The Analysis of Plasma Proteomics for Luminal A Breast Cancer

Background: Breast 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.

Methods: To 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.

Results: The 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).

Conclusions: The 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.