology · 2021Article
- Mast cell-based molecular subtypes and signature associated with clinical outcome in early-stage lung adenocarcinoma.Molecular oncology · 2020Article
- Identification and validation of methylation-driven genes prognostic signature for recurrence of laryngeal squamous cell carcinoma by integrated bioinformatics analysis.Cancer cell international · 2020Article
- A novel epigenetic signature for overall survival prediction in patients with breast cancer.Journal of translational medicine · 2019Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClear 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.
methodAssociations 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.
resultMembrane 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.
conclusionOur study might contribute to the optimization of risk stratification for survival and personalized management of ccRCC patients.
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