Abstract A29: Hypoxia regulated long noncoding RNAs and antisense transcripts in breast cancer: Novel insights of noncoding transcriptional regulation in hypoxic microenvironment
Bibliographic record
Abstract
Abstract Tumour hypoxia is a common feature of tumour microenvironment and contributes to an aggressive phenotype across multiple types of cancers. Hypoxia-inducible factor (HIF) is a key element regulating expression of hundreds of coding and non-coding RNAs, which act to both improve oxygen delivery and to reduce cellular demand of oxygen. We performed a comprehensive analysis on hypoxic transcription landscape and epigenetic markers of transcriptional activation in MCF-7 breast cancer cells under hypoxia using next generation sequencing. Analyses revealed that all classes of non-coding RNA are profoundly regulated by hypoxia including piwiRNA, miRNA, tRNA, and sn/snoRNA. Analysis revealed downregulation of snRNAs and tRNAs in hypoxia, whereas miRNAs, antisense transcripts and lncRNAs are globally upregulated in hypoxia. Many novel natural antisense transcripts are induced under hypoxia and their expression is dependent on HIF. Hypoxia-induced natural antisense transcripts are associated with both induction and repression in cis. Moreover, large numbers of lncRNAs are found overexpressed under hypoxia and are associated with HIF binding and elevated level of RNApol2 and H3K4me3, suggesting direct transcriptional regulation of lncRNAs by HIF. Among the most hypoxia induced lncRNA was NEAT1, which is a direct transcriptional target of HIF-2α. We confirmed the hypoxic NEAT1 upregulation in a number of breast cancer cell lines and in tumour xenografts models treated with bevacizumab. Hypoxia induced NEAT1 directly increase the formation of nuclear paraspeckles bodies. Moreover, hypoxic induction of NEAT1 contributes to increase cell proliferation, cell survival, and inhibit apoptosis. Furthermore, we found that NEAT1 is required to retain hypoxia induced hyper edited Junctional Adhesion Molecule A (JAM-A) mRNA in nucleus to inhibit its translation. Finally, in a large breast cancers cohort, high expression of NEAT1 is linked with poor prognosis and with different 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 hypoxic tumours through non-coding RNAs and open new avenues to find novel pathways and targets to develop therapies for breast cancer. Citation Format: Hani Choudhry, Ashwag Albukhari, Matteo Morotti, Syed Haider, Daniela Moralli, Johannes Schödel, Catherine Green, Carme Camps, Francesca Buffa, Peter Ratcliffe, Ioannis Ragoussis, David Mole, Adrian Harris. Hypoxia regulated long noncoding RNAs and antisense transcripts in breast cancer: Novel insights of noncoding transcriptional regulation in hypoxic microenvironment. [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 A29.
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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) | 0.002 | 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".