The Role of Tumor Cell–Derived Connective Tissue Growth Factor (CTGF/CCN2) in Pancreatic Tumor Growth
Bibliographic record
Abstract
Pancreatic cancer is highly aggressive and refractory to existing therapies. Connective tissue growth factor (CTGF/CCN2) is a fibrosis-related gene that is thought to play a role in pancreatic tumor progression. However, CCN2 can be expressed in a variety of cell types, and the contribution of CCN2 derived from either tumor cells or stromal cells as it affects the growth of pancreatic tumors is unknown. Using genetic inhibition of CCN2, we have discovered that CCN2 derived from tumor cells is a critical regulator of pancreatic tumor growth. Pancreatic tumor cells derived from CCN2 shRNA-expressing clones showed dramatically reduced growth in soft agar and when implanted s.c. We also observed a role for CCN2 in the growth of pancreatic tumors implanted orthotopically, with tumor volume measurements obtained by positron emission tomography imaging. Mechanistically, CCN2 protects cells from hypoxia-mediated apoptosis, providing an in vivo selection for tumor cells that express high levels of CCN2. We found that CCN2 expression and secretion was increased in hypoxic pancreatic tumor cells in vitro, and we observed colocalization of CCN2 and hypoxia in pancreatic tumor xenografts and clinical pancreatic adenocarcinomas. Furthermore, we found increased CCN2 staining in clinical pancreatic tumor tissue relative to stromal cells surrounding the tumor, supporting our assertion that tumor cell-derived CCN2 is important for pancreatic tumor growth. Taken together, these data improve our understanding of the mechanisms responsible for pancreatic tumor growth and progression, and also indicate that CCN2 produced by tumor cells represents a viable therapeutic target for the treatment of 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".