circ-NOTCH1 acts as a sponge of miR-637 and affects the expression of its target gene Apelin to regulate gastric cancer cell growth
Bibliographic record
Abstract
Gastric cancer (GC) is a major cause of cancer-related deaths worldwide, and has a low survival rate, low cure rate, high recurrence rate, and poor prognosis. Recent studies have indicated that circular RNAs (circRNAs) have important functions in the occurrence and progression of GC. Studies on circ-NOTCH1, which was shown to be highly expressed in GC, have indicated that miR-637 binds to circ-NOTCH1 at multiple sites, and a dual-luciferase reporter gene assay further confirmed that miR-637 indeed targeted circ-NOTCH1 and Apelin. Circ-NOTCH1 and Apelin are highly expressed in GC cells and tissues, whereas the expression of miR-637 is reduced. Circ-NOTCH1 and miR-637 do not regulate each other's expression levels, but circ-NOTCH1significantly upregulates the expression of the miR-637 target gene Apelin, whereas miR-637 inhibites the expression of Apelin. Examination of GC cells showed that circ-NOTCH1 enhances cell proliferation and invasiveness, and reduces cell apoptosis; these effects were reversed by miR-637, which could terminate the above effects of circ-NOTCH1. When co-transfected with the circ-NOTCH1 overexpression plasmid and Apelin siRNAs, there were no obvious changes to the levels of cell proliferation, apoptosis, or invasiveness. Therefore, in GC cells, circ-NOTCH1 inhibits the transcriptional activity of miR-637, thereby upregulating the expression of its target gene Apelin and regulating cell proliferation, apoptosis, and invasiveness. This finding provides more experimental evidence for the function of circRNA in GC.
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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 | 0.000 |
| 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".