The predictive value of metabolomic-related biomarkers in breast cancers: Current approaches in biotechnology
Bibliographic record
Abstract
Breast cancer is the second most common cancer worldwide and is a leading cause of cancer-related mortality in women. The rising burden of breast cancer highlights the need for more accurate, non-invasive, and informative diagnostic tools. Despite the current advancements in medicine, predicting treatment response and patient prognosis remains challenging. It has thus become imperative to address the need for precise and reliable prognostic and diagnostic tools. Metabolic profiles, such as lipid processing and steroid hormone metabolism, have recently emerged as significant biomarkers in tumor biology, especially for early detection, prognosis, and therapy monitoring. This literature review explores the predictive value of serum lipid profiles and selected steroids as biomarkers in breast tumors. It shows their potential in improving diagnostic strategies and treatment planning in breast cancer management. These approaches offer valuable insights into tumor biology, metabolic changes, and hormone-driven pathways. Despite current challenges in sample preparation, data interpretation, and technical demands, recent advances such as high-resolution mass spectrometry, as well as spatial metabolomics and artificial intelligence, are helping to overcome these barriers. With continued research and technological progress, metabolomic-related biomarkers are expected to see broader use in clinical settings, supporting personalized treatment and improving outcomes for breast cancer patients.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".