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

Combining machine learning with absolute quantitative metabolomics, the authors have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This study deepens the study's understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive for early breast cancer detection and predicting NAC response in patients with TNBC.

The study's cell samples underwent cell-count and viability assessment with Countstar Rigel S2 during preparation for single-cell or single-nucleus analysis.