Differential Expression of Long Noncoding RNAs in the Livers of Female B6C3F1 Mice Exposed to the Carcinogen Furan
Bibliographic record
Abstract
The mammalian genome is transcribed into mRNAs that code for protein and a broad spectrum of other noncoding (nc) RNA products. Long ncRNAs (lncRNA), defined as ncRNA species > 200 nucleotides long, are emerging as important epigenetic regulators of gene expression that are involved in a spectrum of biological processes of relevance to toxicology. We conducted a gene expression profiling study in the livers of female B6C3F1 mice exposed to the carcinogen furan at 0.0, 1.0, and 2.0mg/kg (noncarcinogenic doses) and at 4.0 and 8.0mg/kg (carcinogenic doses) for 3 weeks. LncRNA differential expression showed a nonlinear dose response with none differentially expressed at 1.0 or 2.0mg/kg, 2 lncRNAs at 4.0mg/kg furan, and 83 at 8mg/kg, representing 13.3% (83/632) of the total number of differentially expressed transcripts. Among the lncRNAs observed, two lncRNAs examined showed transcriptional clustering with nearby protein-coding genes. LincRNA-p21 is an antisense transcript that is 15kb downstream from Cdkn1a locus and appears to be cotranscribed with the protein coding gene Cdkn1a at 8.0mg/kg furan. In a separate independent study, RNA samples from the livers of mice administered benzo(a)pyrene also demonstrated increased levels of Cdkn1a and the antisense lincRNA-p21 transcript. These data demonstrate that lncRNAs are transcriptional targets of furan exposures associated with levels of furan that are cytotoxic and induce cell proliferation. In addition, certain lncRNA transcripts are associated with the expression of nearby coding protein genes. We hypothesize that lncRNAs have potential as epigenetic biomarkers of carcinogenic exposures.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".