ArticleCancer medicine2024
The Analysis of Plasma Proteomics for Luminal A Breast Cancer.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Plasma proteome profiling identifies XPNPEP3 as a novel biomarker associated with metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus.Annals of medicine · 2026Article
- Plasma multi-omics profiling unravels molecular alterations and biomarker panels during recovery from severe coronavirus disease 2019: a pilot study.BMC infectious diseases · 2026Article
- Integrative discovery and targeted proteomics elucidate the plasma exosomal landscape in thyroid disorders, with emphasis on papillary thyroid carcinoma complicating Hashimoto's thyroiditis.Frontiers in endocrinology · 2026Article
- Proteomics in Diagnostic Evaluation and Treatment of Breast Cancer: A Scoping Review.Journal of personalized medicine · 2025Review
- CLEC3A Promotes Immune Evasion and Tumor Progression by Enhancing PD-L1 Stability to Weaken T Cell Cytotoxicity in Luminal Breast Cancer.Breast cancer (Dove Medical Press) · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundBreast cancer is the prevailing malignancy among women, exhibiting a discernible escalation in incidence within our nation; hormone receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common subtype. In this study, we aimed to search for a non-invasive, specific, blood-based biomarker for the early detection of luminal A breast cancer through proteomic studies.
methodsTo explore new potential plasma biomarkers, we applied data-independent acquisition (DIA), a technique combining liquid chromatography and tandem mass spectrometry, to quantify breast cancer-associated plasma protein abundance from a small number of plasma samples in 10 patients with luminal A breast cancer, 10 patients with benign breast tumors, and 10 healthy controls.
resultsThe proteomes of 30 participants in all cohorts were analyzed using the DIA method, and a total of 517 proteins and 3584 peptides were quantified. We found that there were significant differences in plasma protein expression profiles between breast cancer patients and non-breast cancer patients, and breast cancer was mainly related to lipid metabolism pathways. Finally, the optimal protein combinations for the diagnosis of breast cancer were PON3, IGLV3-10, and IGHV3-73 through multi-model analysis, which had a high prediction accuracy for breast cancer (AUC = 0.92), and the model could also distinguish breast cancer from HC (AUC = 0.92) and breast cancer from benign breast tumor (AUC = 0.91).
conclusionsThe study revealed proteomic signatures of patients with luminal A breast cancer, identified multiple differential proteins, and identified three plasma proteins as potential diagnostic biomarkers for breast cancer. It provides a reference for the screening of biomarkers for breast cancer.
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