Hypoxia related mRNA biomarker to predict biochemical failure and metastasis for prostate cancer.
Bibliographic record
Abstract
5 Background: Hypoxia is an important regulatory factor in tumorigenesis and is associated with a poor prognosis. Patients with high risk locally advanced disease account for 13-21% of prostate cancer cases and the ten year cancer specific survival rate for these patients is 62%. Patients with hypoxic tumours could benefit from hypoxia modifying therapeutics in addition to radiotherapy. Clinical companion biomarkers are needed to stratify patients who would benefit from hypoxia modifying therapy in addition to radiotherapy. Methods: RNA-seq analysis was performed on prostate cell lines (PNT2-C2, PC-3, LNCaP and DU145) exposed to 1% hypoxia for 24 hrs. A prostate cancer hypoxia gene signature was derived in silico using publicly available prostate gene expression data sets and the RNA-seq data. The biomarker was then independently validated in multiple cohorts of prostate cancer patients with localized diseases receiving prostatectomy alone, prostatectomy plus adjuvant radiotherapy, prostatectomy plus salvage radiotherapy, or definitive radiotherapy alone. Results: In vitro the hypoxia inducible expression of the hypoxia gene signature was tested at 1% and 0.1% oxygen of which 21 of the 28 genes were regulated by hypoxia. Patients stratified as high hypoxia were associated with significantly poorer 5-year biochemical recurrence free survival in patients undergoing prostatectomy alone, prostatectomy plus adjuvant radiotherapy and definitive radiotherapy alone. In multivariable analysis, the biomarker retained significance after correcting for confounding factors including Gleason group, PSA, a molecular classifier, etc. In another cohort of prostatectomy and salvage radiotherapy treated patients, the mRNA signature predicts metastasis free survival in both univariable and multivariable analyses. Conclusions: We derived a de novo mRNA signature based on hypoxia-regulated genes. The biomarker consistently predicts biochemical failure and metastasis for prostate cancer patients with localized disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".