The role of TGFBI (βig-H3) in gastrointestinal tract tumorigenesis
Bibliographic record
Abstract
BACKGROUND: TGFβ-induced (TGFBI/βig-H3) is a protein inducible by TGFβ1 and secreted by many types of cells. It binds to collagen, forms part of the extracellular matrix (ECM), and interacts with integrins on cell surfaces. In this study, we investigated the role of TGFBI in tumorigenesis and the underlying mechanisms. METHODS: Patient serum TGFBI levels were determined by ELISA. TGFBI transgenic and gene knockout mice and TGFBI-overexpressing liver cells were used for mechanistic studies. RESULTS: We demonstrated that patients with cholangiocarcinomas, hepatic carcinomas or gastric carcinomas presented significantly elevated serum TGFBI levels, and the excess TGFBI was derived from the tumor masses. TGFBI overexpression in mice resulted in increased incidence of spontaneous tumors and N,N-diethylnitrosamine (DEN)-induced liver tumor nodules, compared to that in wild type (WT) mice, while TGFBI knockout mice were comparable to WT controls in these 2 aspects. TGFBI promoted the survival of Aml-12 liver cells with DNA damage after irradiation, and augmented their post-irradiation proliferation. It activated the FAK/AKT/AKT1S1/PRS6/EIF4EBP pathway, which is known to modulate cell survival and proliferation. CONCLUSIONS: Our data suggest that TGFBI functions as a promoter of certain gastrointestinal tract cancers. It provides a survival advantage to cells with DNA damage. Over a long time span, this advantage could translate into increased tumor risks.
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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) | 0.001 | 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".