Circular RNA circESYT2 serves as a microRNA‐665 sponge to promote the progression of hepatocellular carcinoma through ENO2
Bibliographic record
Abstract
Circular RNAs (circRNAs) have emerged as crucial regulators in tumor progression, yet their specific role in hepatocellular carcinoma (HCC) remains largely uncharacterized. In this study, we utilized high-transcriptome sequencing to identify the upregulation of circESYT2 (hsa_circ_002142) in HCC tissues. Functional experiments carried out in vivo and in vitro revealed that circESYT2 played a significant role in maintaining the growth and metastatic behaviors of HCC. Through integrative analysis, we identified enolase 2 (ENO2) as a potential target regulated by circESYT2 through the competitive endogenous RNA sponge mechanism. Additional gain- or loss-of-function experiments indicated that overexpression of circESYT2 led to a tumor-promoting effect, which could be reversed by transfection of microRNA-665 (miR-665) mimic or ENO2 knockdown in HCC cells. Furthermore, the direct interaction between miR-665 and circESYT2 and between miR-665 and ENO2 was confirmed using RNA immunoprecipitation, FISH, RNA pull-down, and dual-luciferase reporter assays, highlighting the involvement of the circESYT2/miR-665/ENO2 axis in promoting HCC progression. These findings shed light on the molecular characteristics of circESYT2 in HCC tissues and suggest its potential as a biomarker or therapeutic target for HCC treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".