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Background <p>Metabolic reprogramming is a hallmark of cancer. However, the precise mechanisms by which specific metabolic pathways drive prostate cancer (PCa) progression and shape the tumor microenvironment remain poorly defined.</p> Methods <p>A machine learning-derived Metabolic Dysfunction Signature (MODS) was developed and validated as a prognostic model. Its clinical relevance was established through multidimensional assessment, demonstrating significant associations with: (1) adverse clinical outcomes, (2) genomic instability, and (3) distinct therapeutic response profiles. Pathway enrichment and transcriptomic deconvolution (via CIBERSORT and ssGSEA) of TCGA and GEO cohorts were performed to characterize MODS-associated remodeling of the tumor microenvironment. Single-cell resolution analysis, utilizing the AUCell algorithm and CellChat ligand-receptor networking, was conducted to elucidate MODS-mediated epithelial-immune crosstalk, highlighting its role in establishing an immunosuppressive niche. Validation of HPRT1 expression and function using human protein atlas database and in vitro models. Furthermore, molecular docking simulations were employed to evaluate the potential targeting of HPRT1 by prevalent environmental compounds.</p> Results <p>The MODS prognostic model demonstrated robust predictive performance across three independent validation cohorts. MODS scores were positively correlated with adverse patient outcomes and advanced clinical stage. Notably, high-MODS tumors exhibited significantly greater genomic instability, reflected in a higher tumor mutational burden, and heightened resistance to conventional pharmacotherapies. Enrichment analysis revealed significant activation of proliferative signaling and oncogenic metabolic pathways in high-MODS tumors. Conversely, immune infiltration analysis indicated that high-MODS tumors were characterized by enhanced immunosuppressive features, including increased M2 macrophage infiltration. Single-cell analysis established that metabolically dysregulated epithelial cells, as defined by high MODS activity, functioned as central drivers of both poor prognosis and immunosuppressive microenvironment formation. Functional assays confirmed the oncogenic properties of HPRT1, promoting PCa cell proliferation and migration. Molecular docking simulations identified HPRT1 as a convergent molecular target for multiple environmental carcinogens implicated in PCa initiation.</p> Conclusion <p>This study defines a prevalent metabolic dysfunction signature in PCa that is integrally linked to patient prognosis, genomic instability, therapeutic resistance, and the establishment of an immunosuppressive tumor microenvironment. Furthermore, HPRT1 is functionally validated as a promoter of PCa progression and is positioned as a novel potential biomarker and therapeutic target for both intervention and prognosis assessment.</p>

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Integrative multi-omics analysis reveals metabolic dysfunction signatures as critical determinants of prostate cancer prognosis and immunosuppressive microenvironments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月3日

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  • Xiangqian Nie,
  • Zhenlin He,
  • Kun Wang,
  • Decai Zhu,
  • Wei Li,
  • Lei Zhang,
  • Ying Yu

摘要

Background

Metabolic reprogramming is a hallmark of cancer. However, the precise mechanisms by which specific metabolic pathways drive prostate cancer (PCa) progression and shape the tumor microenvironment remain poorly defined.

Methods

A machine learning-derived Metabolic Dysfunction Signature (MODS) was developed and validated as a prognostic model. Its clinical relevance was established through multidimensional assessment, demonstrating significant associations with: (1) adverse clinical outcomes, (2) genomic instability, and (3) distinct therapeutic response profiles. Pathway enrichment and transcriptomic deconvolution (via CIBERSORT and ssGSEA) of TCGA and GEO cohorts were performed to characterize MODS-associated remodeling of the tumor microenvironment. Single-cell resolution analysis, utilizing the AUCell algorithm and CellChat ligand-receptor networking, was conducted to elucidate MODS-mediated epithelial-immune crosstalk, highlighting its role in establishing an immunosuppressive niche. Validation of HPRT1 expression and function using human protein atlas database and in vitro models. Furthermore, molecular docking simulations were employed to evaluate the potential targeting of HPRT1 by prevalent environmental compounds.

Results

The MODS prognostic model demonstrated robust predictive performance across three independent validation cohorts. MODS scores were positively correlated with adverse patient outcomes and advanced clinical stage. Notably, high-MODS tumors exhibited significantly greater genomic instability, reflected in a higher tumor mutational burden, and heightened resistance to conventional pharmacotherapies. Enrichment analysis revealed significant activation of proliferative signaling and oncogenic metabolic pathways in high-MODS tumors. Conversely, immune infiltration analysis indicated that high-MODS tumors were characterized by enhanced immunosuppressive features, including increased M2 macrophage infiltration. Single-cell analysis established that metabolically dysregulated epithelial cells, as defined by high MODS activity, functioned as central drivers of both poor prognosis and immunosuppressive microenvironment formation. Functional assays confirmed the oncogenic properties of HPRT1, promoting PCa cell proliferation and migration. Molecular docking simulations identified HPRT1 as a convergent molecular target for multiple environmental carcinogens implicated in PCa initiation.

Conclusion

This study defines a prevalent metabolic dysfunction signature in PCa that is integrally linked to patient prognosis, genomic instability, therapeutic resistance, and the establishment of an immunosuppressive tumor microenvironment. Furthermore, HPRT1 is functionally validated as a promoter of PCa progression and is positioned as a novel potential biomarker and therapeutic target for both intervention and prognosis assessment.