MétaCan
Menu
Back to cohort
Record W2782759066 · doi:10.18632/oncotarget.24070

Differentially expressed microRNAs in lung adenocarcinoma invert effects of copy number aberrations of prognostic genes

2018· article· en· W2782759066 on OpenAlexaffabout
Tomáš Tokár, Chiara Pastrello, Varune Rohan Ramnarine, Chang‐Qi Zhu, Kenneth J. Craddock, Larrisa A. Pikor, Emily A. Vucic, Simon Vary, Frances A. Shepherd, Ming‐Sound Tsao, Wan L. Lam, Igor Jurišica

Bibliographic record

VenueOncotarget · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of TorontoVancouver General HospitalOccupational Cancer Research CentrePrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsmicroRNABiologyCopy-number variationGeneAdenocarcinomaEpigeneticsComparative genomic hybridizationGene expressionGene dosageGene expression profilingLung cancerRegulation of gene expressionGeneticsCancer researchGenomeCancerOncologyMedicine

Abstract

fetched live from OpenAlex

// Tomas Tokar 1 , Chiara Pastrello 1 , Varune R. Ramnarine 1, 2 , Chang-Qi Zhu 1 , Kenneth J. Craddock 1 , Larrisa A. Pikor 3 , Emily A. Vucic 3 , Simon Vary 1, 4, 5 , Frances A. Shepherd 1 , Ming-Sound Tsao 1, 6, 7 , Wan L. Lam 3 and Igor Jurisica 1, 6, 8, 9 1 Princess Margaret Cancer Centre, University Health Network, Toronto, Canada 2 The Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, Canada 3 Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, Canada 4 Mathematical Institute, University of Oxford, Oxford, United Kingdom 5 Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia 6 Department of Medical Biophysics, University of Toronto, Toronto, Canada 7 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada 8 Department of Computer Science, University of Toronto, Toronto, Canada 9 Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia Correspondence to: Igor Jurisica, email: juris@ai.utoronto.ca Keywords: lung adenocarcinoma; copy number aberrations; microRNA; gene regulatory network; prognostic signature Received: August 25, 2017      Accepted: January 02, 2018      Published: January 08, 2018 ABSTRACT In many cancers, significantly down- or upregulated genes are found within chromosomal regions with DNA copy number alteration opposite to the expression changes. Generally, this paradox has been overlooked as noise, but can potentially be a consequence of interference of epigenetic regulatory mechanisms, including microRNA-mediated control of mRNA levels. To explore potential associations between microRNAs and paradoxes in non-small-cell lung cancer (NSCLC) we curated and analyzed lung adenocarcinoma (LUAD) data, comprising gene expressions, copy number aberrations (CNAs) and microRNA expressions. We integrated data from 1,062 tumor samples and 241 normal lung samples, including newly-generated array comparative genomic hybridization (aCGH) data from 63 LUAD samples. We identified 85 “paradoxical” genes whose differential expression consistently contrasted with aberrations of their copy numbers. Paradoxical status of 70 out of 85 genes was validated on sample-wise basis using The Cancer Genome Atlas (TCGA) LUAD data. Of these, 41 genes are prognostic and form a clinically relevant signature, which we validated on three independent datasets. By meta-analysis of results from 9 LUAD microRNA expression studies we identified 24 consistently-deregulated microRNAs. Using TCGA-LUAD data we showed that deregulation of 19 of these microRNAs explains differential expression of the paradoxical genes. Our results show that deregulation of paradoxical genes is crucial in LUAD and their expression pattern is maintained epigenetically, defying gene copy number status.

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 score0.042
Threshold uncertainty score0.513

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 head0.006
GPT teacher head0.246
Teacher spread0.241 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueOncotargetSame topicMicroRNA in disease regulationFrench-language works237,207