α2HS-glycoprotein, an Antagonist of Transforming Growth Factor β <b> <i>In vivo</i> </b>, Inhibits Intestinal Tumor Progression
Bibliographic record
Abstract
Transforming growth factor (TGF)-beta1 is associated with tumor progression and resistance to chemotherapy in established cancers, as well as host immune suppression. Here, we show that the serum glycoprotein alpha2-HS-glycoprotein (AHSG) blocks TGF-beta1 binding to cell surface receptors, suppresses TGF-beta signal transduction, and inhibits TGF-beta-induced epithelial-mesenchymal transition, suggesting that AHSG may play a role in tumor progression. In 66 consecutive sporadic human colorectal cancer specimens, we observed a 3-fold depletion of ASHG in tumor compared with normal tissue, whereas levels of other abundant plasma proteins, albumin and transferrin, were equivalent. Using the Multiple intestinal neoplasia/+ (Min/+) mouse model of intestinal tumorigenesis, we found twice as many intestinal polyps overall, twice as many large polyps (>3 mm diameter), and more progression to invasive adenocarcinoma in Min/+ Ahsg-/- mice than in littermates expressing Ahsg. Phosphorylated Smad2 was more abundant in the intestinal mucosa and tumors of Min/+ mice lacking Ahsg, demonstrating increased TGF-beta signaling in vivo. Furthermore, TGF-beta-mediated suppression of immune cell function was exaggerated in Ahsg-/- animals, as shown by inhibition of macrophage activation and reduction in 12-O-tetradecanoylphorbol 13-acetate-induced cutaneous inflammation. Reconstitution of Ahsg-/- mice with bovine Ahsg suppressed endogenous TGF-beta-dependent signaling to wild-type levels, suggesting that therapeutic enhancement of AHSG levels may benefit patients whose tumors are driven by TGF-beta.
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 | 0.001 |
| 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".