LncRNA AK001796 promotes cell proliferation via acting as a ceRNA of miR-150 in hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Long non-coding RNA AK001796 was initially identified altered in lung cancer. Recent research showed it could participate in the prognosis of hepatocellular carcinoma (HCC). However, the general biological role of AK001796 and its underlying mechanisms in HCC remain unclear. Here we demonstrated that the expression level of AK001796 in HCC tissues and cell lines was up-regulated. Silencing AK001796 suppressed the proliferation ability of HCC cells. Through dual luciferase reporter assays and loss/gain of functions studies, we identified that AK001796 could bind to miR-150, a star microRNA, promoting HCC proliferation. Furthermore, it was reported that growth factor receptor binding protein 2-associated binder 1 (GAB1) is a target gene of miR-150. Owing to AK001796 being a decoy for miR-150 and binding the same putative sites of miR-150 as GAB1, we presented that inhibition of miR-150 in AK001796 silencing cells reversed the reduction in GAB1. Subsequently, our findings demonstrated that silencing AK001796 can impair phospho-ERK1/2 and phospho-AKT. In conclusion, our investigation revealed that AK001796 promoted proliferation by enhancing phospho-ERK1/2 and phospho-AKT through AK001796/miR-150/GAB1 axis in HCC. These results provided further evidence for the critical roles of AK001796 accumulating HCC and suggested that AK001796 might act as an HCC biomarker in clinical treatment.
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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) | 0.002 | 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".