Variance in the Expression of 5-Fluorouracil Pathway Genes in Colorectal Cancer
Bibliographic record
Abstract
Although colorectal cancer has the third highest cancer mortality rate, the treatment remains far from optimized with patients showing variable responses to standard treatment. Molecular differences in pharmacologically relevant genes may contribute to the variability in response. This study used Taqman PCR to investigate the expression of 24 5-fluorouracil (5-FU) pathway genes in colorectal cancer using paired nontumor and tumor sample from 52 patients with Dukes' C colon cancer. In comparing tumor versus nonmalignant tissue, 14 of the 24 genes showed significant variation in gene expression. For 11 of these same genes (FPGS, DHFR, GGH, NME1, NME2, RRM2, UMPH2, UNG, UMPS, TP53, and TK1), a significant proportion of the patients showed an over expression of the particular gene in tumor tissue with a tumor-to-nonmalignant (T/N) ratio >1.2, whereas one gene (DPYD) showed the converse with a large number of patients showing a lower expression in the tumor tissue (T/N < 0.8). Multiple gene correlations for the genes of the 5-FU pathway were found with the Spearman rank correlation of >0.6 (all P > 0.001), suggesting possible coregulation mechanisms. Hierarchical clustering analysis created at least three groups of genes, which were consistent with groupings by the other statistical methods. Additionally, the hierarchical clustering showed two distinct groups of patients based on their gene expression. These variations in gene expression could provide valuable insights for optimizing treatment selection for patients with colorectal 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".