Novel Biomarkers for Prostate Cancer Progression
Bibliographic record
Abstract
Prostate cancer is the most frequently diagnosed type of cancer in Canadian men, with 1 in every 8 men being diagnosed with it at some point in their life. It is, however, often very manageable if detected early enough. Determining which patients have a high potential for progression remains a significant barrier in the treatment of this disease. Using data collected from prostate cancer patients, the University of Windsor computer science department developed technology to isolate novel RNA splice variants that are differentially expressed through progression of prostate cancer. This could indicate a mechanism by which prostate cancer evolves and could provide valuable prognostic markers. To validate the biological significance of these RNA variants, RNA was collected from prostate cells at various stages of progression to aggressive, androgen-independent cancer. Expression of the various transcript variants was analyzed using quantitative-real-time-PCR. One particular splice variant that has emerged as potentially important is from the WWP2 gene, an E3 ubiquitin ligase which has previously been shown to down-regulate the tumour suppressor PTEN. We have demonstrated that protein expression patterns mimic that of the RNA transcript, being increased abruptly at stage III prostate cancer. We are currently investigating how this specific splice pattern can alter WWP2 expression by knocking down expression of the transcript and testing the effects on cell growth and proliferation through various cell culture assays. Expression profiles of these splice variants could act as important biomarkers indicating the severity and likelihood of progression. This could be an invaluable tool in preventing overtreatment leaving life-long side effects of treatment for cancers not likely to progress. Furthermore targeting some of the gene products, like WWP2, may represent a valuable treatment option for aggressive late stage cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".