biostudies-literatureUnknown16(13)AlZaabi AHere, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased 'omics' analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and acCancers2365https://www.ebi.ac.uk/biostudies/studies/S-EPMC11240466biostudies-literatureDifferential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression.PMC11240466Piccolo SAlZaabi AHansen MGraves SfalseDifferential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression.Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased 'omics' analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and ac2024-01-01T00:00:00Z2024 Jun2026-05-28T03:07:56.436Z2026-05-28T03:06:26.82ZS-EPMC112404663900142610.3390/cancers16132365