<i>Apc<sup>Min</sup></i><sup>/+</sup> mouse model of colon cancer: Gene expression profiling in tumors
Bibliographic record
Abstract
The Apc(Min/+) mouse is a popular animal model for studies of human colon cancer, but the molecular changes associated with neoplasia in this system have only been partially characterized. Our aim was to identify novel genes involved in tumorigenesis in this model. RNA from intestinal adenomas and from pre-neoplastic small intestine were prepared from six Apc(Min/+) mice. The tumor transcriptomes were analyzed with high-density oligonucleotide microarrays representing approximately 12,000 probe sets; we compared their profiles with those of matched pre-neoplastic intestine. Stringent analysis revealed reproducible changes for 98 probe sets representing 90 genes, including novel observations regarding 50 genes whose involvement in this mouse model has never been reported. In addition to the expected changes in growth regulatory genes, the altered gene products could be assigned to four functional groupings that should enhance tumorigenesis: metabolic changes that would result in a high rate of glycolysis, alterations in enzymes involved in reactive oxygen species or carcinogen metabolism, cytoskeletal elements, and proteins involved in tumor invasion or angiogenesis. A fifth group consisted of expression changes that might restrict tumor progression, suggesting that the adenomatous state reflects a balance of pro- and anti-tumorigenic factors. Since many of the altered genes had not previously been reported to be involved in any tumorigenic processes, our observations provide a host of new candidates for potential modulation to prevent or treat intestinal neoplasia.
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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".