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Record W2312361153 · doi:10.18632/oncoscience.292

Circadian-disruption-induced gene expression changes in rodent mammary tissues

2016· article· en· W2312361153 on OpenAlexaff
David Z. Kochan, Yaroslav Ilnytskyy, Andrey Golubov, Scott H. Deibel, Robert J. McDonald, Olga Kovalchuk

Bibliographic record

VenueOncoscience · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsBiologyGeneGene expressionTranscriptomeBreast cancerGenome instabilityCircadian rhythmGeneticsCancerGene expression profilingDownregulation and upregulationRodentMessenger RNARegulation of gene expressionCancer researchDNA damageEndocrinologyDNA

Abstract

fetched live from OpenAlex

Evidence is mounting that circadian disruption (CD) is a potential carcinogen in breast cancer development. However, despite the growing concern, to our knowledge, no studies have attempted a genome-wide analysis of CD-induced gene expression changes in mammary tissues. Using a rodent model system, a proven photoperiod-shifting paradigm, varying degrees of CD, and Illumina sequencing, we performed an exploratory genome-wide mRNA analysis in mammary tissues. Even though our analysis did not identify any significant patterns in mRNA levels based on the degree of CD, and the majority of groups did not show changes in gene expression on a large-scale, one group (two-week chronic ZT19) displayed 196 differentially expressed genes, 51 of which have been linked to breast cancer. Through gene-specific pathway analysis, the data illustrate that CD may promote breast cancer development through downregulation of DNA repair and p53 signaling pathways, thus promoting genomic instability and cancer development. Although these results have to be interpreted with caution because only a single group illustrated drastic changes in transcript levels, they indicate that chronic CD may directly induce changes in gene expression on a large-scale with potentially malignant consequences.

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 score0.019
Threshold uncertainty score0.319

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 head0.017
GPT teacher head0.278
Teacher spread0.260 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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