Circular RNA hsa_circ_0076248 promotes oncogenesis of glioma by sponging miR‐181a to modulate SIRT1 expression
Bibliographic record
Abstract
Glioma is one of the most common primary malignancies of the central nervous system, which has aggressive clinical behavior and a poorer prognosis. MicroRNAs (miRs) are a class of small noncoding RNAs that function as mediators of gene expression, which can be sponged by circRNA provided with a closed circular structure. Dysregulations of circular RNAs (circRNAs) and miRs have been implicated in the development and progression of glioma. In the current study, we investigated the role of circular RNA hsa_circ_0076248 in mediating the oncogenesis of glioma by sponging miR-181a to modulate silent information regulator 1 (SIRT1) expression in vitro and in vivo. The quantitative real-time polymerase chain reaction results showed that the expression of miR-181a was significantly decreased in glioma tissues and cell lines compared with normal brain tissues and normal gliocyte, respectively, and the expression of hsa_circ_0076248 and SIRT1 demonstrated the opposite. Bioinformatics analysis identified hsa_circ_0076248 could sponge miR-181a, and miR-181a could target the mRNA of SIRT1. Our results verified that downregulating hsa_circ_0076248 or upregulating miR-181a could depress the proliferation and invasion of glioma in vitro and in vivo. The experiment also showed that downregulating hsa_circ_0076248 or upregulating miR-181a could remarkably promote the temozolomide chemotherapy sensitivity. Furthermore, Western blot analysis testified that downregulating hsa_circ_0076248 or upregulating miR-181a could promote the expression of p53 and SIRT1. In summary, our study sheds light on the regulatory mechanism of hsa_circ_0076248 in glioma growth and invasion via sponging miR-181a, which downregulates the SIRT1 expression and also suggests that hsa_circ_0076248, miR-181a, and SIRT1 may serve as potential therapeutic targets 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) | 0.002 | 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".