Silencing of <i>Astrocyte elevated gene-1</i> (<i>AEG-1</i>) inhibits proliferation, migration, and invasiveness, and promotes apoptosis in pancreatic cancer cells
Bibliographic record
Abstract
To investigate the role of Astrocyte elevated gene-1 (AEG-1) in the development and progress of pancreatic cancer, short hairpin RNA (shRNA) was inserted into the RNA interference vector to knock-down the endogenous AEG-1 in two pancreatic cancer cell lines: AsPC-1 and PANC-1. Our results showed that silencing of AEG-1 suppressed the proliferation, colony formation ability, and cell stemness of AsPC-1 and PANC-1 cells, and inhibited their G1-to-S phase transition. Results from apoptosis assay showed that knock-down of AEG-1 led to cell apoptosis. The expression of anti-apoptotic Bcl-2 was downregulated and that of the pro-apoptotic Bax and cleaved caspase-3 was upregulated in AEG-1-silenced pancreatic cancer cells. Further, the capability of AEG-1-silenced cells to migrate and to invade through the Matrigel-coated membrane was weaker, and the expression of matrix metallopeptidase 2 (MMP-2) and MMP-9 were decreased. Moreover, the AKT-β-catenin signaling pathway was inhibited in the cells with knock-down of AEG-1. In addition, the growth of xenograft tumors formed by AsPC-1 and PANC-1 cells was suppressed by AEG-1 shRNA. In conclusion, our study demonstrates that pancreatic cancer cells require AEG-1 to maintain their survival and metastasis, suggesting AEG-1 as a potential target for the treatment of pancreatic cancers.
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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) | 0.001 | 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".