Methylation-associated silencing of <i>miR-193a-3p</i> promotes ovarian cancer aggressiveness by targeting GRB7 and MAPK/ERK pathways
Bibliographic record
Abstract
Human growth factor receptor-bound protein-7 (GRB7) is a pivotal mediator involved in receptor tyrosine kinase signaling and governing diverse cellular processes. Aberrant upregulation of GRB7 is frequently associated with the progression of human cancers. However, the molecular mechanisms leading to the upregulation of GRB7 remain largely unknown. Here, we propose that the epigenetic modification of GRB7 at the post-transcriptional level may be a crucial factor leading to GRB7 upregulation in ovarian cancers. Methods: The upstream miRNA regulators were predicted by in silico analysis. Expression of GRB7 was examined by qPCR, immunoblotting and immunohistochemical analyses, while miR-193a-3p levels were evaluated by qPCR and in situ hybridization in ovarian cancer cell lines and clinical tissue arrays. MS-PCR and pyrosequencing analyses were used to assess the methylation status of miR-193a-3p. Stable overexpression or gene knockdown and Tet-on inducible approaches, in combination with in vitro and in vivo tumorigenic assays, were employed to investigate the functions of GRB7 and miR-193a-3p in ovarian cancer cells. Results: , directly targeted the 3' UTR of GRB7. However, only miR-193a-3p showed a significantly inverse correlation with GRB7-upregulated ovarian cancers. Epigenetic studies revealed that methylation-mediated silencing of miR-193a-3p led to a stepwise decrease in miR-193a-3p expression from low to high-grade ovarian cancers. Intriguingly, miR-193a-3p not only modulated GRB7 but also ERBB4, SOS2 and KRAS in the MAPK/ERK signaling pathway to enhance the oncogenic properties of ovarian cancer cells in vitro and in vivo. Conclusion: These findings suggest that epigenetic silencing of miR-193a-3p by DNA hypermethylation is a dynamic process in ovarian cancer progression, and miR-193a-3p may be explored as a promising miRNA replacement therapy in this disease.
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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".