The Impact of DTYMK as a Prognostic Marker in Colorectal Cancer
Bibliographic record
Abstract
Background: Overexpression of deoxythymidylate kinase (DTYMK) has been associated with more aggressiveness and pathological behaviors in hepatocellular carcinoma (HCC) and non-small cell lung cancer (NSCLC). However, the expression of DTYMK and its prognostic significance in colorectal cancer (CRC) patients are yet unknown. The goal of this study was to investigate the DTYMK immunohistochemistry reactivity in CRC tissues and to see how it correlated with various histological and clinical features as well as survival. Methods: Several bioinformatics databases and two tissue microarrays (TMAs) of 227 cases were used in this study. Immunohistochemistry assay was used to study the protein expression of DTYMK. Results: Based on the GEPIA, UALCAN, and Oncomine databases, DTYMK expression has increased in tumor tissues at both RNA and protein levels in colorectal adenocarcinoma (COAD) compared to normal tissues. A high DTYMK H-score was found in 122/227 (53%) of the cases, whereas a low DTYMK H-score was found in 105/227. The age at diagnosis (P = 0.036), stage of the disease (P = 0.038), and site of origin (P = 0.032) were all linked to a high DTYMK H-score. Patients with high level of DTYMK had bad overall survival. Interestingly, high DTYMK protein level was associated with PSM2 (P = 0.002) and MSH2 (P = 0.003), but not with MLH2 or MSH6. Conclusion: This is the first study to cover the expression and prognostic significance of DTYMK in CRC. DTYMK was upregulated in CRC and could be considered as a prognostic biomarker.
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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 | 0.001 | 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".