Epidermal Growth Factor Receptor Controls Flat Dysplastic Aberrant Crypt Foci Development and Colon Cancer Progression in the Rat Azoxymethane Model
Bibliographic record
Abstract
PURPOSE: Colonic carcinogenesis deranges growth-regulating epidermal growth factor receptors (EGFR). We previously showed that EGFR signals were up-regulated in human aberrant crypt foci (ACF), putative colon cancer precursors. The azoxymethane model of colon cancer recapitulates many aspects of human colonic tumors. Recent studies indicate that flat dysplastic ACF with increased beta-catenin are tumor precursors in this model. We asked, therefore, if EGFR signals are required for flat dysplastic ACF development and cancer progression. EXPERIMENTAL DESIGN: Rats received azoxymethane or saline, and standard chow or chow supplemented with gefitinib, an EGFR inhibitor, for 44 weeks. EGFR signals were quantified in normal colon, flat ACF, and tumors by computerized analysis of immunostains and Western blots. K-ras mutations were assessed by PCR and mRNA for egfr ligands by quantitative real-time PCR. RESULTS: EGFR inhibition with gefitinib decreased the incidence of flat dysplastic ACF from 66% to 36% and tumors from 71% to 22% (P < 0.05). This inhibitor also reduced the overexpressions of cyclin D1 and Cox-2 in flat ACF. Furthermore, in flat ACF, EGFR blockade decreased the up-regulation of c-Jun, FosB, phosphorylated active signal transducers and activators of transcription 3, and CCAAT/enhancer binding protein-beta, potential regulators of cyclin D1 and Cox-2. In colonic tumors, EGFR blockade significantly decreased angiogenesis, proliferation, and progression while also increasing apoptosis (P < 0.05). Gefitinib also inhibited the activations of extracellular signal-regulated kinase, Src, and AKT pathways in tumors. CONCLUSIONS: We have shown for the first time that EGFR promotes the development of flat dysplastic ACF and the progression of malignant colonic tumors. Furthermore, we have mechanistically identified several transcription factors and their targets as EGFR effectors in colonic carcinogenesis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".