Epigenetically silenced miR-34b/c as a novel faecal-based screening marker for colorectal cancer
Bibliographic record
Abstract
BACKGROUND: MicroRNAs are tiny non-coding small endogenous RNAs that regulate gene expression by translational repression, mRNA cleavage and mRNA inhibition. The aim of this study was to investigate the hypermethylation of miR-34b/c and miR-148a in colorectal cancer, and correlate this data to clinicopathological features. We also aimed to evaluate the hypermethylation of miR-34b/c in faeces specimens as a novel non-invasive faecal-DNA-based screening marker. METHODS: The 5-aza-2'-deoxycytidine treatment and methylation-specific PCR were carried out to detect the hypermethylation of miR-34b/c and miR-148a. RESULTS: The miR-34b/c hypermethylation was found in 97.5% (79 out of 82) of primary colorectal tumours, P=0.0110. In 75% (21 out of 28) of faecal specimens we found a hypermethylation of miR-34b/c while only in 16% (2 out of 12) of high-grade dysplasia. In addition, miR-148a was found to be hypermethylated in 65% (51 out of 78) of colorectal tumour tissues with no significant correlation to clinicopathological features. However, a trend with female gender and advanced age was found, P=0.083. We also observed a trend to lower survival rate in patients with miR-148a hypermethylation with 10-year survival probability: 48 vs 65%, P=0.561. CONCLUSIONS: These findings show that aberrant hypermethylation of miR-34b/c could be an ideal class of early screening marker, whereas miR-148a could serve as a disease progression follow-up marker.
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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 | 0.000 |
| 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) | 0.001 | 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".