DNA Repair Pathway Profiling and Microsatellite Instability in Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: The ability to maintain DNA integrity is a critical cellular function. DNA repair is conducted by distinct pathways of genes, many of which are thought to be altered in colorectal cancer. However, there has been little characterization of these pathways in colorectal cancer. METHOD: By using the TaqMan real-time quantitative PCR, RNA expression profiling of 20 DNA repair pathway genes was done in matched tumor and normal tissues from 52 patients with Dukes' C colorectal cancer. RESULTS: The relative mRNA expression level across the 20 DNA repair pathway genes varied considerably, and the individual variability was also quite large, with an 85.4 median fold change in the tumor tissue genes and a 127.2 median fold change in the normal tissue genes. Tumor-normal differential expression was found in 13 of 20 DNA repair pathway genes (only XPA had a lower RNA level in the tumor samples; the other 12 genes had significantly higher tumor levels, all P<0.01). Coordinated expression of ERCC6, HMG1, MSH2, and POLB (RS>or=0.60) was observed in the tumor tissues (all P<0.001). Apoptosis index was not correlated with expression of the 20 DNA repair pathway genes. MLH1 and XRCC1 RNA expression was correlated with microsatellite instability status (P=0.045 and 0.020, respectively). An inverse correlation was found between tumor MLH1 RNA expression and MLH1 DNA methylation (P=0.003). CONCLUSION: Our study provides an initial characterization of the DNA repair pathways for understanding the cellular DNA damage/repair system in human 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 | 0.001 |
| 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) | 0.001 | 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".