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Record W2537332001 · doi:10.1002/gcc.22420

Germline miRNA DNA variants and the risk of colorectal cancer by subtype

2016· article· en· W2537332001 on OpenAlexaff
Noralane M. Lindor, Melissa C. Larson, Melissa S. DeRycke, Shannon K. McDonnell, Saurabh Baheti, Zachary C. Fogarty, Aung Ko Win, John D. Potter, Daniel D. Buchanan, Mark Clendenning, Polly A. Newcomb, Graham Casey, Steven Gallinger, Loı̈c Le Marchand, John L. Hopper, Mark A. Jenkins, Ellen L. Goode, Stephen N. Thibodeau

Bibliographic record

VenueGenes Chromosomes and Cancer · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersCenters for Disease Control and PreventionNational Center for Chronic Disease Prevention and Health PromotionUniversity of Hawai'iUniversity of Southern CaliforniaNational Cancer InstituteNational Institutes of HealthCalifornia Department of Public HealthPublic Health Institute
KeywordsmicroRNAColorectal cancerBiologyGermlineGeneCancerGeneticsGermline mutationGenome-wide association studyGenetic variationSingle-nucleotide polymorphismMutationOncologyBioinformaticsMedicineGenotype

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) regulate up to one-third of all protein-coding genes including genes relevant to cancer. Variants within miRNAs have been reported to be associated with prognosis, survival, response to chemotherapy across cancer types, in vitro parameters of cell growth, and altered risks for development of cancer. Five miRNA variants have been reported to be associated with risk for development of colorectal cancer (CRC). In this study, we evaluated germline genetic variation in 1,123 miRNAs in 899 individuals with CRCs categorized by clinical subtypes and in 204 controls. The role of common miRNA variation in CRC was investigated using single variant and miRNA-level association tests. Twenty-nine miRNAs and 30 variants exhibited some marginal association with CRC in at least one subtype of CRC. Previously reported associations were not confirmed (n = 4) or could not be evaluated (n = 1). The variants noted for the CRCs with deficient mismatch repair showed little overlap with the variants noted for CRCs with proficient mismatch repair, consistent with our evolving understanding of the distinct biology underlying these two groups. © 2016 The Authors Genes, Chromosomes & Cancer Published by Wiley Periodicals, Inc.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.226

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 head0.004
GPT teacher head0.229
Teacher spread0.224 · 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 designBench or experimental
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

Citations10
Published2016
Admission routes1
Has abstractyes

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