Targeting GPR133 <i>via</i> miR-106a-5p inhibits the proliferation, invasion, migration and epithelial-mesenchymal transition (EMT) of glioma cells
Bibliographic record
Abstract
<b>Background:</b> 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.<b>Objective:</b> 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.<b>Methods:</b> 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 <i>in vitro</i> and xenograft assays <i>in vivo</i>. To determine the targeting relationship between miR-106a-5p and GPR133, a dual-luciferase reporter assay was conducted.<b>Results:</b> 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.<b>Conclusions:</b> 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 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 | 0.000 | 0.001 |
| 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 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".