lncRNA CTBP1-AS2 promotes proliferation and migration of glioma by modulating miR-370-3p–Wnt7a-mediated epithelial–mesenchymal transition
Bibliographic record
Abstract
Glioma is one of the most common and aggressive malignant primary brain tumors, with a poor 5-year survival rate. The long noncoding RNA (lncRNA) CTBP1-AS2 has been shown to be correlated with the prognosis of cancer, but the role of CTBP1-AS2 in glioma and its concrete mechanism is fully unknown. The clinical data and tissues of glioma patients were analyzed. Cell viability and migration assays were performed. Western blotting and qRT-PCR were adopted for investigation of target protein expressions. Double luciferase assay was used to investigate the interaction between different elements. The lncRNA CTBP1-AS2 had increased expression profiles in tumor tissues, which is associated with poor prognosis. In detail, CTBP1-AS2 knockdown decreased proliferation and migration phenotypes in both U87-MG and LN229 cells. Moreover, CTBP1-AS2 knockdown suppressed the key epithelial-mesenchymal transition (EMT) markers by downregulating Wnt7a-mediated signaling. Furthermore, miR-370-3p functioned as a link that could be absorbed by CTBP1-AS2, thus regulating Wnt7a expression. Lastly, the CTBP1-AS2-miR-370-3p-Wnt7a axis modulated EMT in glioma cells in vitro and in vivo. This study provides new insights that a novel lncRNA, CTBP1-AS2, regulates EMT of glioma by modulating the miR-370-3p-Wnt7a axis.
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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".