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Record W4298353256 · doi:10.1002/hep.22641

AASLD Abstracts 220-442

2008· article· et· W4298353256 on OpenAlexaff
Fanyin Meng, Chiara Braconi, Lyudmila Khrapenko, Tushar Patel, Alphonse E. Sirica, Zichen Zhang, Deanna J. Campbell, Susie Lee, Coral Ho, Xin Chen, Chang Han, William C. Bowen, George K. Michalopoulos, Tong Wu, Mike A. Leonis, Angela M. Longmeier, Jeremy Gibson, Susan E. Waltz, Natalia Pediconi, Stefania Vossio, Valeria Schinzari, Teresa Pollicino, Giuseppe Montalto, Melchiorre Cervello, Giovanni Raimondo, Massimo Levrero, Gio- Vanni Raimondo, Karin E. R. Gooijert, Rick Havinga, Henk Wolters, Renxue Wang, Victor Ling, Susumu Tazuma, Henkjan J. Verkade

Bibliographic record

VenueHepatology · 2008
Typearticle
Languageet
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsCitationMedicineLibrary scienceInformation retrievalWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.286
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.7140.618

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.

Opus teacher head0.017
GPT teacher head0.235
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations5
Published2008
Admission routes1
Has abstractyes

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