Allelic Loss of a Common Microsatellite Marker<b><i>MYCL1</i></b>
Bibliographic record
Abstract
PURPOSE: Allelic loss involving chromosome arms 5q, 8p, 17p, and 18q is commonly detected in colorectal cancer (CRC). The short arm of chromosome 1 is also frequently affected in a whole range of cancer types, including CRC. Our aim in the present study was to determine whether allelic losses on 1p were likely to be of much value in predicting the prognosis of CRC cases. EXPERIMENTAL DESIGN: Genomic DNA was prepared from tumor and corresponding normal tissue specimens from 90 patients who had undergone curative resection for CRC. Loss of heterozygosity (LOH) on chromosome arms 1p, 2p, 5q, 7q, 8p, 17p, 17q, and 18q was examined using 14 microsatellite markers, and possible correlations between LOH and clinicopathological factors (including tumor recurrence and patient survival) were investigated. LOH at the MYCL1 microsatellite marker at 1p34 was detected in 12 of 74 (16.2%) patients who were informative for this marker. RESULTS: After controlling for tumor stage and gender and excluding findings for patients with remote metastasis, we found that patients who were positive for LOH at MYCL1 were 31 times more likely to experience recurrence than those who were negative for LOH at this locus (95% confidence intervals, 2.27- infinity; P = 0.04). There were indications of a similar tendency for LOH at the 14-3-3-sigma-TG microsatellite marker at 1p35, but we could find no evidence of a significant association between LOH at this site and tumor recurrence or patient survival. We were also unable to detect significant association between LOH at the various sites on 2p, 5q, 7q, 8p, 17p, 17q, and 18q and either tumor recurrence or patient survival. CONCLUSIONS: CRC patients whose tumors exhibited LOH at MYCL1 at chromosome 1p34 were likely to have a poor prognosis, suggesting that this marker may have clinical relevance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".