Emerging Biomarkers in Breast Cancer: Translational and Multi-Omics Perspectives in Precision Oncology

Breast cancer remains a leading cause of cancer-related mortality among women worldwide, emphasizing the urgent need for improved diagnostic and therapeutic strategies. This review comprehensively explores the emerging landscape of breast cancer biomarkers, integrating insights from molecular mechanisms, clinical validation, and future translational applications. It highlights the evolution from classical receptor-based classification (ER, PR, HER2) to next-generation multi omics and AI-assisted biomarker discovery. Particular emphasis is placed on genetic, epigenetic, proteomic, and metabolomic markers, as well as liquid biopsy derived components such as ctDNA methylation, exosomal RNA, and extracellular vesicle biomarkers. The review critically analyses the reliability, reproducibility, and regulatory challenges of biomarker validation in clinical trials, including assay standardization and patient heterogeneity. Additionally, the discussion underscores the growing role of artificial intelligence in computational pathology and data harmonization across omics platforms. Limitations of current approaches and future research directions such as integrative modelling, personalized diagnostics, and real-world clinical translation are outlined to guide ongoing advancements in precision oncology. Overall, this article provides a mechanistic, evidence-based, and forward-looking overview of how emerging biomarkers are reshaping breast cancer diagnosis, prognosis, and therapeutic decision-making.乳腺癌仍是全球范围内导致女性癌症相关死亡的首要病因，凸显了优化诊断与治疗策略的迫切需求。本综述全面探讨了乳腺癌生物标志物领域的新兴态势，整合了分子机制、临床验证与未来转化应用等维度的研究洞见。综述重点阐述了从经典的基于受体的分类：雌激素受体（Estrogen Receptor, ER）、孕激素受体（Progesterone Receptor, PR）与人表皮生长因子受体2（Human Epidermal Growth Factor Receptor 2, HER2），到下一代多组学（multi omics）与人工智能（Artificial Intelligence, AI）辅助的生物标志物发现的演进历程。本文特别聚焦于遗传、表观遗传、蛋白质组学与代谢组学标志物，以及液体活检衍生的组分，如循环肿瘤DNA（circulating tumor DNA, ctDNA）甲基化、外泌体RNA与细胞外囊泡标志物。本综述批判性分析了临床试验中生物标志物验证的可靠性、可重复性与监管挑战，涵盖检测标准化与患者异质性等核心议题。此外，本文还着重强调了人工智能在计算病理学与跨组学平台的数据协调中日益凸显的重要作用。本文还概述了当前研究方法的局限性以及未来研究方向，包括整合建模、个性化诊断与真实世界临床转化，以期为精准肿瘤学领域的持续发展提供指引。总体而言，本文从机制层面、循证角度与前瞻性视角，全面梳理了新兴生物标志物如何重塑乳腺癌的诊断、预后评估与治疗决策流程。