Deregulation of small non-coding RNAs at the <i>DLK1-DIO3</i> imprinted locus predicts lung cancer patient outcome
Bibliographic record
Abstract
// Katey S.S. Enfield 1 , Victor D. Martinez 1 , Erin A. Marshall 1 , Greg L. Stewart 1 , Sonia H.Y. Kung 1 , Jhon R. Enterina 1 , Wan L. Lam 1 1 Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, B.C. V5Z 1L3, Canada Correspondence to: Katey S.S. Enfield, email: kenfield@bccrc.ca Keywords: DLK1-DIO3, imprinted locus, lung cancer, microRNA, piwi-interacting RNA Received: September 02, 2016 Accepted: October 27, 2016 Published: November 05, 2016 ABSTRACT Deregulation of the imprinted DLK1 - DIO3 locus at chromosome 14q32.1-14q32.31 has been associated with developmental and respiratory disorders, including cancer. In lung cancer, deregulation of imprinting at DLK1 - DIO3 was recently described in smokers. Deregulated expression of a microRNA (miRNA) cluster mapping to this locus was also associated with patient outcome, suggesting the importance of this locus to lung cancer disease phenotypes. The DLK1 - DIO3 locus is complex, and encodes several protein-coding genes, in addition to long and short non-coding RNAs. While the role of miRNAs is established, the biological importance of another relevant class of small RNAs, PIWI-interacting RNAs (piRNAs), has not been investigated. When somatically expressed, piRNAs regulate gene transcription through DNA methylation. Interestingly, their expression patterns have been observed to be altered in cancer and correlated with patient outcome. Here, we characterize the somatic expression of piRNAs encoded at DLK1 - DIO3 in two independent cohorts of lung adenocarcinoma and lung squamous cell carcinoma and investigate their associations with patient outcome. We find that the expression of piRNAs encoded at DLK1-DIO3 enhances the prognostic potential of small non-coding RNAs specific to this locus in predicting patient outcome, further emphasizing the importance of regulation at this locus in lung cancer.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 0.001 |
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".