Targeting GPR133 <i>via</i> miR-106a-5p inhibits the proliferation, invasion, migration and epithelial-mesenchymal transition (EMT) of glioma cells
Bibliographic record
Abstract
Background: Glioma is the most common malignant brain tumor. GPR133 is a key factor in the progression of glioma. However, the role of GPR133 in glioma invasion and EMT and the microRNAs (miRNAs) associated with this pathway are still poorly understood.Objective: This study aims to elucidate the biological function of miR-106a-5p and GPR133 in glioma as well as the molecular mechanism of their interaction.Methods: The mRNA expression of miR-106a-5p and GPR133 in glioma specimens and cells was analyzed by quantitative real-time polymerase chain reaction (qRT–PCR). The protein level of GPR133 and the levels of invasion- and EMT-related proteins were measured by western blotting. miR-106a-5p and GPR133 function in glioma cells was determined through cell counting kit-8 (CCK-8), transwell, wound healing, colony formation assays in vitro and xenograft assays in vivo. To determine the targeting relationship between miR-106a-5p and GPR133, a dual-luciferase reporter assay was conducted.Results: A marked reduction in miR-106a-5p expression was observed in glioma cells and specimens. Patients with high expression of miR-106a-5p had a good prognosis, while patients with high expression of GPR133 had a shorter OS. Additionally, overexpression of miR-106a-5p or downregulation of GPR133 inhibited the progression of glioma cells. Furthermore, miR-106a-5p negatively regulated GPR133 expression by binding to its 3′-UTR, and restrained the invasion, migration, proliferation and EMT of glioma cells by targeting GPR133.Conclusions: miR-106a-5p is a tumor suppressor that negatively regulates GPR133. The miR-106a-5p/GPR133 axis could potentially serve as a therapeutic target for glioma.
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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 | 0.000 |
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".