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Machine learning

New Personalized Medicine Study Results from Wenzhou Medical University Described

Strategic Insights

— 1 min read

Researchers at Wenzhou Medical University, in collaboration with several institutions, have published a new study on personalized medicine, focusing on immunogenic cell death (ICD) and its impact on bladder cancer prognosis and treatment. The study, published in ACS Omega, identified 32 key ICD-risk genes (IRGs) and constructed an ICD-based prognostic signature that stratifies bladder cancer patients into high- and low-risk groups. The study also identified SLC2A3 as a hub gene and a potential therapeutic target for precision therapy in combination with immunotherapy.

Key Takeaways:

  • The study established a risk signature based on ICD and identified ICD-related genes as diagnostic markers and therapeutic targets for bladder cancer.
  • Thirty-two key ICD-risk genes (IRGs) were screened from correlation and univariate Cox regression analyses.
  • The ICD-based prognostic signature constructed using IRGs indicated significant differences in the tumor microenvironment (TME) and treatment response between different risk groups.
  • SLC2A3 was identified as the most critical IRG exhibiting high expression in the fibroblast population of patients with bladder cancer, especially in cancer-associated fibroblasts (CAFs).
  • Inhibiting SLC2A3 expression may enhance the effectiveness of immunotherapy and identified potential drugs targeting SLC2A3.
  • The study demonstrated that the identified IRGs serve as risk factors for clinical prognosis in bladder cancer and successfully constructed an ICD-based prognostic signature.
  • SLC2A3 holds potential as a therapeutic target to advance precision and personalized treatment strategies for bladder cancer in combination with immunotherapy.

Statistics:

  • 32 key ICD-risk genes (IRGs) were screened from correlation and univariate Cox regression analyses.
  • 101 combinatorial machine learning algorithms and 10 individual machine learning algorithms identified SLC2A3 as a hub gene.
  • The study stratified bladder cancer patients into high- and low-risk groups based on the ICD-based prognostic signature.
  • SLC2A3 was identified as the most critical IRG exhibiting high expression in the fibroblast population of patients with bladder cancer.

Sources:

  • NewsRx. New Personalized Medicine Study Results from Wenzhou Medical University Described (Integrated Multiomics Analysis and Machine Learning Approaches In Bladder Cancer: Unveiling the Impact of Immunogenic Cell Death and Its Key Gene Slc2a3 On ...). Immunotherapy Weekly. July 2, 2025; p 2902.
  • ACS Omega. "Integrated Multiomics Analysis and Machine Learning Approaches In Bladder Cancer: Unveiling the Impact of Immunogenic Cell Death and Its Key Gene Slc2a3 On Prognosis and Personalized Treatment Strategies." Amer Chemical Soc, 1155 16TH St, NW, Washington, DC 20036, USA.

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