Differentially expressed microRNAs in lung adenocarcinoma invert effects of copy number aberrations of prognostic genes
Bibliographic record
Abstract
// 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".