Structure and Function of the Perinucleolar Compartment in Cancer Cells
Bibliographic record
Abstract
The perinucleolar compartment (PNC) is a subnuclear body that forms in cancer cells. In vivo analyses using human tumor tissues demonstrate a close correlation between PNC prevalence and disease progress in colorectal carcinoma, and a high PNC prevalence is associated with poor patient outcome. These findings are consistent with previous observations in breast cancer and cancer cell lines in vitro. The PNC is composed of thick strands that form a filamental meshwork often extending into the nucleolus. Although it appears to be electron dense as observed by transmission electron microscopy (TEM), the actual density of the structure imaged by electron spectroscopy is much lower, similar to that of the interchromatin space, and is lined with ribonucleoproteins (RNPs). In situ detections show that the PNC is highly enriched with a subset of small RNAs of polymerase III (Pol III) origins and RNA-binding proteins primarily implicated in pre-mRNA processing. A novel gel-shifting approach demonstrates that the addition of PNC-associated RNAs into HeLa cell lysates increases the mobility of polypyrimidine tract-binding (PTB) protein in a native gel electrophoresis, suggesting an interaction between these RNAs and PTB proteins. On the basis of these and other findings, we propose a working model in which novel RNPs have a key role in regulating gene expression at the PNC in cancer cells.
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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) | 0.001 | 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".