{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16(13)"],"submitter":["AlZaabi A"],"pubmed_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"],"journal":["Cancers"],"pagination":["2365"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11240466"],"repository":["biostudies-literature"],"pubmed_title":["Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression."],"pmcid":["PMC11240466"],"pubmed_authors":["Piccolo S","AlZaabi A","Hansen M","Graves S"],"additional_accession":[]},"is_claimable":false,"name":"Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression.","description":"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","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jun","modification":"2026-05-28T03:07:56.436Z","creation":"2026-05-28T03:06:26.82Z"},"accession":"S-EPMC11240466","cross_references":{"pubmed":["39001426"],"doi":["10.3390/cancers16132365"]}}