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  9. Predictive And Prognostic Markers
  11. Advancing Bladder Cancer Management: Development Of A Prognostic Model And Personalized Therapy

Advancing bladder cancer management: development of a prognostic model and personalized therapy

Xiang Huang1, Guotu Du1, Ying Yang2

  • 1Department of Urology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.

Frontiers in Immunology
|August 6, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study developed a new prognostic model for bladder cancer (BLCA) using basal-squamous subtype-related genes (BSSRGs), improving patient stratification and personalized treatment strategies. The model accurately predicts outcomes and guides immune-based therapies for better bladder cancer management.

Area of Science:

  • Oncology
  • Genomics
  • Immunology

Background:

  • Bladder cancer (BLCA) presents a significant public health challenge due to high incidence and mortality.
  • Understanding molecular subtypes is crucial for improving BLCA treatment outcomes.
  • BLCA exhibits molecular heterogeneity that requires further characterization and application.

Purpose of the Study:

  • To map the molecular heterogeneity of bladder cancer.
  • To develop a robust prognostic model for BLCA using single-cell and bulk RNA sequencing data.
  • To investigate immunological characteristics and personalized treatment strategies through a risk score.

Main Methods:

  • Utilized single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data (GSE135337, TCGA-BLCA, etc.).
  • Identified molecular subtypes, focusing on the basal-squamous (Ba/Sq) subtype.
Keywords:
basal squamousbladder cancermolecular subtypespersonalized treatment

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  • Constructed and validated a prognostic model using LASSO and Cox regression on Ba/Sq subtype-related genes (BSSRGs).

    • Main Results:

    • Six molecular subtypes were identified, with the Ba/Sq subtype linked to poor prognosis.
    • The BSSRGs-based prognostic model demonstrated strong predictive performance (AUC values) across datasets.
    • Distinct immune infiltration, tumor mutation burden (TMB), and immune dysfunction/exclusion (TIDE) scores were observed between high- and low-risk groups.
    • Significant differences in potential systemic drug response rates were found between risk groups.

    Conclusions:

    • Introduced and validated a novel prognostic model for BLCA based on BSSRGs.
    • The risk score facilitates patient stratification and immune profiling for personalized therapy.
    • This approach offers advancements in BLCA management through molecular and immunological insights.
    prognostic model
    single-cell RNA sequencing