Polycomb group protein gene silencing, non-coding RNA, stem cells, and cancerThis paper is one of a selection of papers published in this Special Issue, entitled The 30th Annual International Asilomar Chromatin and Chromosomes Conference, and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
Epigenetic programming is an important facet of biology, controlling gene expression patterns and the choice between developmental pathways. The Polycomb group proteins (PcGs) silence gene expression, allowing cells to both acquire and maintain identity. PcG silencing is important for stemness, X chromosome inactivation (XCI), genomic imprinting, and the abnormally silenced genes in cancers. Stem and cancer cells commonly share gene expression patterns, regulatory mechanisms, and signalling pathways. Many microRNA species have oncogenic or tumor suppressor activity, and disruptions in these networks are common in cancer; however, long non-coding (nc)RNA species are also important. Many of these directly guide PcG deposition and gene silencing at the HOX locus, during XCI, and in examples of genomic imprinting. Since inappropriate HOX expression and loss of genomic imprinting are hallmarks of cancer, disruption of long ncRNA-mediated PcG silencing likely has a role in oncogenesis. Aberrant silencing of coding and non-coding loci is critical for both the genesis and progression of cancers. In addition, PcGs are commonly abnormally overexpressed years prior to cancer pathology, making early PcG targeted therapy an option to reverse tumor formation, someday replacing the blunt instrument of eradication in the cancer therapy arsenal.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".