The Potential Use of N-Myristoyltransferase as a Biomarker in the Early Diagnosis of Colon Cancer
Bibliographic record
Abstract
Colon cancer is one of the most common malignant diseases and a major cause of mortality in the Western world. Metastasis to lymph nodes and other gastrointestinal organs, especially to the liver and lungs, is most common and occurs in up to 25% of cancer patients when initially diagnosed. The majority of colon cancers develop from noncancerous adenomatous polyps on the lining of the colon which grow over the years to become cancerous. If detected early, the surgical resections of the growth, often in combination with chemotherapy, significantly increases life expectancy. We have shown that the enzyme N-myristoyltransferase (NMT) which carries out lipid modification of several proteins (including many of those involved in oncogenesis) is expressed at higher levels in cancerous tissues from the colon. We have also shown that in peripheral blood mononuclear cells (PBMC) and bone marrow (BM) cells collected from colon cancer patients and from azoxymethane-induced rats the expression and localization of NMT is altered. We have observed strong positivity for NMT in immunohistochemical analysis for PBMC from colon cancer patients as compared to control groups. Furthermore, in the bone marrow (BM) mononuclear cells, NMT was found to be confined to the nuclei whereas in control groups it was observed to be located in the cytoplasm. In conclusion, this strikingly differential localization offers the basis of a potential investigational tool for screening or diagnosis of individuals at risk for or suspected of having colon cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".