Analysis of chromosomal instability in pulmonary or liver metastases and matched primary hepatocellular carcinoma after orthotopic liver transplantation
Bibliographic record
Abstract
To investigate the genetic mechanism of metastatic spread in hepatocellular carcinoma (HCC), we analyzed genomic changes in lung or liver metastases and the corresponding primary tumors (83 tumor samples) in 18 patients who underwent orthotopic liver transplantation. We studied the incidence of microsatellite instability (MSI) and loss of heterozygosity (LOH) involving 8 highly polymorphic microsatellite markers and the polyA tract, Bat26. We also sought alterations of p53 and beta-catenin gene mutations. High MSI (>30-40% of the loci analyzed) was found only in primary tumors (11%), whereas LOH was observed in 50% of primary and in 39% of recurrent tumors. p53 mutations were found in 2 cases of primary HCC but not in the corresponding metastases. P53 was overexpressed in 4 primary HCC (22%) and 7 metastases (39%). The percentage of beta-catenin gene mutations was low (6%). Lung metastases retained the D16S402 microsatellite abnormalities observed in the primary tumors, whereas recurrent liver tumor did not (p = 0.02). In conclusion, LOH and P53 protein overexpression, rather than mutations in the p53 or beta-catenin genes or MSI, seem to be involved in the spreading of HCC, suggesting the presence of metastasis suppressor genes in the vicinity of the chromosomal loci in question.
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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) | 0.001 | 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".