Oncogenic function of growth arrest-specific transcript 5 by competing with miR-423-3p to regulate SMARCA4 in hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Long noncoding RNA growth arrest-specific transcript 5 (GAS5) has been identified as a tumor suppressor due to its downregulation in several cancers. However, our comprehensive analyses revealed aberrant overexpression of GAS5 in various cancers, with a direct association with SMARCA4 in hepatocellular carcinoma (HCC). Differential expression analyses were conducted using publicly available transcriptome datasets. Functional studies of GAS5 and its downstream targets in HCC were performed via small interfering RNA-mediated knockdown in various HCC cell lines, in vivo xenograft mouse models and spontaneous liver cancer models in Ras -transgenic mice. Here we discover that METTL3-mediated N 6 -methyladenosine modification promoted IGF2BP2 binding, stabilizing GAS5 in HCC. GAS5 expression was significantly upregulated in large cohort of patients with solid cancer, including HCC. Targeted disruption of GAS5 resulted in notable inhibition of growth and proliferation in HCC cells. Further analyses demonstrated that GAS5 enhanced in vitro tumorigenesis and metastatic potential of HCC cells. MicroRNA target prediction and functional validation indicated that GAS5 shared a miR-423-3p binding element with SMARCA4 messenger RNA, functioning as a competing endogenous RNA. This interaction was validated in vitro tumorigenesis assays and in vivo models. Moreover, a synergistic effect was observed with vehicle containing a small interfering RNA mixture targeting both GAS5 and SMARCA4 in these animal models. N 6 -methyladenosine-mediated IGF2BP2 binding stabilizes GAS5, which functions as a competing endogenous RNA for miR-423-3p, thereby enhancing the translation of SMARCA4 messenger RNA. GAS5 acts as a crucial regulator of the oncogenic SMARCA4 in hepatocellular carcinogenesis, presenting a potential therapeutic target for the treatment of liver malignancies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".