Identification of copper related biomarkers in breast cancer using machine learning
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most prevalent and deadly cancer among women globally, necessitating more effective diagnostic and therapeutic approaches. This study aims to explore new treatment targets and diagnostic tools. METHODS: Employing machine learning techniques and utilizing PCR, IHC technologies, and multiple databases, we identified and validated genes closely linked with breast cancer and copper-induced cell death. We then explored how their expression levels impact cancer diagnosis, prognosis, immune cell infiltration, and drug sensitivity. RESULTS: This investigation identified three crucial genes-MT1M, GRHL2, and PKM-intimately associated with the copper death mechanism in breast cancer pathology. Validated through comprehensive analysis across cells, tissue models, and diverse databases, these genes showed significant differential expression (P-value < 0.05), affirming their pivotal role in enhancing diagnostic accuracy (AUC values: 0.917, 0.970, 0.951) and prognostic assessment (HR = 0.65, P = 0.018; HR = 1.69, P = 0.0011; HR = 1.51, P = 0.012) in breast cancer. Additionally, their expression levels influence the infiltration of immune cells and the sensitivity to certain drugs. CONCLUSION: MT1M, GRHL2, and PKM are novel diagnostic and therapeutic targets for breast cancer. These findings enhance prognostic evaluations, deepen our understanding of its mechanisms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".