Multi-omics and molecular biomarkers in breast cancer: a comprehensive review of diagnostic, prognostic, and predictive applications.

Abstract

Breast cancer continues to be the most commonly diagnosed cancer among women globally and is a primary contributor to cancer-related deaths. While early detection significantly enhances survival rates, currently available serum biomarkers, including CEA and CA15-3, lack sufficient sensitivity and specificity for reliable early-stage breast cancer diagnosis. Multi-omics methodologies, encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics, are revolutionizing cancer diagnostics by facilitating the identification of new biomarkers and pathways pertinent to early detection and personalized treatment. Among the newly emerging classes of biomarkers, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and exosome-derived materials exhibit notable potential, as they reflect the molecular intricacies and dynamic changes of breast tumors in a minimally invasive fashion. In this review, we provide an overview of recent progress in multi-omics approaches and emphasize significant miRNA-, lncRNA-, and exosome-related biomarkers associated with the initiation, progression, and therapeutic response of breast cancer. Additionally, we explore how the amalgamation of these molecular signatures with artificial intelligence and machine learning can improve diagnostic precision, enhance risk stratification in asymptomatic or high-risk populations, and aid in personalized treatment decision-making. Lastly, we identify existing limitations, technical and translational hurdles, and future pathways necessary for the integration of multi-omics and liquid biopsy-based precision diagnostics into standard clinical practice, aiming ultimately to decrease breast cancer-related morbidity and mortality.