Association between methylation quantitative trait loci and colorectal cancer risk, survival and cancer recurrence
Bibliographic record
Abstract
BACKGROUND: Epigenetic changes contribute to colorectal cancer (CRC) pathogenesis. We investigated whether methylation quantitative trait loci (mQTLs) are associated with CRC risk, survival and recurrence. METHODS: Using a well-characterised Scottish case-control study (6821 CRC cases, 14,692 controls), we derived 118,982 mQTLs based on the Genetics of DNA Methylation Consortium (GoDMC). Association analysis between mQTLs and CRC risk, survival and recurrence was performed using logistic regression or Cox models respectively. Additionally, colocalisation analysis was performed. RESULTS: ). Four regions mapped to POU5F1B, POU2AF2 (c11orf53)/POU2AF3 (COLCA2), GREM1 and CABLES2 were previously identified. Four regions mapped to PPA2, PANDAR/LAP3P2, POU6F1 and CTIF contained SNPs previously identified by CRC GWAS but with SNPs annotated to different genes. We found no evidence that any of the 19 mQTLs associated with CRC risk influenced survival or recurrence after FDR correction. Colocalisation analysis suggested that in three of the ten regions the causal variants were shared for methylation and CRC risk. CONCLUSION: This study adds to the repertoire of CRC genes. However, we found no associations between methylation and CRC survival or recurrence.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".