Selective Loss of MEG3 Expression and Intergenic Differentially Methylated Region Hypermethylation in the MEG3/DLK1 Locus in Human Clinically Nonfunctioning Pituitary Adenomas
Bibliographic record
Abstract
CONTEXT: MEG3 is an imprinted gene encoding a novel noncoding RNA that suppresses tumor cell growth. Although highly expressed in the normal human pituitary, it is unknown which of the normal pituitary cell types and pituitary tumors express MEG3. OBJECTIVES: Our objectives were 1) to investigate cell-type- and tumor-type-specific expression of MEG3 in the human pituitary and 2) to investigate whether methylation in the intergenic differentially methylated region (IG-DMR) at the DLK1/MEG3 locus is involved in the loss of MEG3 expression in tumors. DESIGN AND METHODS: RT-PCR, quantitative RT-PCR, Northern blot, and a combination of in situ hybridization and immunofluorescence were used to determine the cell-type- and tumor-type-specific MEG3 expression. Bisulfite treatment and PCR sequencing of genomic DNA were used to measure the CpG methylation status in the normal and tumor tissues. Five normal human pituitaries and 17 clinically nonfunctioning, 11 GH-secreting, seven prolactin-secreting, and six ACTH-secreting pituitary adenomas were used. RESULTS: All normal human pituitary cell types express MEG3. However, loss of MEG3 expression occurs only in nonfunctioning pituitary adenomas of a gonadotroph origin. All other pituitary tumor phenotypes examined express MEG3. Hypermethylation of the IG-DMR at the DLK1/MEG3 locus is present in nonfunctioning pituitary adenomas. CONCLUSIONS: MEG3 is the first human gene identified expressed in multiple normal human pituitary cell types with loss of expression specifically restricted to clinically nonfunctioning pituitary adenomas. The IG-DMR hypermethylation may be an additional mechanism for MEG3 gene silencing in such 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".