Validation of DNA promoter hypermethylation biomarkers in breast cancer — a short report
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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".