Inhibiting the m<sup>6</sup>A Reader IGF2BP3 Suppresses Ovarian Cancer Cell Growth via Regulating PLAGL2 mRNA Stabilization
Bibliographic record
Abstract
Background: The oncogene IGF2 mRNA binding protein 3 (IGF2BP3) could function as an m 6 A reader in stabilizing many tumor-associated genes’ mRNAs. However, the relevant oncogenic mechanism by which IGF2BP3 promotes ovarian cancer growth is largely unknown. Methods: The IGF2BP3 expression in ovarian cancer was identified by retrieving the datasets from The Cancer Genome Atlas (TCGA). GEO datasets evaluated the relevant signaling pathways in IGF2BP3 knockdown in ovarian cancer cells. IGF2BP3 positive correlation gene in TCGA was calculated. MTS proliferation assay was identified in IGF2BP3 knockdown and rescued by PLAG1 like zinc finger 2 (PLAGL2) overexpression in ES-2 and SKOV3 cells. Bioinformatic analysis and RIP-qPCR were predicted and identified the IGF2BP3 binding site and PLAGL2 mRNA stability. The animal experiment identified IGF2BP3 proliferation inhibition. Results: IGF2BP3 was upregulated in ovarian cancer tissue and cells. The depletion of IGF2BP3 in ovarian cancer cells leads to an enhancement of the pathway involved in cellular proliferation and mRNA stability. IGF2BP3 positive correlation suppressed pro-proliferation gene PLAGL2. IGF2BP3 knockdown suppressed ovarian cancer cell proliferation and was rescued by PLAGL2 overexpression. Luciferase reporter assay confirmed that IGF2BP3 could bind to 3'-UTR of PLAGL2 to maintain the mRNA stability. Further, in in vivo experiments, IGF2BP3 knockdown suppressed ovarian cancer cell proliferation via inhibiting PLAGL2 expression. Conclusion: All of these indicate that PLAGL2 mediates the main function of IGF2BP3 knockdown on ovarian cancer proliferation inhibition through mRNA stability regulation. World J Oncol. 2024;15(1):100-113 doi: https://doi.org/10.14740/wjon1747
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".