Long non‐coding RNAs define favourable biology in high‐risk non‐muscle‐invasive bladder cancer
Bibliographic record
Abstract
Background: To evaluate whether long non-coding RNA (lncRNA) expression patterns can improve molecular stratification and outcome prediction in high-risk non-muscle-invasive bladder cancer (NMIBC). Methods: RNA sequencing data from high-grade Ta (TaHG) and T1 (n = 212) tumours from the UROMOL consortium (Lindskrog et al., Nature Communications 2021) were analysed. Unsupervised consensus clustering based on lncRNA expression patterns identified distinct patient subgroups, which were characterized using gene expression patterns and gene signatures. A single-sample classifier was trained using elastic net logistic regression on UROMOL lncRNA expression profiles and applied to the Knowles cohort for independent validation. Recurrence-free survival (RFS) and progression-free survival (PFS) were evaluated using Kaplan-Meier (KM) plots, univariate and multivariate analyses. Results: ) and lower G2M and E2F gene signatures, suggesting reduced rates of tumour growth. A transcriptomic classifier trained on UROMOL lncRNA profiles successfully stratified recurrence risk in an independent validation cohort (Knowles, n = 120), where predicted high-risk cases (LC2/3) demonstrated significantly poorer recurrence-free survival (p < 0.001). While these findings highlight lncRNA expression as a potential stratification tool, limitations include the retrospective design, treatment heterogeneity and the need for external validation. Conclusion: LncRNA-based clustering demonstrates significant potential for improving patient stratification in high-risk NMIBC, identifying less aggressive tumours in an otherwise high-risk setting. A transcriptomic classifier trained on these findings was successfully validated in an independent cohort, supporting its potential clinical utility in refining risk assessment and guiding treatment decisions. Prospective studies are needed to further validate and refine this approach.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".