AASLD Abstracts 220-442
Bibliographic record
Abstract
Background: Autocrine expression of Interleukin-6 (IL-6) contributes to cholangiocarcinoma growth. IL-6 can modulate the expression of miRNA involved in tumor growth, and can also alter the expression of DNA methyltransferase 1 (DNMT-1). Alterations in DNMT-1 can epigenetically regulate the expression of tumor suppressor genes. Thus, our aim was to evaluate the role of IL-6 dependent miRNA on modulation of DNMT-1 and tumor suppressor gene expression in cholangiocarcinoma. Methods: Mz-ChA-1, KMCH-1, TFK-1 malignant and H69 nonmalignant cholangiocytes were used. Tumor cells were stably transfected to over-express IL-6. miRNA profiling was performed using a microarray and the expression of selected mature miRNA verified using real-time PCR. Luciferase reporter constructs were used to assess direct effects of miRNA at sites on DNMT-1 3'-UTR. Precursors to miRNA were used to enhance cellular expression. miRNA target prediction was performed using PicTar and Miranda databases. DNMT-1, p16INK4A and Rassf1a expression were assessed by western blot analysis. Results: miR-148a, miR-152 and miR-301 have sequence complementarity to the 3'-UTR of DNMT-1. Relative to non-malignant H69 cells, expression of miR-148a, miR-152 and miR-301 was decreased in Mz-ChA-1 cells by 0.25 0.03-fold, 0.23 0.02fold, and 0.07 0.01-fold. Similar changes were noted in KMCH-1 and TFK cells. Expression of both miR-148a and miR-152 was decreased in IL-6 over-expressing tumor cells compared to controls in vitro (miR-148a: 0.2 0.1-fold; miR-152: 0.1 0.1-fold) and in tumor cell xenografts in vivo (miR-148a: 0.6 0.1-fold; miR-152: 0.6 0.1-fold). There was a concomitant decrease in several methylation-sensitive tumor suppressor genes including Rassf1a, and p16INK4a. DNMT-1 was verified as a direct target for miR-148a and miR-152. miR-148a decreased DNMT-1 expression by 75 2% in Mz-ChA-1 and 62 9% in KMCH-1 cells, and reduced cell proliferation in both cell lines. miR-152 also reduced prolfieration and decreased DNMT-1 expression by 58 6 % and 62 9% in Mz-ChA-1 and KMCH-1 cells respectively. Enforced expression of either miR-148a or miR-152 also increased expression of Rassf1a and p16INK4a activation. Summary and Conclusions: We report for the first time that IL-6 can (a) regulate DNMT1 expression by modulation of microRNAs miR-148a and miR-152, and (b) alter the expression of methylation dependent tumor suppressor genes Rassf1a and p16INK4a. These studies provide a direct link between IL-6 and epigenetic regulation of oncogenic pathways and justify targeting IL-6 for the prevention or treatment of cholangiocarcinoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".