ArticleAnnals of translational medicine2021
A systematic analysis of a potential metabolism-related prognostic signature for breast cancer patients.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integration of tumor microenvironment and metabolic signatures reveals TMPI-defined prognostic and immunotherapy-relevant phenotypes in breast cancer.Frontiers in pharmacology · 2026Article
- Host and bacterial urine proteomics might predict treatment outcomes for immunotherapy in advanced non-small cell lung cancer patients.Frontiers in immunology · 2025Article
- A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes.Biochemistry and biophysics reports · 2023Article
- Increased Expression of QPRT in Breast Cancer Infers a Poor Prognosis and Is Correlated to Immunocytes Infiltration.Journal of healthcare engineering · 2022Article
- Signature based on metabolic-related gene pairs can predict overall survival of osteosarcoma patients.Cancer medicine · 2021Article
- Silencing RPL9 promotes malignant proliferation in breast cancer cells.International journal of immunopathology and pharmacologyArticle
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Authors and funding
12 authors.
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Abstract
backgroundMetabolic pathways play an essential role in breast cancer. However, the role of metabolism-related genes in the early diagnosis of breast cancer remains unknown.
methodsIn our study, RNA sequencing (RNA-seq) expression data and clinicopathological information from The Cancer Genome Atlas (TCGA) and GSE20685 were obtained. Univariate cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expressed metabolism-related genes. Then, the formula of the metabolism-related risk model was composed, and the risk score of each patient was calculated. The breast cancer patients were divided into high-risk and low-risk groups with a cutoff of the median expression value of the risk score, and the prognostic analysis was also used to analyze the survival time between these two groups. In the end, we also analyzed the expression, interaction, and correlation among genes in the metabolism-related gene risk model.
resultsThe results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group in both TCGA and GSE20685 datasets. In addition, after adjusting for different clinicopathological features in multivariate analysis, the metabolism-related risk model remained an independent prognostic indicator in TCGA dataset.
conclusionsIn summary, we systematically developed a potential metabolism-related gene risk model for predicting prognosis in breast cancer patients.
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