Genome‐wide DNA methylation profiling identifies two novel genes in cervical neoplasia
Bibliographic record
Abstract
DNA methylation analysis may improve risk stratification in cervical screening. We used a pan‐epigenomic approach to identify new methylation markers along the continuum of cervical intraepithelial neoplasia (CIN) to cervical cancer. Physician‐collected samples (54 normal, 50 CIN1, 40 CIN2 and 42 CIN3) were randomly selected from women at a single‐center colposcopy clinic. Extracted DNA was subjected to Illumina Infinium EPIC array analysis, and methylation was assessed blinded to histopathological and clinical data. CpG sites whose state of methylation correlated with lesion grade were assessed (Spearman correlation), and a weighted methylation score was calculated comparing normal to CIN3. Validation of the top selected genes was performed in an independent cohort (100 normal, 50 CIN1, 50 CIN2, 50 CIN3 and 8 cervical cancers) of new patients, referred for colposcopic examination at three hospitals, using targeted DNA methylation Illumina amplicon sequencing. The relationship between a combined weighted marker score and progression from normal through precancerous lesions and cervical cancer was compared using one‐way ANOVA. Our analyses revealed 7,715 CpGs whose methylation level correlated with progression (from normal to CIN1, CIN2 and CIN3), with a significant trend of increased methylation with lesion grade. We shortlisted a bigenic (hyaluronan synthase 1, HAS1 and ATPase phospholipid transporting 10A, ATP10A corresponding to cg03419058 and cg13944175 sites) marker set; r = 0.55, p < 0.0001. Validation of the four most discriminating genes (CA10, DPP10, FMN2 and HAS1) showed a significant correlation between methylation levels and disease progression (p‐value < 2.2 × 10−16, adjusted R2 = 0.952). Translational research of the identified genes to future clinical applications is warranted.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".