Breast cancer is the most common cancer among women worldwide, and there is growing interest in identifying novel biomarkers to improve early diagnosis, prognosis, and treatment strategies. Plasma proteomics is highly accessible and enables the detection of molecular changes at early disease stages, making it a valuable approach for biomarker discovery. In this study, we validated a data-independent acquisition (DIA) mass spectrometry assay following S-trap digestion using plasma samples from breast cancer patients (n=23) and healthy controls (n=6). A total of 179 proteins were upregulated (≥2-fold) and 97 were downregulated (≤0.5-fold) in the cancer group compared to controls. Additionally, breast cancer cell lines (MCF7, BT474, HCC1954, BT20, and MDA-MB231) were analyzed using data-dependent acquisition (DDA) and compared with plasma proteomics data. Overlapping differentially expressed proteins (sDEP) were identified across cell lines, with the number of shared proteins as follows: MCF7 (3: upregulated 2, downregulated 1), BT474 (4: upregulated 1, downregulated 3), HCC1954 (13: upregulated 7, downregulated 6), BT20 (7: upregulated 5, downregulated 2), and MDA-MB231 (4: upregulated 3, downregulated 1). These findings support the identification of clinically relevant breast cancer biomarkers through the integration of cell line analysis and DIA-based plasma proteomics.