Whole-transcriptome analysis reveals established and novel associations with TMPRSS2:ERG fusion in prostate cancer.
Bibliographic record
Abstract
BACKGROUND/AIM: Shortcomings of current methods of prostate cancer detection call for improved biomarkers. The transmembrane protease, serine 2:ets-related gene (TMPRSS2:ERG) gene fusion leads to the overexpression of ERG, an E-twenty six (ETS) family transcription factor, and is the most prevalent genetic lesion in prostate cancer, but its clinical utility remains unclear. MATERIALS AND METHODS: Two radical prostatectomy samples were analysed by next-generation whole-transcriptome sequencing. The chosen samples differed in fusion gene status, as previously determined by reverse transcription polymerase chain reaction (RT-PCR). RESULTS: Next-generation sequencing identified the involvement of novel and previously reported prostate cancer-related transcripts, the WNT signalling pathway, evasion of p53-mediated anti-proliferation and several ETS-regulated pathways in the prostate cancer cases examined. Overexpression of Rho GDP-dissociation inhibitor (RhoGDIB), a gene associated with fusion-positive prostate cancer, was found to elicit spindle-shaped morphology, faster cell migration and increased cell proliferation, phenotypic changes suggestive of cancer progression. CONCLUSION: The present findings confirm the value of comprehensive sequencing for biomarker development and provide potential avenues of future study.
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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) | 0.001 | 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".