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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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A Long Noncoding RNA-based Classifier for Identifying More Aggressive Low-grade Ta Bladder Cancer with Elevated FGFR3

Tran Anh Thu Phung1, Rachel Weng1, Peter C Black1

  • 1Vancouver Prostate Centre, M. H. Mohseni Institute of Urologic Sciences, Vancouver, BC, Canada; Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.

European Urology Oncology
|February 28, 2026
PubMed
Summary
This summary is machine-generated.

A new classifier identifies aggressive low-grade, stage Ta bladder cancer (TaLG NMIBC) by analyzing long noncoding RNA cluster 2 (LC2). This tool aids in predicting recurrence risk for non-muscle-invasive bladder cancer patients.

Keywords:
BiomarkersGene expression profilingLong noncoding RNANon–muscle-invasive bladder cancerSingle-sample classifierTa bladder cancer

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Area of Science:

  • Oncology
  • Genomics
  • Molecular Biology

Background:

  • Non-muscle-invasive bladder cancer (NMIBC) classification requires improved risk stratification.
  • Low-grade, stage Ta (TaLG) NMIBC can exhibit aggressive behavior, leading to recurrence or progression.
  • Previous research identified long noncoding RNA cluster 2 (LC2) as a marker of aggressive TaLG NMIBC.

Purpose of the Study:

  • To develop and validate a single-sample transcriptome classifier for identifying the aggressive LC2 subgroup in TaLG NMIBC.
  • To assess the prognostic significance of the LC2 classifier in predicting recurrence-free survival.
  • To understand the biological pathways associated with the LC2 subgroup.

Main Methods:

  • Utilized the UROMOL cohort (n=276) for training and the Hurst cohort (n=72) for validation.
  • Employed feature selection via median absolute deviation and nested cross-validation.
  • Trained an elastic net regression model with ten-fold cross-validation and evaluated performance using pathway analysis, Kaplan-Meier, and Cox regression.

Main Results:

  • The classifier successfully identified LC2 tumors, with 7/72 cases detected in the Hurst cohort.
  • LC2-TaLG patients demonstrated significantly worse recurrence-free survival (log-rank p < 0.001).
  • LC2 status was a strong predictor of recurrence (HR 4.52, p=0.001), and LC2-predicted tumors showed consistent biological patterns.

Conclusions:

  • A reproducible single-sample transcriptomic classifier for aggressive TaLG NMIBC has been developed.
  • The classifier demonstrates potential for identifying patients at higher risk of recurrence.
  • Further validation could support clinical decision-making for NMIBC management.