BJUI compass · 2025
Long non-coding RNAs define favourable biology in high-risk non-muscle-invasive bladder cancer.
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Weng Rachel, Phung Tran Anh Thu, Bell Robert, Dyrskjøt Lars, Gibb Ewan A
Аннотация
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: LncRNA expression patterns identified three distinct clusters of TaHG and T1 tumours (LC1, LC2, LC3). Of these, the LC1 subgroup (n = 47) had significantly better RFS (p = 0.04) and PFS (p = 0.002). The LC1 subgroup was characterized by downregulation of genes associated with proliferation (i.e., FOXM1, MKI67) 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.
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: LncRNA expression patterns identified three distinct clusters of TaHG and T1 tumours (LC1, LC2, LC3). Of these, the LC1 subgroup (n = 47) had significantly better RFS (p = 0.04) and PFS (p = 0.002). The LC1 subgroup was characterized by downregulation of genes associated with proliferation (i.e., FOXM1, MKI67) 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.