Anterior Gradient 2 Is Expressed and Secreted during the Development of Pancreatic Cancer and Promotes Cancer Cell Survival
Bibliographic record
Abstract
Pancreatic cancer is a major oncological challenge due to its aggressive growth and metastasis. In the current study, we investigated the role of anterior gradient 2 (AGR2) in these processes. AGR2 mRNA, as assessed by quantitative real-time reverse transcription-PCR (Q-RT-PCR), was 14-fold higher in pancreatic cancer compared with normal and pancreatitis tissues. Immunohistochemistry revealed high expression of AGR2 in neoplastic cells with 98% (56 of 57) positivity on pancreatic cancer and minimal staining in normal and pancreatitis tissues. AGR2 was also expressed in early pancreatic intraepithelial neoplastic lesions. RT-PCR and Western blotting showed elevated AGR2 expression in seven of nine pancreatic cancer cell lines. AGR2, as detected in conditioned media from cancer cells, indicated that it was secreted. The influence of AGR2 on pancreatic cancer cells was evaluated by silencing with small interfering RNA and short hairpin RNA. Silencing of AGR2 significantly reduced cell proliferation (MTS assay) and invasion (Boyden chamber assay) and improved gemcitabine sensitivity (fluorescence-activated cell sorting analysis). Conditioned media from cells in which AGR2 was silenced had a reduced ability to stimulate proliferation of pancreatic cancer cells, suggesting that secreted AGR2 was active. In vivo, silencing of AGR2 in MPanc-96 cells led to a significant reduction of tumor growth and increased the effectiveness of gemcitabine treatments in orthotopic tumor models evaluated by noninvasive bioluminescence imaging. In summary, AGR2 is expressed and secreted during pancreatic cancer development and plays an important role in cancer cell growth and survival. These observations suggest that AGR2 may be a useful molecular target in pancreatic cancer.
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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".