The Role of the IGF-I Receptor in the Regulation of Matrix Metalloproteinases, Tumor Invasion and Metastasis
Bibliographic record
Abstract
The breakdown of the extracellular matrix (ECM) by proteinases is an essential step in the process of cancer invasion and metastasis. Malignant progression is frequently associated with upregulated production and/or activity of one or several ECM degrading proteinases. Prominent among them are the matrix metalloproteinases (MMPs). The MMPs constitute a family of structurally related, zinc-dependent endopeptidases collectively capable of degrading essentially all the components of the extracellular matrix. At present, 23 members of the human MMP gene family are known. The increased expression and/or activity of one or more members of this family have been documented in essentially all human malignancies and some have been implicated in the process of angiogenesis. Prominent among those are MMP-2 and MT1-MMP, two metalloproteinases that form a cell membrane-associated complex leading to MMP-2 activation and ECM proteolysis. Here, we review our data that identified the type 1 insulin-like growth factor receptor (IGF-IR) as a regulator of tumor invasion and the synthesis of MT1-MMP and MMP-2 and report on the signal transduction pathways that mediate this regulation. These findings are discussed in the context of a broader review of the role of the IGF-IR/IGF axis in the regulation of tumor invasion and metastasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".