Hypermethylation of the Promoter Region Is Associated with the Loss of <i>MEG3</i> Gene Expression in Human Pituitary Tumors
Bibliographic record
Abstract
MEG3 is a human homolog of the mouse maternal imprinted gene, Gtl2. Gtl2 has been suggested to be involved in fetal and postnatal development and to function as an RNA. Recently our laboratory demonstrated that a cDNA isoform of MEG3, MEG3a, inhibits cell growth in vitro. Interestingly, MEG3 is highly expressed in the normal human pituitary. In striking contrast, no MEG3 expression was detected in human clinically nonfunctioning pituitary tumors. These data indicate that this imprinted gene may be involved in pituitary tumorigenesis. In the present study we investigated the mechanism underlying the absence of MEG3 expression in human clinically nonfunctioning pituitary tumors. No genomic abnormality was detected in the tumors examined. Instead, we found that two 5'-flanking regions, immediately in front of and approximately 1.6-2.1 kb upstream of the first exon, respectively, were hypermethylated in tumors without MEG3 expression compared with the normal pituitary. Reporter assays demonstrated that these two regions are functionally important in gene expression activation. Furthermore, treatment of human cancer cell lines with a methylation inhibitor resulted in MEG3 expression. We conclude that hypermethylation of the MEG3 regulatory region is an important mechanism associated with the loss of MEG3 expression in clinically nonfunctioning pituitary tumors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".