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<p>Molecular characterization of non-muscle-invasive bladder cancer (NMIBC) provides critical insights into the heterogeneity of clinical outcomes and treatment responses. The integration of existing transcriptomic subtyping systems to reconcile inconsistencies would facilitate clinical translation. The Markov cluster algorithm was utilized to construct a network integrating five published bladder cancer classification systems based on transcriptional profiles, aiming to establish an integrated molecular classification framework and identify different prognostics clusters. Four integrated molecular clusters (IMC1-4) with distinct transcriptomic, genomic, proteomic and clinical characteristics were identified. IMC2 exhibited significant enrichment of <i>FGFR3</i> mutations, whereas IMC4 showed predominant <i>TP53</i> alterations. IMC1 and IMC3 demonstrated improved PFS following Bacillus Calmette–Guérin (BCG) instillation in patients recommended for BCG treatment per the European Association of Urology (EAU) guidelines. Using transcriptomic profiles, a single-sample classifier was developed to categorize tumors into integrated molecular clusters. This study provides an integrated classification system to understand the heterogeneity of NMIBC and establish a preclinical framework for clinical management, therapeutic targets, and monitoring of clinical trials.</p>

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An integrated molecular classification system identifies distinct prognostic clusters of non-muscle-invasive bladder cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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  • Kezhi Liu,
  • Zhenhua Chen,
  • Xiaohan Jin,
  • Hui Liang,
  • Haihua Liu,
  • Jinwei Chen,
  • Chengpeng Gui,
  • Yuhang Chen,
  • Ziran Dai,
  • Hao Zhou,
  • Zheyu Ai,
  • Zhu Wang,
  • Qiong Deng,
  • Jieyan Wang,
  • Meiyu Jin,
  • Junhang Luo,
  • Wei Chen,
  • Zihao Feng

摘要

Molecular characterization of non-muscle-invasive bladder cancer (NMIBC) provides critical insights into the heterogeneity of clinical outcomes and treatment responses. The integration of existing transcriptomic subtyping systems to reconcile inconsistencies would facilitate clinical translation. The Markov cluster algorithm was utilized to construct a network integrating five published bladder cancer classification systems based on transcriptional profiles, aiming to establish an integrated molecular classification framework and identify different prognostics clusters. Four integrated molecular clusters (IMC1-4) with distinct transcriptomic, genomic, proteomic and clinical characteristics were identified. IMC2 exhibited significant enrichment of FGFR3 mutations, whereas IMC4 showed predominant TP53 alterations. IMC1 and IMC3 demonstrated improved PFS following Bacillus Calmette–Guérin (BCG) instillation in patients recommended for BCG treatment per the European Association of Urology (EAU) guidelines. Using transcriptomic profiles, a single-sample classifier was developed to categorize tumors into integrated molecular clusters. This study provides an integrated classification system to understand the heterogeneity of NMIBC and establish a preclinical framework for clinical management, therapeutic targets, and monitoring of clinical trials.