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Background <p>Bladder cancer possesses the highest incidence rates globally. Integrative bioinformatics analyses are necessary to develop predictive biomarkers and potential therapeutic targets. Absorption, distribution, metabolism, and excretion (ADME), the disposition of pharmaceutical compounds in organisms, exert a crucial effect on tumorigenesis.</p> Methods and Results <p>Herein, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm identified a 6-ADME gene signature comprising <i>CYP1A1</i>, <i>CYP3A5</i>, <i>SLC15A2</i>, <i>SLCO1B3</i>, <i>TPMT</i>, and <i>UGT1A1</i>. Lower risk scores were significantly linked to higher disease-specific survival (DSS)/overall survival (OS) in patients with bladder cancer. The receiver operating characteristic (ROC) curve demonstrated that the risk score-based model exhibited a limited to moderate predictive performance in discriminating bladder cancer patients’ outcomes. Univariate and multivariate Cox risk regression analyses identified risk factors for bladder cancer patients’ prognosis: risk scores and grade. The nomogram model revealed that its prognostic outcome is reliable. In other words, the risk model could stratify patients with bladder cancer by different clinical characteristics. Moreover, among these 6 ADME key genes, <i>SLCO1B3</i> was strongly associated with disease-free survival in bladder cancer patients according to GSE32894; however, its specific roles in bladder cancer have not yet been fully elucidated. In vitro, <i>SLCO1B3</i> knockdown markedly inhibited cell proliferation, migration, and invasion in bladder cancer by regulating the Wnt/β-catenin signaling axis, which may correlate with its regulation of epithelial-mesenchymal transition (EMT)-related proteins.</p> Conclusions <p>A risk model based on 6 key ADME genes has been established using the LASSO algorithm and shows good prognostic potential for patients with bladder cancer. The risk score can also significantly distinguish bladder cancer samples across different clinical statuses, suggesting that the risk model may have diagnostic value for bladder cancer patients.</p>

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SLCO1B3 is associated with poor survival and promotes bladder cancer progression through Wnt/β-catenin signaling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Mingzhe Xu,
  • Xiaohang Song,
  • Kudusi,
  • Xiongbing Zu,
  • Minfeng Chen,
  • Wei He,
  • Lin Qi

摘要

Background

Bladder cancer possesses the highest incidence rates globally. Integrative bioinformatics analyses are necessary to develop predictive biomarkers and potential therapeutic targets. Absorption, distribution, metabolism, and excretion (ADME), the disposition of pharmaceutical compounds in organisms, exert a crucial effect on tumorigenesis.

Methods and Results

Herein, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm identified a 6-ADME gene signature comprising CYP1A1, CYP3A5, SLC15A2, SLCO1B3, TPMT, and UGT1A1. Lower risk scores were significantly linked to higher disease-specific survival (DSS)/overall survival (OS) in patients with bladder cancer. The receiver operating characteristic (ROC) curve demonstrated that the risk score-based model exhibited a limited to moderate predictive performance in discriminating bladder cancer patients’ outcomes. Univariate and multivariate Cox risk regression analyses identified risk factors for bladder cancer patients’ prognosis: risk scores and grade. The nomogram model revealed that its prognostic outcome is reliable. In other words, the risk model could stratify patients with bladder cancer by different clinical characteristics. Moreover, among these 6 ADME key genes, SLCO1B3 was strongly associated with disease-free survival in bladder cancer patients according to GSE32894; however, its specific roles in bladder cancer have not yet been fully elucidated. In vitro, SLCO1B3 knockdown markedly inhibited cell proliferation, migration, and invasion in bladder cancer by regulating the Wnt/β-catenin signaling axis, which may correlate with its regulation of epithelial-mesenchymal transition (EMT)-related proteins.

Conclusions

A risk model based on 6 key ADME genes has been established using the LASSO algorithm and shows good prognostic potential for patients with bladder cancer. The risk score can also significantly distinguish bladder cancer samples across different clinical statuses, suggesting that the risk model may have diagnostic value for bladder cancer patients.