Abstract A21: MiR-106a and miR-106b affect growth and metastasis of lung adenocarcinoma
Bibliographic record
Abstract
Abstract Introduction: MiR-106a and miR-106b are paralogs of the oncogenic miR-17~92, and have been associated with poor outcome and metastasis in several solid tumors. Their role in lung cancer is relatively unexplored. We characterized the expression of miR-106a and miR-106b in a clinical cohort of lung adenocarcinoma (AC) tumors and assessed their ability to regulate growth and metastasis in cell models. Methods: MicroRNA (miRNA) expression was deduced from small RNA sequencing data derived from clinical lung AC specimens (60 localized, 27 with lymph node invasion) and paired non-malignant tissues. MiR-106a and miR-106b overexpression vectors and controls were stably transfected into immortalized non-malignant Human Bronchial Epithelial Cells (HBECs) and stage I AC cell lines with epithelial expression patterns by lentiviral delivery. Migration and invasion was assessed by Boyden chamber assay, while cell proliferation was assessed by BrdU incorporation assay. Expression of epithelial-to-mesenchymal transition (EMT) markers and other proteins of interest were assessed by Western Blot. Clinical associations in an external cohort were derived using publically available TCGA data. Results: MiR-106a and miR-106b were significantly overexpressed in lung AC with lymph node invasion. Overexpression of miR-106a and miR-106b significantly increased proliferation of lung AC cell lines, and was associated with decreased levels of predicted target, p21. AC cell lines displayed a marked increase in metastatic phenotypes in vitro, and were associated with increased mesenchymal and decreased epithelial markers, characteristic of EMT. Importantly, tumors with high expression of both miR-106a and miR-106b and mesenchymal marker vimentin had significantly poorer outcome. Conclusions: MiR-106a and miR-106b are overexpressed in metastatic lung AC. Lung AC cell models indicate these miRNAs are metastatic agonists, affecting the metastatic potential of cells at least in part via induction of EMT. A deeper characterization of this observation may reveal therapeutic intervention points, or, with the development of miRNA therapeutics, miR-106a/b may be promising targets to prevent or treat metastatic disease. Citation Format: Katey SS Enfield, David A. Rowbotham, Alice Holly, Christine Anderson, Kevin W. Ng, Brenda de Carvalho Minatel, Graham Dellaire, Chiara Pastrello, Igor Jurisica, Calum MacAulay, Stephen Lam, Wan L. Lam. MiR-106a and miR-106b affect growth and metastasis of lung adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr A21.
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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 | 0.000 |
| 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 | 0.000 | 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".