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Background <p>Metabolic reprogramming and immune landscape remodeling are hallmarks of prostate cancer (PCa) progression and therapy resistance. However, the interplay between tumor metabolism, immune infiltration, and prognosis remains poorly characterized.</p> Methods <p>We obtained transcriptomic and clinical data of PCa patients from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to assess metabolic pathway activity and define metabolic subtypes. Immune infiltration was evaluated using multiple algorithms, including CIBERSORT and xCell. Prognostic genes were identified through univariate Cox and LASSO regression analyses, and a metabolic risk model was constructed and validated. Functional enrichment, immune checkpoint expression, and clinical associations were further analyzed. A nomogram was developed by integrating clinical features and risk scores.</p> Results <p>Two distinct metabolic subtypes—Metabolism_H and Metabolism_L—were identified, exhibiting differential metabolic activity, immune infiltration, and clinical outcomes. The Metabolism_H group showed upregulation of lipid and amino acid metabolism pathways and was associated with an immunosuppressive microenvironment and worse prognosis. A robust metabolic risk score derived from 14 prognostic genes significantly stratified patients by overall survival (p &lt; 0.001). The risk score positively correlated with PD-L1 expression and immune exclusion features. The integrated nomogram demonstrated strong predictive power for 1-, 3-, and 5-year survival (AUC &gt; 0.74) and good calibration.</p> Conclusion <p>Our findings highlight the metabolic and immunological heterogeneity of prostate cancer and provide a novel metabolism-based prognostic model. Targeting tumor metabolism may enhance immune responses and improve risk stratification and therapeutic outcomes in PCa patients.</p>

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Development of a metabolism-associated prognostic risk model based on immune landscape stratification in prostate cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Yuanting Liu,
  • Yixiao Shi,
  • Yujie Cao,
  • Yuchun Zhu

摘要

Background

Metabolic reprogramming and immune landscape remodeling are hallmarks of prostate cancer (PCa) progression and therapy resistance. However, the interplay between tumor metabolism, immune infiltration, and prognosis remains poorly characterized.

Methods

We obtained transcriptomic and clinical data of PCa patients from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to assess metabolic pathway activity and define metabolic subtypes. Immune infiltration was evaluated using multiple algorithms, including CIBERSORT and xCell. Prognostic genes were identified through univariate Cox and LASSO regression analyses, and a metabolic risk model was constructed and validated. Functional enrichment, immune checkpoint expression, and clinical associations were further analyzed. A nomogram was developed by integrating clinical features and risk scores.

Results

Two distinct metabolic subtypes—Metabolism_H and Metabolism_L—were identified, exhibiting differential metabolic activity, immune infiltration, and clinical outcomes. The Metabolism_H group showed upregulation of lipid and amino acid metabolism pathways and was associated with an immunosuppressive microenvironment and worse prognosis. A robust metabolic risk score derived from 14 prognostic genes significantly stratified patients by overall survival (p < 0.001). The risk score positively correlated with PD-L1 expression and immune exclusion features. The integrated nomogram demonstrated strong predictive power for 1-, 3-, and 5-year survival (AUC > 0.74) and good calibration.

Conclusion

Our findings highlight the metabolic and immunological heterogeneity of prostate cancer and provide a novel metabolism-based prognostic model. Targeting tumor metabolism may enhance immune responses and improve risk stratification and therapeutic outcomes in PCa patients.