Down-regulation of ZIP4 by RNA Interference Inhibits Pancreatic Cancer Growth and Increases the Survival of Nude Mice with Pancreatic Cancer Xenografts
Bibliographic record
Abstract
PURPOSE: Zinc levels have been correlated with cancer risk, although the role of zinc and zinc transporters in cancer progression is largely unknown. We recently found that a zinc transporter, ZIP4, is overexpressed in pancreatic cancer. In this study, we further deciphered the role that ZIP4 plays in a pancreatic cancer mouse model by silencing ZIP4. EXPERIMENTAL DESIGN: ZIP4 stable silencing was established in pancreatic cancer cell lines ASPC-1 (ASPC-shZIP4) and BxPC-3 (BxPC-shZIP4) by short hairpin RNA using retrovirus vectors. The stable cells were characterized in vitro and in vivo using a nude mouse xenograft model. RESULTS: Silencing of ZIP4 was associated with decreased cell proliferation, migration, and invasion. Both ASPC-shZIP4 and BxPC-shZIP4 cells showed a significant reduction in tumor volume and weight in the s.c. model, and decreased primary tumor weight in the orthotopic model compared with the vector control cells (ASPC-shV and BxPC-shV). Silencing of ZIP4 also caused reduced incidence of tumor metastasis in the mice and downsized the tumor grade. More importantly, silencing of ZIP4 significantly increased the survival rate of nude mice with orthotopic xenografts (P = 0.005). All ASPC-shZIP4-injected mice (100%) remained alive up to 32 days after tumor implantation, whereas only 30% of the ASPC-shV mice were alive at the same time point. CyclinD1 expression was decreased in the ASPC-shZIP4 xenografts. CONCLUSIONS: These results identify a previously uncharacterized role of ZIP4 in pancreatic cancer progression, and indicate that knocking down ZIP4 by short hairpin RNA might be a novel treatment strategy for pancreatic cancers with ZIP4 overexpression.
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) | 0.001 | 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) | 0.002 | 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".