Stathmin, Interacting with Nf-κB, Promotes Tumor Growth and Predicts Poor Prognosis of Pancreatic Cancer
Bibliographic record
Abstract
Stathmin (STMN) has been known as a p53-regulated protein and has been shown to play an oncogenic role in a range of human malignancies. Paradoxically, most recent studies demonstrated that stathmin has a dual function as both an oncogene and a metastasis suppressor. Stathmin is a member of microtubule dynamic destabilizing proteins and stathmin-regulated microtubule disruption could lead to a variety of cell dysfunctions such as enhanced chronic hypoxia in pancreatic cancer. In this study, we identified that stathmin promotes proliferation of pancreatic cancer cells by an underlying nuclear factor kappa B (Nf-κB) interacting mechanism. In human specimens, stathmin was significantly overexpressed in pancreatic cancer tissues and high expression of stathmin was correlated with vascular emboli (p=0.028), tumor size (p=0.019), and overall survival (p=0.031). Functional assays showed that knockdown of stathmin significantly reduced pancreatic cancer cell viability, colony formation, and arrested the cell cycle at the G2/M phase. Furthermore, silence of stathmin could reduce pancreatic tumor growth in nude mice. For the mechanism, Western blot analyses demonstrated that Nf-κB (p65) was significantly down-regulated when stathmin was silenced. In addition, co-immunoprecipitation (CoIP) assay suggested that stathmin was able to interact with Nf-κB (p65). Our findings indicate that stathmin might play its oncogenic role by an interaction with Nf-κB pathway, which may reveal a novel mechanism to uncover the role of microtubule-destabilizing stathmin in pancreatic cancer environment as well as provide a potential therapeutic strategy for 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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".