Progranulin (PC-cell-derived growth factor/acrogranin) regulates invasion and cell survival.
Bibliographic record
Abstract
Progranulin (pgrn; PC-cell-derived growth factor, epithelin precursor, or acrogranin) has been identified recently as an autocrine regulator of tumorigenesis in several cancer cells including SW-13 adrenal carcinomas and some breast cancers, but how pgrn promotes tumor progression is not well understood. SW-13 cells do not form tumors in nude mice but become highly tumorigenic when their pgrn expression is elevated, and this provides a useful model in which to investigate the role of pgrn in tumorigenesis. Here we show that, in SW-13 cells, the level of pgrn expression is a major determinant of the intrinsic activity of the mitogen-activated protein kinase, phosphatidylinositol 3'-kinase, and focal adhesion kinase signaling pathways. Pgrn stimulates the invasion of SW-13 cells across Matrigel-coated filters, increases the expression of matrix metalloproteinase 13 and 17, protects against anoikis, and overcomes the inhibition of cell growth imposed on SW-13 cells by interstitial type-I collagen. Inhibition of the mitogen-activated protein kinase and phosphatidylinositol 3'-kinase signaling pathways impairs each of the pgrn-dependent biological responses tested, but to different extents. The ability of pgrn to stimulate cell division, invasion, and survival demonstrates that pgrn regulates multiple steps in carcinomal progression, and suggests that the pgrn system may be a possible future therapeutic target.
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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) | 0.002 | 0.001 |
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".