Nuclear Factor-κB Contributes to Hedgehog Signaling Pathway Activation through Sonic Hedgehog Induction in Pancreatic Cancer
Bibliographic record
Abstract
The hedgehog (Hh) signaling pathway, which functions as an organizer in embryonic development, is implicated in the development of various tumors. In pancreatic cancer, pathway activation is reported to result from aberrant expression of the ligand, sonic Hh (Shh). However, the details of the mechanisms regulating Shh expression are not yet known. We hypothesized that nuclear factor-kappaB (NF-kappaB), a hallmark transcription factor in inflammatory responses, contributes to the overexpression of Shh in pancreatic cancer. In the present study, we found a close positive correlation between NF-kappaB p65 and Shh expression in surgically resected pancreas specimens, including specimens of chronic pancreatitis and pancreatic adenocarcinoma. We showed that blockade of NF-kappaB suppressed constitutive expression of Shh mRNA in pancreatic cancer cells. Further activation of NF-kappaB by inflammatory stimuli, including interleukin-1beta, tumor necrosis factor-alpha, and lipopolysaccharide, induced overexpression of Shh, resulting in activation of the Hh pathway. Overexpression of Shh induced by these stimuli was also suppressed by blockade of NF-kappaB. NF-kappaB-induced Shh expression actually activated the Hh pathway in a ligand-dependent manner and enhanced cell proliferation in pancreatic cancer cells. In addition, inhibition of the Hh pathway as well as NF-kappaB suppressed the enhanced cell proliferation. Our data suggest that NF-kappaB activation is one of the mechanisms underlying Shh overexpression in pancreatic cancer and that proliferation of pancreatic cancer cells is accelerated by NF-kappaB activation in part through Shh 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) | 0.000 | 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) | 0.001 | 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".