Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression.

ABSTRACT: 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 ac

SUBMITTER: AlZaabi A 

PROVIDER: S-EPMC11240466 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature