Non-coding RNAs predict recurrence-free survival of patients with hypoxic tumours
Bibliographic record
Abstract
Hypoxia promotes tumour aggressiveness and reduces patient survival. A spectrum of poor outcome among patients with hypoxic tumours suggests that additional factors modulate how tumours respond to hypoxia. PIWI-interacting RNAs (piRNAs) are small non-coding RNAs with a pivotal role in genomic stability and epigenetic regulation of gene expression. We reported that cancer type-specific piRNA signatures vary among patients. However, remarkably homogenous piRNA profiles are detected across patients with renal cell carcinoma, a cancer characterized by constitutive upregulation of hypoxia-related signaling induced by common mutation or loss of von Hippel-Lindau factor (VHL). By investigating >3000 piRNA transcriptomes in hypoxic and non-hypoxic tumors from seven organs, we discovered 40 hypoxia-regulated piRNAs and validated this in cells cultured under hypoxia. Moreover, a subset of these hypoxia-regulated piRNAs are regulated by VHL/HIF signaling in vitro. A hypoxia-regulated piRNA-based score (PiSco) was associated with poor RFS for hypoxic tumours, particularly Stage I lung adenocarcinomas, suggesting that hypoxia-regulated piRNA expression can predict tumour recurrence even in early-stage tumours and thus may be of clinical utility.
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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 | 0.000 | 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) | 0.002 | 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 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".