Hypoxia and Transforming Growth Factor β Cooperate to Induce Fibulin-5 Expression in Pancreatic Cancer
Bibliographic record
Abstract
The deposition of extracellular matrix (ECM) is a defining feature of pancreatic ductal adenocarcinoma (PDA), where ECM signaling can promote cancer cell survival and epithelial plasticity programs. However, ECM signaling can also limit PDA tumor growth by producing cytotoxic levels of reactive oxygen species. For example, excess fibronectin stimulation of α5β1 integrin on stromal cells in PDA results in reduced angiogenesis and increased tumor cell apoptosis because of oxidative stress. Fibulin-5 (Fbln5) is a matricellular protein that blocks fibronectin-integrin interaction and thus directly limits ECM-driven reactive oxygen species production and supports PDA progression. Compared with normal pancreatic tissue, Fbln5 is expressed abundantly in the stroma of PDA; however, the mechanisms underlying the stimulation of Fbln5 expression in PDA are undefined. Using in vitro and in vivo approaches, we report that hypoxia triggers Fbln5 expression in a TGF-β- and PI3K-dependent manner. Pharmacologic inhibition of TGF-β receptor, PI3K, or protein kinase B (AKT) was found to block hypoxia-induced Fbln5 expression in mouse embryonic fibroblasts and 3T3 fibroblasts. Moreover, tumor-associated fibroblasts from mouse PDA were also responsive to TGF-β receptor and PI3K/AKT inhibition with regard to suppression of Fbln5. In genetically engineered mouse models of PDA, therapy-induced hypoxia elevated Fbln5 expression, whereas pharmacologic inhibition of TGF-β signaling reduced Fbln5 expression. These findings offer insight into the signaling axis that induces Fbln5 expression in PDA and a potential strategy to block its production.
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".