Chromosome 18 suppresses tumorigenic properties of human prostate cancer cells
Bibliographic record
Abstract
Although prostate cancer is still the most diagnosed cancer in men, most genes implicated in its progression are yet to be identified. Chromosome abnormalities have been detected in human prostate tumors, many of them associated with prostate cancer progression. Indeed, alterations (including deletions or amplifications) of more than 15 human chromosomes have been reported in prostate cancer. We hypothesized that transferring normal human chromosomes into human prostate cancer cells would interfere with their tumorigenic and/or metastatic properties. We used microcell-mediated chromosome transfer to introduce human chromosomes 10, 12, 17, and 18 into highly tumorigenic (PC-3M-Pro4) and highly metastatic (PC-3M-LN4) PC-3-derived cell lines. We tested the in vitro and in vivo properties of these hybrids. Introducing chromosome 18 into the PC-3M-LN4 prostate cancer cell line greatly reduced its tumorigenic phenotype. We observed retarded growth in soft agar, decreased invasiveness through Matrigel, and delayed tumor growth into nude mice, both subcutaneously and orthotopically. This phenotype is associated with a marker in the 18q21 region. Combined with the loss of human chromosome 18 regions often seen in patients with advanced prostate cancer, our results show that chromosome 18 encodes one or more tumor-suppressor genes whose inactivation contributes to prostate cancer progression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".