<p>Emerging Roles Of hsa-circ-0046600 Targeting The miR-640/HIF-1α Signalling Pathway In The Progression Of HCC</p>
Bibliographic record
Abstract
PURPOSE: Circular RNAs (circRNAs) play important roles in the development and progression of various human cancers. hsa-circ-0046600 is a circRNA of unknown function. The purpose of this study was to investigate the biological function of hsa-circ-0046600 in hepatocellular carcinoma (HCC) and elucidate the possible molecular mechanisms of this circRNA. MATERIALS AND METHODS: GSE97332, quantitative reverse transcription polymerase chain reaction (qRT-PCR) and fluorescence in situ hybridization (FISH) were used to detect the expression of hsa-circ-0046600 in HCC tissues and cells. A dual-luciferase reporter assay was used to confirm the interaction between hsa-circ-0046600 and miR-640, and a meta-analysis confirmed the expression of miR-640 in HCC. Bioinformatics was used for the functional analysis of miR-640 target genes. N-cadherin and HIF-1α expression was measured by Western blot analysis. RESULTS: The expression level of hsa-circ-0046600 in HCC tissue was significantly higher than that in adjacent normal tissue (P < 0.05) and was associated with tumour size, TNM stage and pathological vascular invasion. Moreover, the downregulated expression of hsa-circ-0046600 significantly inhibited the migration of HepG2 and SK-HEP-1 cells. hsa-circ-0046600 is present mainly in the cytoplasm and promotes the expression of proteins such as HIF-1α by competitively binding to miR-640 in HCC, thereby affecting the malignant biological behaviour of liver cancer cells. CONCLUSION: hsa-circ-0046600 can be used as a new biomarker for HCC diagnosis and disease progression and provides a potential target for targeted therapy.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".