ArticleContrast media & molecular imaging2022
Identification of Ten-Gene Related to Lipid Metabolism for Predicting Overall Survival of Breast Invasive Carcinoma.
Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Single nucleus RNA sequencing identifies PLXNA4 upregulation in end-stage arrhythmogenic right ventricular cardiomyopathy.iScience · 2026Article
- APOL1 risk genotypes influence DNA methylation across multiple genomic elements in APOL1-APOL4- MYH9 region in African Americans.Clinical epigenetics · 2026Article
- Machine learning identification of molecular targets for medulloblastoma subgroups using microarray gene fingerprint analysis.Computational and structural biotechnology journal · 2025Article
- Suppressing mitochondrial inner membrane protein (IMMT) inhibits the proliferation of breast cancer cells through mitochondrial remodeling and metabolic regulation.Scientific reports · 2024Article
- Apolipoprotein L3 inhibits breast cancer proliferation and modulates cell cycle via the P53 pathway.Journal of Cancer · 2024Article
- Construct ceRNA Network and Risk Model of Breast Cancer Using Machine Learning Methods under the Mechanism of Cuproptosis.Diagnostics (Basel, Switzerland) · 2023Article
- The oxidative aging model integrated various risk factors in type 2 diabetes mellitus at system level.Frontiers in endocrinology · 2023Article
- APOL4, a Novel Immune-Related Prognostic Biomarker for Glioma.Journal of clinical medicine · 2022Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Predicting the risk of poor prognosis of breast cancer is crucial to treating breast cancer. This study investigated the prognostic assessment of 10 lipid metabolism-related genes constructed as breast cancer models based on this study. Methods: The TCGA database was used to obtain clinical information and expression data of breast cancer patients, and GSEA analysis and univariate and multivariate Cox proportional risk regression models were performed to identify lipid metabolism genes closely associated with overall survival (OS) of breast cancer patients and to construct a prognostic risk score model based on lipid metabolism gene markers. The Kaplan-Meier method was used to analyze the survival status of patients with high and low-risk scores, and ROC curves assessed the accuracy of this risk score. Finally, the relationship between this risk score and clinicopathological characteristics of BRCA was analyzed in a stratified manner, and the validity of this risk score as an independent prognostic factor was determined using univariate and multivariate Cox regression analyses. Results: One hundred and forty-four differentially expressed lipid metabolism-related genes were identified in cancer and paracancerous tissues in BRCA, 21 of which were associated with overall survival (OS) in BRCA ( Conclusion: By mining the TCGA database, we identified 10 lipid metabolism-related genes APOL4, NR1H3, SLC25A5, APOL3, OSBPL1A, DYNLT1, IMMT, MAP2K6, ZDHHC8, and RAB2A, which are closely related to the prognosis of BRCA patients, and constructed a prognostic risk scoring system based on 10 lipid metabolism genes tags.
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