Emerging biomarkers in breast cancer: translational and multi-omics perspectives in precision oncology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".