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Journal of Peking University (Health Sciences) ›› 2026, Vol. 58 ›› Issue (4): 794-802. doi: 10.19723/j.issn.1671-167X.2026.04.016

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Development of a 36-gene signature for predicting recurrence in non-muscle-invasive bladder cancer and validation in a Chinese cohort

Liyuan DONG, Yun PENG, Yuxuan SONG, Xiaoyang LI, Yiqing DU*(), Tao XU   

  1. Department of Urology, Peking University People's Hospital, Beijing 00044, China
  • Received:2026-03-02 Online:2026-08-18 Published:2026-05-13
  • Contact: Yiqing DU
  • Supported by:
    the Noncommunicable Chronic Diseases-National Science and Technology Major Project(2024ZD0525700); the National Natural Science Foundation of China(82471866); the National Natural Science Foundation of China(82472912); the Beijing Natural Science Foundation(L252190)

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Abstract:

Objective: To develop a transcriptomic-based recurrence prediction model for non-muscle-invasive bladder cancer (NMIBC), to comprehensively validate its efficacy in an independent Chinese cohort, and to explore the underlying biological mechanisms driving tumor recurrence. Methods: Transcriptome profiling data from the multicenter European Urothelial Cancer Molecular Classification Consortium (UROMOL) cohort (n=535) were used as the training cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression was employed to identify prognostic genes and construct a risk score model. Internal validation was strictly performed using Bootstrap resampling and 10-fold cross-validation. External validation was conducted in an independent Chinese cohort (n=85) from Peking University People ' s Hospital. Gene set enrichment analysis (GSEA) was applied to elucidate the distinct biological differences between high- and low-risk groups. Results: A 36-gene prognostic signature was identified from 12 170 genes to construct the risk score model. In the training cohort, the model achieved a concordance index (C-index) of 0.702 (95%CI: 0.664-0.740), with 1-, 3-, and 5-year areas under the receiver operating characteristic curve (AUC) of 0.782, 0.811, and 0.799, respectively. The 5-year recurrence-free survival (RFS) rates for the high- and low-risk groups were 35.2% and 72.8%, respectively (Log-rank P < 0.001). Internal validation via Bootstrap (C-index=0.702) and 10-fold cross-validation (C-index=0.708) demonstrated robust stability. In the independent Chinese validation cohort, the model maintained a favorable C-index of 0.685 (95%CI: 0.631-0.739), with 1-, 3-, and 5-year AUC of 0.705, 0.743, and 0.665, respectively. Its predictive performance was comparable to the traditional European Organization for Research and Treatment of Cancer (EORTC) risk score (C-index=0.652). GSEA revealed that the low-risk group was enriched in energy metabolism and protein homeostasis pathways, while the high-risk group was associated with extracellular matrix (ECM) remodeling and cell adhesion pathways. Conclusion: The 36-gene signature developed in this study demonstrates favorable predictive performance and stability across both the training and independent Chinese cohorts. While exhibiting comparable predictive efficacy to the traditional EORTC scoring system, the model serves as an independent prognostic factor that provides crucial supplementary molecular stratification. Furthermore, the model underscores the critical roles of energy metabolism and ECM remodeling in NMIBC recurrence, providing a valuable molecular basis for precise individualized management of NMIBC patients in China.

Key words: Non-muscle-invasive bladder cancer, Neoplasm recurrence, Prediction, Gene signatures, Cohort studies

CLC Number: 

  • R737.14

Table 1

Comparison of baseline clinical characteristics between the training and validation cohorts"

Variables Training cohort (n=535) Validation cohort (n=85) P
Age/years 69 (62.0-76.0) 67 (60.0-74.0) 0.065
Sex 0.541
  Male 414 (77.4) 66 (77.6)
  Female 121 (22.6) 19 (22.4)
T stage < 0.001
  CIS 3 (0.6) 1 (1.2)
  Ta 135 (25.2) 61 (71.8)
  T1 397 (74.2) 23 (27.1)
Tumor grade 0.881
  PUNLMP 0 (0) 11 (12.9)
  Low grade 320 (59.8) 34 (40.0)
  High grade 215 (40.2) 40 (47.1)
EORTC risk group 0.003
  Low risk 324 (60.6) 54 (63.5)
  Intermediate risk 0 (0) 31 (36.5)
  High risk 211 (39.4) 0 (0)

Figure 1

Construction and performance evaluation of the 36-gene signature A, forest plot of univariate Cox regression results for the 36 selected genes; B, RNA risk score distribution in the training cohort; C, comparison of risk scores between recurrent and non-recurrent patients; D, time-dependent ROC curves for 1-, 3-, and 5-year RFS prediction; E, Kaplan-Meier RFS curves for low-risk (green) and high-risk (red) groups based on the median cutoff. ROC, receiver operating characteristic; AUC, area under curve; RFS, recurrence-free survival; UROMOL, Urothelial Cancer Molecular Classification Consortium."

Figure 2

Internal validation and external validation of the model in an independent Chinese cohort A, Bootstrap distribution of concordance index (C-index) from 1 000 resamples; B, bar plot of C-index across 10-fold cross-validation; C, time-dependent ROC curves for 1-, 3-, and 5-year RFS prediction in the independent Chinese validation cohort; D, Kaplan-Meier RFS curves for low-risk (green) and high-risk (red) groups in the validation cohort. SD, standard deviation; Other abbreviations as in Figure 1."

Figure 3

Predictive efficacy in the 36-gene signature and EORTC score A, Kaplan-Meier RFS curves according to EORTC risk groups in the training cohort; B, Kaplan-Meier RFS curves according to EORTC risk groups in the validation cohort; C, time-dependent ROC curves for the EORTC score in the validation cohort; D, performance comparison across three models in the validation cohort. EORTC, European Organization for Research and Treatment of Cancer; C-index, concordance index; Other abbreviations as in Figure 1."

Figure 4

Biological pathways and mechanisms associated with the 36-gene signature A, GO enrichment analysis of the 36 signature genes (top 20 pathways ranked by adjusted P value shown); B, GSEA bubble plot in high-and low-risk patients; C, GSEA plot of the "Energy metabolism" pathway; D, GSEA plot of the "Protein homeostasis" pathway; E, GSEA plot of the "Adhesion and ECM remodeling" pathway. GO, gene ontology; NES, normalized enrichment score; GSEA, gene set enrichment analysis; ECM, extracellular matrix; BP, biological process; CC, cellular component; MF, molecular function."

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