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Record W2596680439 · doi:10.1080/15592294.2017.1305527

Modulation of transcription factor binding and epigenetic regulation of the <i>MLH1</i> CpG island and shore by polymorphism rs1800734 in colorectal cancer

2017· article· en· W2596680439 on OpenAlexafffund
Andrea J. Savio, Bharati Bapat

Bibliographic record

VenueEpigenetics · 2017
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity Health NetworkSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsBiologyEpigeneticsCpG siteDNA methylationMLH1Transcription factorEnhancerEpigenetics of physical exercisePromoterChromatin immunoprecipitationMethylationMolecular biologyGeneticsCancer researchGeneColorectal cancerCancerGene expressionDNA mismatch repair

Abstract

fetched live from OpenAlex

The MLH1 promoter polymorphism rs1800734 is associated with MLH1 CpG island hypermethylation and expression loss in colorectal cancer (CRC). Conversely, variant rs1800734 is associated with MLH1 shore, but not island, hypomethylation in peripheral blood mononuclear cell DNA. To explore these distinct patterns, MLH1 CpG island and shore methylation was assessed in CRC cell lines stratified by rs1800734 genotype. Cell lines containing the variant A allele demonstrated MLH1 shore hypomethylation compared to wild type (GG). There was significant enrichment of transcription factor AP4 at the MLH1 promoter in GG and GA cell lines, but not the AA cell line, by chromatin immunoprecipitation studies. Preferential binding to the G allele was confirmed by sequencing in the GA cell line. The enhancer-associated histone modification H3K4me1 was enriched at the MLH1 shore; however, H3K27ac was not, indicating the shore is an inactive enhancer. These results demonstrate the role of variant rs1800734 in altering transcription factor binding as well as epigenetics at regions beyond the MLH1 CpG island in which it is located.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.271
Teacher spread0.253 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2017
Admission routes2
Has abstractyes

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