Temporal stability and age-related prevalence of loss of imprinting of the insulin-like growth factor-2 gene
Bibliographic record
Abstract
BACKGROUND: Loss of genomic imprinting (LOI) of the insulin-like growth factor-2 gene (IGF2) is an epigenetic change involving abnormal activation of the normally silent maternally inherited allele. LOI of IGF2 gene is found in tumor tissue, normal adjoining mucosa and peripheral blood lymphocytes (PBL) of some patients with colorectal cancer (CRC), suggesting that this alteration precedes and is a risk factor for CRC. However, whether LOI of IGF2 is transitory or remains a permanent epigenetic alteration is unknown. RESULTS: Four-hundred patients, mean age 60.7 years (range 15-95), 287 (80%) Caucasian were studied. This included 210 (51.4%) patients with no colorectal neoplasia, and 190 (48.6) with colorectal neoplasia. LOI of IGF2 was present in all age strata examined, and no statistically significant association across age strata (p trend > 0.05) was noted. Forty-nine patients had repeat analysis of blood imprinting status at a mean follow up time of 38.2 +/- 12.9 months. All but three patients had the same imprinting status at follow up (94% agreement, kappa 0.79, p < 0.001). Genomic imprinting was stable for patients with and without colorectal neoplasia. METHODS: Standard RT-PCR assays for imprinting analysis of IGF2 were performed on PBL from ApaI informative individuals recruited at baseline and repeated 1 to 3 years later. Prevalence of LOI of IGF2 was also evaluated according to age strata. CONCLUSION: LOI of the IGF2 gene in PBL appears to be a stable epigenetic phenomenon in most patients. Furthermore, LOI of IGF2 was not associated with age, suggesting an inherited or congenital epigenetic event. These findings support the concept that LOI of IGF2 may be a useful risk factor for CRC predisposition.
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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) | 0.002 | 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".