<i>MIR517C</i>inhibits autophagy and the epithelial-to-mesenchymal (-like) transition phenotype in human glioblastoma through KPNA2-dependent disruption of TP53 nuclear translocation
Bibliographic record
Abstract
The epithelial-to-mesenchymal (-like) transition (EMT), a crucial embryonic development program, has been linked to the regulation of glioblastoma (GBM) progression and invasion. Here, we investigated the role of MIR517C/miR-517c, which belongs to the C19MC microRNA cluster identified in our preliminary studies, in the pathogenesis of GBM. We found that MIR517C was associated with improved prognosis in patients with GBM. Furthermore, following treatment with the autophagy inducer temozolomide (TMZ) and low glucose (LG), MIR517C degraded KPNA2 (karyopherin alpha 2 [RAG cohort 1, importin alpha 1]) and subsequently disturbed the nuclear translocation of TP53 in the GBM cell line U87 in vitro. Interestingly, this microRNA could inhibit autophagy and reduce cell migration and infiltration in U87 cells harboring wild-type (WT) TP53, but not in U251 cells harboring mutant (MU) TP53. Moreover, the expression of epithelial markers (i.e., CDH13/T-cadherin and CLDN1 [claudin 1]) increased, while the expression of mesenchymal markers (i.e., CDH2/N-cadherin, SNAI1/Snail, and VIM [vimentin]) decreased, indicating that the EMT status was blocked by MIR517C in U87 cells. Compared with MIR517C overexpression, MIR517C knockdown promoted infiltration of U87 cells to the surrounding structures in nude mice in vivo. The above phenotypic changes were also observed in TP53(+/+) and TP53(-/-) HCT116 colon cancer cells. In summary, our study provided support for a link between autophagy and EMT status in WT TP53 GBM cells and provided evidence for the signaling pathway (MIR517C-KPNA2-cytoplasmic TP53) involved in attenuating autophagy and eliminating the increased migration and invasion during the EMT.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".