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Abstract <p>Triple-negative breast cancer (TNBC) is a biologically aggressive subtype of breast cancer marked by high heterogeneity and poor prognosis. Copper metabolism has been implicated in TNBC progression, but its functional contributions remain insufficiently defined. In this study, we analyzed transcriptomic data from 229 TNBC and adjacent normal samples from The Cancer Genome Atlas (TCGA) to identify 26 differentially expressed copper metabolism-related genes (DEGs<sub>-CM</sub>). A nine-gene Cox model (<i>HEPHL1</i>, <i>COX7A1</i>, <i>COX4I2</i>, <i>JUN</i>, <i>MAPT</i>, <i>MT1A</i>, <i>AOC3</i>, <i>DCT</i>, <i>AOC2</i>) demonstrated robust prognostic value, with time-dependent AUCs of 0.88, 0.84, and 0.80 at 1, 3, and 5&#xa0;years. Functional enrichment analyses revealed epithelial-mesenchymal transition (EMT) and angiogenesis pathways enriched in high-risk groups. Four genes (<i>AOC3</i>, <i>COX4I2</i>, <i>COX7A1</i>, <i>JUN</i>) were further identified as copper metabolism-related metastasis genes (CMMRGs) through correlation with metastasis-associated programs. Based on these genes, machine learning classifiers were developed to predict TNBC presence and lymph node metastasis. Classifiers trained on the full dataset achieved consistently high performance, with most models showing AUCs greater than 0.97 (random forest, XGBoost, and AdaBoost classifier) even when using reduced gene panels (26-, 9-, and 4-gene sets), demonstrating stable classification across gene panels. In contrast, performance declined notably in metastasis-specific classification, largely due to the limited number of labeled metastatic samples. Among these, the 9-gene panel yielded the highest test AUCs across most models (gradient boosting machine AUC 0.64), suggesting that it may provide an optimal balance between model complexity and discriminative power, while also highlighting a key limitation related to the restricted sample size and incomplete clinical annotations in the metastasis-specific dataset. Single-cell RNA sequencing confirmed fibroblast-specific enrichment of CMMRGs and associated EMT signatures, suggesting a mechanistic link between copper metabolism, stromal remodeling, and metastasis. These results establish a copper-centered molecular framework for TNBC diagnosis and metastasis prediction, supporting the translational potential of copper metabolism-related genes in clinical applications.</p> Graphical Abstract <p></p>

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Unveiling the Impact of Copper Metabolism on Epithelial-Mesenchymal Transition of Triple-Negative Breast Cancer: Identification of Therapeutic Targets

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Kai Zhuang,
  • Lin Yan,
  • Muhammad Waqas,
  • Haixin Feng,
  • Siqi Tang,
  • Haoqi He,
  • Xinshi Yu,
  • Yanling Wang,
  • Zunnan Huang

摘要

Abstract

Triple-negative breast cancer (TNBC) is a biologically aggressive subtype of breast cancer marked by high heterogeneity and poor prognosis. Copper metabolism has been implicated in TNBC progression, but its functional contributions remain insufficiently defined. In this study, we analyzed transcriptomic data from 229 TNBC and adjacent normal samples from The Cancer Genome Atlas (TCGA) to identify 26 differentially expressed copper metabolism-related genes (DEGs-CM). A nine-gene Cox model (HEPHL1, COX7A1, COX4I2, JUN, MAPT, MT1A, AOC3, DCT, AOC2) demonstrated robust prognostic value, with time-dependent AUCs of 0.88, 0.84, and 0.80 at 1, 3, and 5 years. Functional enrichment analyses revealed epithelial-mesenchymal transition (EMT) and angiogenesis pathways enriched in high-risk groups. Four genes (AOC3, COX4I2, COX7A1, JUN) were further identified as copper metabolism-related metastasis genes (CMMRGs) through correlation with metastasis-associated programs. Based on these genes, machine learning classifiers were developed to predict TNBC presence and lymph node metastasis. Classifiers trained on the full dataset achieved consistently high performance, with most models showing AUCs greater than 0.97 (random forest, XGBoost, and AdaBoost classifier) even when using reduced gene panels (26-, 9-, and 4-gene sets), demonstrating stable classification across gene panels. In contrast, performance declined notably in metastasis-specific classification, largely due to the limited number of labeled metastatic samples. Among these, the 9-gene panel yielded the highest test AUCs across most models (gradient boosting machine AUC 0.64), suggesting that it may provide an optimal balance between model complexity and discriminative power, while also highlighting a key limitation related to the restricted sample size and incomplete clinical annotations in the metastasis-specific dataset. Single-cell RNA sequencing confirmed fibroblast-specific enrichment of CMMRGs and associated EMT signatures, suggesting a mechanistic link between copper metabolism, stromal remodeling, and metastasis. These results establish a copper-centered molecular framework for TNBC diagnosis and metastasis prediction, supporting the translational potential of copper metabolism-related genes in clinical applications.

Graphical Abstract