Validation of novel <scp>DNA</scp> methylation markers in cervical precancer and cancer
Bibliographic record
Abstract
We have recently identified, using a genome-wide approach, new methylation markers which were evaluated among various cervical intraepithelial neoplasia (CIN) grades and cervical cancer. We herein validate the methylated state of these genes in independent study populations, based on histology ascertained outcomes regardless of human papillomavirus status. CA10, DPP10, FMN2 and HAS1 (discovery set: 54 normal, 50 CIN1, 40 CIN2, 42 CIN3) were evaluated by targeted bisulfite next generation sequencing (NGS) (Illumina MiSeq platform) in 258 (training set: 100 normal, 50 CIN1, 50 CIN2, 50 CIN3, 8 cancers) and 373 (validation set: 100 normal, 57 CIN1, 61 CIN2, 53 CIN3, 102 cancers) physician-collected samples (PreservCyt). Using targeted amplification NGS data from the training set for 94 normal and eight cancer samples, we calculated for each gene the median methylation value. These were summed and normalized to compute a four-gene Marker Polygenic Score (MPS). We compared the relationship between MPS and progression from normal through CIN grades and cancer, separately in the training and validation sets, and tested its clinical performance via receiver-operating characteristic curves. MPS increased with increasing CIN grade, and accurately predicted cervical cancer in the training (area under the curve, AUC = 0.9950) and validation (AUC = 0.9337) sets, comparing normal to cancer. Using the highest threshold of 100% specificity, sensitivity for detection of cervical cancer was 67.7%; whereas reducing specificity to 95% increased sensitivity to 84.3%. Further evaluation of these biomarkers is warranted in prospective studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".