MicroRNA-149 is epigenetically silenced tumor-suppressive microRNA, involved in cell proliferation and downregulation of AKT1 and cyclin D1 in human glioblastoma multiforme
Bibliographic record
Abstract
Aberrant DNA methylation has been shown to inactivate tumor suppressor genes during carcinogenesis. MicroRNA-149 (miR-149) was recently demonstrated to function as a tumor suppressor gene in glioblastoma multiforme (GBM). However, the potential linkage of miR-149 levels and the underlying epigenetic regulatory mechanism in human GBM has not been studied. We used quantitative real-time polymerase chain reaction to investigate the levels of miR-149 in GBM tissues, their matched adjacent normal tissues, and glioblastoma U87MG cell line. Using bisulfite genomic sequencing technology, DNA methylation status of upstream region of miR-149 was evaluated in study population groups and the U87MG cell line. After treatment of cells with 5-aza-2'-deoxycitidine (5-aza-dC), the DNA methylation status, gene expression, and target protein levels of miR-149 were investigated. Our studies revealed that methylation and expression levels of miR-149 were significantly increased and decreased, respectively in GBM patients relative to the adjacent normal tissues (P < 0.01). MiR-149 suppressed the expression of AKT1 and cyclin D1 and reduced the proliferative activities of the U87MG cell line. Treatment of U87MG cells with 5-aza-dC reversed the hypermethylation status of miR-149, enhanced the expression of its gene, and decreased target mRNA and proteins levels (P < 0.01). These findings suggest that the methylation mechanism is associated with decreased expression levels of miR-149, which may in turn lead to the increased levels of its oncogenic target proteins.
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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".