Abstract P2-05-01: The non-coding transcriptome of hypoxic breast cancer: Novel insights of clinical relevant long non-coding RNA in hypoxia signalling
Bibliographic record
Abstract
Abstract Hypoxia is associated with aggressive and poor prognosis of breast cancer. Generally, pan-genome analyses of hypoxia have focussed on protein-coding genes, however, the role of non-coding RNAs, in particular long non-coding RNAs (lncRNA) in hypoxia is not well characterised. We undertook an integrated genomic analysis of the hypoxic transcriptome in MCF7 breast cancer cells, employing total RNA-seq together with ChIP-seq for the hypoxia-inducible transcription factor (HIF) and for epigenetic marks of transcriptional activation (RNApol2 and histone H3K4me3). Analyses revealed that all classes of RNA are significantly regulated by hypoxia including piwiRNA, miRNA, tRNA, and sn/snoRNA. Significant numbers of lncRNAs were upregulated in hypoxia and were associated with increased RNApol2 and H3K4me3 markers and with HIF binding, indicating direct transcriptional activation of lncRNAs by HIF. The most hypoxiclly upregulated lncRNA was NEAT1, which is a direct transcriptional target of HIF-2a but not HIF-1a. The role of NEAT1 in cancer has not been previously studied. We demonstrated that hypoxic NEAT1 induction is common in breast cancer cell lines and xenografts models treated with bevacizumab. NEAT1 directly induces the formation of nuclear paraspeckle bodies in hypoxia. Moreover, it contributes to tumourigenicity by increasing cell proliferation, colony formation, and reducing apoptosis. In addition, we report that NEAT1 is required to retain hypoxia induced hyper edited Junctional Adhesion Molecule A (JAM-A) mRNA in the nucleus, thus preventing export into the cytoplasm for translation. Finally, in a large cohort of 2000 breast cancers, high levels of NEAT1 were associated with poor clinical outcomes and clinicopathological features. Our results extend knowledge of the hypoxic transcriptional response into the spectrum of non-coding transcripts. These findings provide novel mechanisms of transcriptional regulation in hypoxia and open new avenues to find novel pathways and targets to develop therapies for breast cancer. Citation Format: Hani Choudhry, Johannes Schodel, Ashwag Albukhari, Syed Haider, Francesca Buffa, Peter J Ratcliffe, David R Mole, Ioannis Ragousis, Adrian L Harris. The non-coding transcriptome of hypoxic breast cancer: Novel insights of clinical relevant long non-coding RNA in hypoxia signalling [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-05-01.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".