ArticleDiscover oncology2025
Integrated bulk and single-cell transcriptomic analysis unveiled a novel cuproptosis-related lipid metabolism gene molecular pattern and a risk index for predicting prognosis and antitumor drug sensitivity in breast cancer.
Article in Discover oncology, 2025. 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.
- Prognostic Relevance of Neddylation-Related Genes in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Cuproptosis scoring system identifies novel molecular subtypes and modulates biological behavior in breast cancer.Discover oncology · 2025Article
- Pan cancer research reveals the role of PTGDS in tumor suppression and immune regulation.NPJ precision oncology · 2025Article
- Survival impact of adjuvant radiotherapy in early stage low risk elderly male breast cancer patients treated with breast conserving surgery.Scientific reports · 2025Article
- Stromal PLAU mediates tumor progression and informs a novel therapeutic target in triple-negative breast cancer.Cancer cell international · 2025Article
- Downregulation of PIP4K2C inhibits the breast cancer cell proliferation, migration and invasion.Translational oncology · 2025Article
- Novel Causal Evidence for the Role of Circulating Lipid Metabolites in Breast Cancer Risk: A Bidirectional Mendelian Randomization Analysis.Journal of nutrition and metabolism · 2025Article
- Integrative multidimensional analysis of age-associated synthetic lethal genes and development of a prognostic model in breast cancer.Frontiers in immunology · 2025Article
- Prognostic differential subpopulation classification and immunotherapy response prediction in pancreatic cancer patients based on the gene features of necrotizing apoptosis.Frontiers in immunology · 2025Article
- Multi-cohort validation of a lipid metabolism and ferroptosis-associated index for prognosis and immunotherapy response prediction in hormone receptor-positive breast cancer.International journal of biological sciences · 2025Article
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9 authors.
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Abstract
Breast cancer is the second most prevalent malignant tumor worldwide and is highly heterogeneous. Cuproptosis, a newly identified form of cell death, is intimately connected to lipid metabolism. This study investigated breast cancer heterogeneity through the lens of cuproptosis-related lipid metabolism genes (CLMGs), with the goal of predicting patient prognosis, immunotherapy efficacy, and sensitivity to anticancer drugs. By utilizing transcriptomic data from The Cancer Genome Atlas (TCGA) for breast cancer, we identified 682 CLMGs and applied the nonnegative matrix factorization (NMF) method to categorize breast cancer patients into four distinct clusters: cluster 1, ''immune-cold and stroma-poor''; cluster 2, ''immune-infiltrated''; cluster 3, ''stroma-rich''; and cluster 4, ''moderate infiltration''. We subsequently developed a risk model based on CLMGs that incorporates ACSL1, ATP2B4, ATP7B, ENPP6, HSPH1, PIP4K2C, SRD5A3, and ULBP1. This model demonstrated excellent prognostic predictive performance in both the internal (testing and entire sets) and external (GSE20685 and Kaplan-Meier Plotter sets) validation sets. High-risk patients presented lower expression levels of immune checkpoint-related genes and lower immunophenoscores (IPSs), whereas low-risk patients presented higher CD8
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