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<p>Despite widespread use of ultrasound and mammography, the accuracy of early breast cancer detection remains suboptimal, particularly in Asian women with dense breast tissue, underscoring the unmet need for biologically informed, non-invasive diagnostic approaches. Notably, systematic characterization of circulating proteomic and metabolomic alterations in early-stage breast cancer remains limited, especially in large, well-validated cohorts. Here, leveraging a registered multicenter prospective study (NCT06016790) together with an independent external validation cohort, we enrolled 662 participants across nine healthcare institutions to evaluate a plasma-based multi-omics liquid-biopsy framework for non-invasive detection. Data-independent acquisition proteomics and untargeted metabolomics profiled 5,549 proteins and 630 metabolites, with targeted validation performed in independent cohorts. Integrated analyses revealed coordinated molecular remodeling characterized by enrichment of cytoskeleton- and adhesion-associated proteins (for example ACTN1, VCL, ITGA2B and MYH9) together with rewiring of lipid-metabolic pathways. Network and trajectory modeling further identified 13 malignancy-associated protein modules and two progression-linked metabolic trajectories. Based on these features, we developed ProMeta-BC, a combined plasma proteome-metabolome model incorporating 15 proteins and 5 metabolites, which achieved robust discrimination between benign and malignant lesions (AUC 0.973 and 0.951 in training and validation cohorts, respectively), and ProMeta-BC-LN for prediction of axillary lymph-node metastasis (AUC 0.841 and 0.753). Notably, in cases with discordant radiological and pathological findings, the model detected 8 of 9 imaging-negative cancers and correctly reclassified 38 of 56 patients with inconsistent lymph-node calls, indicating complementary clinical utility. Together, these findings show that coordinated molecular signatures encoded in the circulating proteome–metabolome capture disease-relevant biology and provide a scalable, interpretable framework for non-invasive detection and stratification in breast cancer.</p>

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Plasma proteome–metabolome signatures enable non-invasive early detection and lymph node risk stratification in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Wei Zhang,
  • Yao Yao,
  • Yimeng Wang,
  • Lesang Shen,
  • Jinghua Ding,
  • Yuxuan Zhu,
  • Heli Xu,
  • Yinkuan Shao,
  • Xidong Gu,
  • Haiqi Lu,
  • Jun Zhou,
  • Haiming Deng,
  • Jun Zhou,
  • Wuzhen Chen,
  • Wenjie Xia,
  • Jinxing Jiang,
  • Xiuyan Yu,
  • Shanshan Sun,
  • Jiaxin Chen,
  • Jian Liu,
  • Dezheng Wang,
  • WenJia Liu,
  • Ziao Lin,
  • Kailun Xu,
  • Qijun Wu,
  • Jian Huang,
  • Hengyu Li,
  • Zhuohang Yu,
  • Chao Ni

摘要

Despite widespread use of ultrasound and mammography, the accuracy of early breast cancer detection remains suboptimal, particularly in Asian women with dense breast tissue, underscoring the unmet need for biologically informed, non-invasive diagnostic approaches. Notably, systematic characterization of circulating proteomic and metabolomic alterations in early-stage breast cancer remains limited, especially in large, well-validated cohorts. Here, leveraging a registered multicenter prospective study (NCT06016790) together with an independent external validation cohort, we enrolled 662 participants across nine healthcare institutions to evaluate a plasma-based multi-omics liquid-biopsy framework for non-invasive detection. Data-independent acquisition proteomics and untargeted metabolomics profiled 5,549 proteins and 630 metabolites, with targeted validation performed in independent cohorts. Integrated analyses revealed coordinated molecular remodeling characterized by enrichment of cytoskeleton- and adhesion-associated proteins (for example ACTN1, VCL, ITGA2B and MYH9) together with rewiring of lipid-metabolic pathways. Network and trajectory modeling further identified 13 malignancy-associated protein modules and two progression-linked metabolic trajectories. Based on these features, we developed ProMeta-BC, a combined plasma proteome-metabolome model incorporating 15 proteins and 5 metabolites, which achieved robust discrimination between benign and malignant lesions (AUC 0.973 and 0.951 in training and validation cohorts, respectively), and ProMeta-BC-LN for prediction of axillary lymph-node metastasis (AUC 0.841 and 0.753). Notably, in cases with discordant radiological and pathological findings, the model detected 8 of 9 imaging-negative cancers and correctly reclassified 38 of 56 patients with inconsistent lymph-node calls, indicating complementary clinical utility. Together, these findings show that coordinated molecular signatures encoded in the circulating proteome–metabolome capture disease-relevant biology and provide a scalable, interpretable framework for non-invasive detection and stratification in breast cancer.