Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction.

ABSTRACT: Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the traini

SUBMITTER: Song H 

PROVIDER: S-EPMC11381768 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature