Validation of DNA promoter hypermethylation biomarkers in breast cancer — a short report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: DNA promoter hypermethylation of tumor suppressor genes is known to occur early in cancer development, including breast cancer. To improve early breast cancer detection, we aimed to investigate whether the identification of DNA promoter hypermethylation might be of added value. METHODS: The methylation status of a panel of 19 candidate genes (AKR1B1, ALX1, ARHGEF7, FZD10, GHSR, GPX7, GREM1, GSTP1, HOXD1, KL, LHX2, MAL, MGMT, NDRG2, RASGRF2, SFRP1, SFRP2, TM6SF1 and TMEFF2) was determined in formalin-fixed paraffin-embedded normal breast and breast cancer tissue samples using gel-based methylation-specific PCR (MSP). RESULTS: The promoters of the AKR1B1, ALX1, GHSR, GREM1, RASGRF2, SFRP2, TM6SF1 and TMEFF2 genes were found to be significantly differentially methylated in normal versus malignant breast tissues. Based on sensitivity, specificity and logistic regression analyses the best performing genes for detecting breast cancer were identified. Through multivariate analyses, we found that AKR1B1 and TM6SF1 could detect breast cancer with an area under the curve (AUC) of 0.986 in a receiver operating characteristic (ROC) assessment. CONCLUSIONS: Based on our data, we conclude that AKR1B1 and TM6SF1 may serve as candidate methylation biomarkers for early breast cancer detection. Further studies are underway to evaluate the methylation status of these genes in body fluids, including nipple aspirates and blood.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it