The IGF-I Receptor Can Alter the Matrix Metalloproteinase Repertoire of Tumor Cells through Transcriptional Regulation of PKC-α
Bibliographic record
Abstract
The IGF-I receptor (IGF-IR) was identified as a tumor progression factor, but its role in invasion and metastasis has been the subject of some controversy. Previously we reported that in murine lung carcinoma M-27 cells, overexpression of IGF-IR increased the synthesis and activation of matrix metalloproteinase (MMP)-2 via Akt/phosphatidylinositol 3-kinase signaling. In contrast, we show here that in these and other cells, IGF-IR overexpression reduced the constitutive and phorbol 12-myristate 13-acetate (PMA)-inducible expression of three protein kinase C (PKC)-regulated metalloproteinases, MMP-3, MMP-9, and MMP-13, in cultured cells as well as in vivo in sc tumors. To elucidate the underlying mechanism, we analyzed the effect of IGF-IR on PKC expression and activity using wild-type and IGF-IR-overexpressing (M-27(IGFIR)) tumor cells. Our results show that overexpression and activation of IGF-IR reduced PKC-alpha expression, PKC activity, and downstream ERK1/2 signaling, and these effects were reversed in cells expressing kinase (Y(1131,1135,1136)F) or C-terminal (Y(1250/51)F) domain mutants of IGF-IR. This reduction was due to transcriptional down-regulation of PKC-alpha as evidenced by reduced PKC-alpha mRNA expression in a phosphatidylinositol 3-kinase-dependent manner and a blockade of PKC-alpha promoter activation as revealed by a reporter gene assay. Finally, reconstitution of PKC-alpha levels could restore MMP-9 expression levels in these cells. Collectively, these results show that IGF-IR can inhibit PKC-alpha gene transcription and thereby block the synthesis of PMA-regulated MMPs, suggesting that within the same cells, IGF-IR can act as both a positive and negative regulator of MMP expression and function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".