Identifying differentially expressed transcripts associated with prostate cancer progression using RNA-Seq and machine learning techniques
Bibliographic record
Abstract
Background: Prostate cancer is complicated by a high level of unexplained variability in the aggressiveness of newly diagnosed disease. Given that this is one of the most prevalent cancers worldwide, finding biomarkers to effectively stratify high risk patient populations is a vital next step in improving survival rates and quality of life after treatment. Materials and Methods: In this study, we selected a dataset consisting of 106 prostate cancer samples, which represent various stages of prostate cancer and developed by RNA-Seq technology. Our objective is to identify differentially expressed transcripts associated with prostate cancer progression using pair-wise stage comparisons. Results: Using machine learning techniques, we identified 44 transcripts that are correlated to different stages of progression. Expression of an identified transcript, USP13, is reduced in stage T3 in comparison with stage T2c, a pattern also observed in breast cancer tumourigenesis. We also identified another differentially expressed transcript, PTGFR, which has also been reported to be involved in prostate cancer progression and has also been linked to breast, ovarian and renal cancers. Conclusions: The results support the use of RNA-Seq along with machine learning techniques as an essential tool in identifying potential biomarkers for prostate cancer progression. Further studies elucidating the biochemical role of identified transcripts in vitro are crucial in validating the use of these biomarkers in the prediction of disease progression and development of effective therapeutic strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".