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.

• Tác giả: Shuning Liu, Fei Ma, Yalong Qi, Jiani Wang, Yuanyi Wang, Yue Wang, Yuhan Wei, Chang Xu, Cheng Zeng
• Ngôn ngữ: eng
• Ký hiệu phân loại: 651.504 Special topics of records management
• Thông tin xuất bản: United States : Discover oncology , 2025
• Bộ sưu tập: NCBI
• ID: 707382

Tóm tắt

 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