Oral Presentations 4
Bibliographic record
Abstract
Small cell lung cancer (SCLC) is characterised by the expression of neuronal genes not seen in non-SCLC (NSCLC) or normal lung. The full-length neuron-restrictive silencer factor (NRSF) is a transcriptional repressor of neuronal genes in non-neuronal cells. We previously identifi ed a splice variant of NRSF that encodes a truncated sNRSF isoform expressed only in SCLC, and aimed to determine its function and downstream targets. We have shown that the expression of several NRSF-regulated genes correlated with that of sNRSF in lung cancer and used reporter constructs based on NRSF-regulated promoters, such as arginine vasopressin (AVP), as readout for sNRSF function. Mutation of an NRSF binding site reduced transcription dependent on the AVP promoter in SCLC by 50% (p<0.005), whilst overexpression of sNRSF could activate the AVP promoter in NSCLC where it is normally silenced. Interestingly, overexpression of sNRSF also resulted in signifi cantly increased proliferation of NSCLC (p<0.05). RNA interference (RNAi) was used to further investigate the role of sNRSF. Knockdown in lung cancer cells was optimised using published sequences to target other proteins, and fi ve RNAi sequences targeted to NRSF or sNRSF were then designed and evaluated. A SCLC line was established that stably expresses EGFP and sNRSF RNAi, preliminary RT-PCR and immunocytochemistry show down-regulated NRSF. These cells also had a reduced ability to activate an AVP reporter construct. Taken together, these data support the role of sNRSF as a transcriptional activator that antagonises full-length NRSF, and suggest that it may be a key transcriptional regulator in SCLC. We are currently further characterising the full profi le of NRSF splicing in SCLC. Lung cancer cells with modulated sNRSF expression will now be used to identify novel target genes regulated by sNRSF in lung cancer by microarray and proteomic analysis. Identifi cation of biological relevant genes will help us to understand the role of sNRSF and may ultimately provide new opportunities for developing detection or treatment strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".