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Integrated multi-omics profiling of immune microenvironment and drug resistance signatures for precision prognosis in prostate cancer

  • 0Department of Urology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan, China.
Cancer Drug Resistance (alhambra, Calif.) +

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August 22, 2025

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August 22, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a new prognostic model for prostate cancer (PCa) by analyzing tumor microenvironment (TME) data. The model identifies high-risk PCa patients with specific genetic mutations and predicts their response to different therapies.

Area Of Science

  • Oncology
  • Immunology
  • Genomics

Background

  • Prostate cancer (PCa) remains a leading cause of cancer mortality in men.
  • Treatment resistance in PCa is significantly influenced by the complex tumor microenvironment (TME).

Purpose Of The Study

  • To develop an immune-centric prognostic model for PCa.
  • To correlate TME dynamics, genomic instability, and drug resistance heterogeneity.

Main Methods

  • Integrated multi-omics data from TCGA and GEO databases (554 PCa samples).
  • Assessed immune cell infiltration using CIBERSORT and ESTIMATE.
  • Employed WGCNA to identify immune-related modules.
  • Utilized single-cell RNA sequencing (ScRNA-seq) to uncover resistance patterns.
  • Constructed and validated a 10-gene prognostic model using LASSO regression.

Main Results

  • Identified two immune subtypes: high-risk subgroups showed TP53 mutations, increased tumor mutation burden (TMB), and enriched energy metabolism.
  • ScRNA-seq revealed PCa cell clusters with high-risk subtypes sensitive to bendamustine/dacomitinib and resistant to apalutamide/neratinib.
  • The 10-gene model accurately categorized patients into high/low-risk groups with distinct survival outcomes (log-rank P < 0.0001) and high predictive accuracy (AUC: 0.854-0.889) in validation datasets.

Conclusions

  • Established a TME-driven prognostic framework connecting immune heterogeneity, genomic instability, and therapeutic resistance in PCa.
  • Identified metabolic dependencies and subtype-specific vulnerabilities for targeted therapies.
  • Findings offer strategies to overcome treatment failure by targeting energy metabolism or tailoring therapies based on resistance signatures.

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