bims-meproc Biomed News
on Metabolism in Prostate Cancer
Issue of 2026–05–31
seven papers selected by
Grigor Varuzhanyan, UCLA
  1. Integrated Phenotypic and Transcriptomic Profiling Positions ONC212 as a Lead Imipridone in Androgen-Independent Prostate Cancer Models.
  2. Lipid Metabolic Rewiring During Continuous Prostate Cancer Progression Defines a Biologically Relevant Four-Gene Prognostic Signature.
  3. Ferroptosis vulnerability of enzalutamide resistant prostate cancer conferred by ACSL4 overexpression and GPX4 antagonism.
  4. Metabolic Plasticity and Enzalutamide Resistance: Emerging Roles of PGK1 in Prostate Cancer.
  5. Deciphering the Heterogeneity of Cancer-Associated Fibroblasts in Prostate Cancer: From Stromal Biology to Clinical Translation.
  6. Ketogenic and Low-Carbohydrate Diets in Prostate Cancer: Metabolic Rationale, Preclinical Evidence, and Preliminary Clinical Data.
  7. Integrative Machine Learning Framework Revealing TRPM4-Associated Signatures and Identifying SPATA6 as a Potential Biomarker in Prostate Cancer.
  1. Int J Mol Sci. 2026 May 20. pii: 4597. [Epub ahead of print]27(10):
    Integrated Phenotypic and Transcriptomic Profiling Positions ONC212 as a Lead Imipridone in Androgen-Independent Prostate Cancer Models.
    Fatima Ghamlouche, Amani Yehya, Abdallah Kurdi, Sana Hachem, Varun V Prabhu, Georges Daoud, Wassim Abou-Kheir.
      Prostate cancer (PCa) remains lethal at advanced stages, partly due to stem-like subpopulations known as prostate cancer stem cells (PCSCs) that sustain tumor growth and therapeutic resistance. Imipridones are small-molecule anticancer agents, with next-generation derivatives ONC206 and ONC212 designed for enhanced potency and broader activity. This study compared their antitumor efficacy and mechanisms in advanced androgen-independent PCa (AIPC) models, namely DU145 and PC3 cells, using two- and three-dimensional systems encompassing bulk cancer cells and PCSCs. DU145 and PC3 AIPC cells were treated with ONC201 (parent compound), ONC206, or ONC212. Functional assays assessed proliferation, viability, migration, invasion, PCa spheroids formation, cell cycle distribution, and mitochondrial membrane potential and mass, while RNA sequencing defined transcriptional responses. ONC212 was the most potent derivative, inhibiting proliferation and migration and abolishing PCa spheroids at nanomolar doses, whereas ONC201 and ONC206 required higher concentrations. Transcriptomic analyses revealed shared repression of DNA replication and cell-cycle transition programs, with activation of integrated stress and unfolded protein responses (ISR/UPR) and FOXO signaling. ONC206 favored PERK-ATF4-mediated apoptosis with reduced DNA repair, while ONC212 more strongly impacted oxidative phosphorylation-related pathways and mitochondrial RNA processing. Imipridones induced a time-dependent cell-cycle redistribution with increased sub-G1 accumulation and modulated mitochondrial membrane potential and mass in a context-dependent manner. Collectively, these findings position ONC212 as a leading imipridone candidate in AIPC models, combining potent inhibition of tumor and stem-like cell functions with a coherent stress-response signature that supports further translational evaluation.
    Keywords:  3D culture; ONC206; ONC212; imipridones; integrated stress response; prostate cancer; prostate cancer stem cells; transcriptomics; unfolded protein response
    DOI:  https://doi.org/10.3390/ijms27104597
  2. Biol Proced Online. 2026 May 25.
    Lipid Metabolic Rewiring During Continuous Prostate Cancer Progression Defines a Biologically Relevant Four-Gene Prognostic Signature.
    Rongna Li, Hongying He, Hui Sun, Yili Long, Nan Huang, Yingbing Zhu, Hongtao Chen, Guanmin Jiang, Xiaohui Chen.
       BACKGROUND: Lipid metabolic reprogramming is increasingly recognized as a critical feature of prostate cancer progression, but the lipid metabolism-related genes that remain continuously dysregulated from normal tissue to primary tumor and metastatic disease have not been systematically characterized, and their biological and prognostic relevance remains incompletely understood.
    OBJECTIVE: To identify lipid metabolism-related genes associated with continuous prostate cancer progression and develop a prognostic signature for survival stratification.
    METHODS: Clinical prostate cancer specimens and a high-fat diet (HFD)-driven RM-1 tumor model were first used to evaluate lipid metabolic alterations in vivo. GSE6919 transcriptomic data were used to identify genes shared between the Normal-Primary and Primary-Metastatic transitions. These genes were intersected with a curated lipid metabolism-related gene set, followed by GO and KEGG enrichment analyses. TCGA prostate adenocarcinoma expression and clinical data were used for LASSO regression to construct a prognostic model. The four core genes were further evaluated by clinicopathological correlation analysis, protein- and transcript-level validation in clinical tissues and prostate cancer cell lines, and functional assays under oleic acid-induced lipid stress. Immune infiltration analysis, ssGSEA, and nomogram analysis were performed to assess the biological and clinical relevance of the model.
    RESULTS: Clinical tissues showed increased PLIN3 expression, and HFD feeding promoted tumor growth and reinforced lipid metabolic alterations in vivo. A total of 44 lipid metabolism-related genes were identified as continuously dysregulated during prostate cancer progression. These genes were mainly enriched in fatty acid metabolism, lipid catabolism, peroxisome, lipid droplet, glycolysis/gluconeogenesis, arachidonic acid metabolism, and PPAR signaling. Eight genes were significantly associated with overall survival in TCGA, and a four-gene signature comprising ALDH3A2, ENO2, PPP1CB, and PTGIS was established. This model effectively stratified patients into high- and low-risk groups with significantly different survival outcomes. The risk score was positively associated with clinical T stage and Gleason score. The four core genes were also associated with lipid metabolic enzymes, immune infiltration patterns, and multiple metabolism-related pathways. Protein- and transcript-level validation in clinical tissues and prostate cancer cell lines supported the biological relevance of the signature, although PTGIS showed a more context-dependent pattern. Functionally, silencing ENO2 reduced oleic acid-induced lipid peroxidation, whereas silencing PPP1CB enhanced it, while ALDH3A2 showed a more context-dependent effect. A nomogram integrating the risk score with clinical variables improved individualized survival prediction.
    CONCLUSION: We identified lipid metabolism-related genes continuously dysregulated during prostate cancer progression and established a four-gene prognostic signature with potential value for survival prediction and risk assessment. These findings highlight lipid metabolic rewiring as an important component of prostate cancer evolution and provide candidate biomarkers for future mechanistic and translational studies.
    Keywords:  Biomarker; Lipid metabolism; Prognosis; Progression; Prostate cancer
    DOI:  https://doi.org/10.1186/s12575-026-00345-1
  3. Cell Death Dis. 2026 May 29.
    Ferroptosis vulnerability of enzalutamide resistant prostate cancer conferred by ACSL4 overexpression and GPX4 antagonism.
    Yi Zhou, Jiapeng He, Haozhe Zhang, Qiqi Wang, Zhaojun Yu, Yifan Zhang, Sangsang Li, Yitong Chen, Weiwei Zhou, Qianyu Xu, Yu Yin, Qiang Wei, Chris Soon Heng Tan, Ju Guo, Bin Fu, Hailiang Hu.
      Enzalutamide, as a second-generation anti-androgen agent, has been used to treat castration-resistant prostate cancer (CRPC) or metastatic castration-sensitive prostate cancer (mCSPC). However, enzalutamide resistance inevitably developed for most treated CRPC/mCSPC, and limited effective therapies are currently available for these enzalutamide-resistant prostate cancers. In this study, we utilize our established enzalutamide-resistant prostate cancer cell lines to reveal a vulnerability of these cancer cells to GPX4-targeted ferroptosis. Interestingly, the established enzalutamide-resistant prostate cancer cells are mixed populations that predominantly exhibit stem cell-like (SCL) and neuroendocrine-like (NEL) phenotypes and may reflect cellular heterogeneity during the development of enzalutamide resistance in prostate cancer. We further demonstrated that ACSL4, a long-chain fatty acid-CoA ligase, was upregulated by the JAK/STAT pathway in enzalutamide-resistant SCL/NEL cells, thereby facilitating tumor proliferation and metastasis while increasing sensitivity to ferroptosis. To antagonize the ACSL4-conferred ferroptosis risk, SCL/NEL cells upregulated GPX4 through AP-1 transcription complex to suppress ferroptosis and thus promoted the malignant progression of SCL/NEL cells. Notably, we characterized Auranofin, an anti-rheumatoid arthritis drug, as a ferroptosis inducer for these SCL/NEL cells in vitro and in vivo by targeting AP-1 and decreasing GPX4 expression, suggesting a new application for Auranofin in treating enzalutamide-resistant stem cell-like AP-1High CRPC.
    DOI:  https://doi.org/10.1038/s41419-026-08906-8
  4. Endocrinology. 2026 May 24. pii: bqag063. [Epub ahead of print]
    Metabolic Plasticity and Enzalutamide Resistance: Emerging Roles of PGK1 in Prostate Cancer.
    Min Zhang, Xiaoqi Liu.
      Enzalutamide resistance remains a key challenge in the treatment of advanced prostate cancer (PCa), as clinical efficacy is frequently hindered by acquired resistance. While reactivation of AR signaling including AR splice variants, has been extensively studied, accumulating evidence indicates that metabolic reprogramming is a key adaptive response to sustained AR inhibition. As resistance advances, tumor cells exhibit metabolic plasticity, dynamically regulating glycolysis, mitochondrial respiration, lipid metabolism, and redox homeostasis to adapt to treatment pressure. In this context, phosphoglycerate kinase 1 (PGK1), a key glycolytic enzyme, has emerged as a potential mediator linking metabolic adaptation to therapy resistance. Beyond its ATP-generating function, PGK1 exhibits non-canonical functions, including regulation of mitochondrial metabolism, protein kinase activity, and stress-response signaling. These properties suggest that PGK1 may integrate AR-driven metabolic programs with downstream survival pathways under therapeutic pressure. Preclinical studies support a role for PGK1 in promoting glycolytic phenotypes and resistance-associated metabolic states. However, current evidence remains largely associative, and direct in vivo validation in prostate-specific models is limited. Moreover, whether PGK1 functions as a causal driver or downstream effector of resistance remains unresolved. This review summarizes current understanding of AR-metabolism coupling and evaluates PGK1 as a potential metabolic-signaling node in enzalutamide resistance, highlighting key knowledge gaps and future directions for metabolic targeting in PCa.
    Keywords:  AR signaling; CRPC; Cancer metabolism; Enzalutamide resistance; PGK1
    DOI:  https://doi.org/10.1210/endocr/bqag063
  5. Cancers (Basel). 2026 May 14. pii: 1600. [Epub ahead of print]18(10):
    Deciphering the Heterogeneity of Cancer-Associated Fibroblasts in Prostate Cancer: From Stromal Biology to Clinical Translation.
    Ho Trong Tan Truong, Whi-An Kwon, Hyeong Jung Woo, Minseok S Kim, Nhu Quang Tran, Jae Young Joung.
      Prostate cancer (PCa) progression and treatment resistance are driven by tumor-intrinsic mechanisms and adaptive remodeling of the tumor microenvironment, in which cancer-associated fibroblasts (CAFs) play a crucial role. Although CAF biology is increasingly recognized, a major translational gap remains: CAFs are highly heterogeneous, and comprise distinct functional states with divergent effects on disease progression, immune regulation, and therapeutic resistance. To bridge this gap, we synthesize evidence from single-cell and spatial transcriptomic studies, tissue-based pathology, liquid biopsy assays, and molecular imaging to construct an evidence-tiered, decision-oriented translational framework that connects stromal mechanisms, translational measurement strategies, and therapeutic interventions in PCa. Single-cell and spatial transcriptomic analyses have consistently identified multiple CAF programs, including matrix-remodeling, inflammatory, immunoregulatory, antigen-presenting, and therapy-imprinted states, each with distinct functional outputs and clinical correlates. Tissue-based readouts, including reactive stromal grade (RSG) and fibroblast activation protein (FAP) immunohistochemistry, provide practical proxies for stromal activation and correlate with disease-specific mortality and imaging phenotypes. Circulating CAFs (cCAFs) represent an emerging liquid biopsy modality for longitudinal stromal monitoring, although technical standardization is required before clinical implementation. FAP-targeted PET imaging and emerging dual prostate-specific membrane antigen (PSMA)/FAP-targeted theranostic strategies provide noninvasive tools for patient selection and response assessment, particularly in PSMA-discordant or tracer-heterogeneous disease. Androgen receptor (AR)-targeted therapy can reprogram stromal states toward resistance-promoting circuits, highlighting the dynamic and plastic nature of the CAF compartment. A state-based CAF framework organizes stromal biology into testable translational hypotheses rather than immediate clinical standards. RSG and FAP-based tissue or imaging readouts are practical markers of stromal activation, whereas spatial CAF-immune signatures and cCAF assays remain investigational and require assay harmonization and prospective validation. Future trials should pre-specify stromal biomarkers as enrichment or pharmacodynamic variables when matched to the intervention and should avoid treating CAFs as a uniform therapeutic target.
    Keywords:  cancer-associated fibroblasts; castration-resistant prostate cancer; fibroblast activation protein; liquid biopsy; prostate cancer; single-cell analysis; spatial transcriptomics; tumor microenvironment
    DOI:  https://doi.org/10.3390/cancers18101600
  6. J Clin Med. 2026 May 20. pii: 3946. [Epub ahead of print]15(10):
    Ketogenic and Low-Carbohydrate Diets in Prostate Cancer: Metabolic Rationale, Preclinical Evidence, and Preliminary Clinical Data.
    Silvia Manfrini, Andrea Malgeri, Carmine Mone, Ludovica Di Francesco, Giulia Pecora, Rossella Mazzilli, Giuseppe Defeudis, Manon Yeganeh Khazrai, Antongiulio Faggiano.
      Background: Prostate cancer (PCa) is the most commonly diagnosed malignancy in men and a leading cause of cancer-related mortality worldwide. Growing evidence indicates that metabolic syndrome components, including obesity, insulin resistance, and hyperglycemia, contribute to PCa development, and progression to more aggressive form. At the same time, standard treatments such as androgen deprivation therapy (ADT) and androgen receptor pathway inhibitors (ARPIs) significantly improve oncologic outcomes but are associated with adverse metabolic effects, including increased fat mass, insulin resistance, and sarcopenia, potentially worsening patients' overall metabolic profile and quality of life. Tumor progression in PCa is strongly driven by androgen receptor (AR) signaling, which is closely linked to cellular metabolic reprogramming, highlighting metabolism as a potential therapeutic target. Aim: The aim of this study was to evaluate and synthesize current evidence on the role of the ketogenic diet (KD) in PCa, with particular emphasis on its interaction with hormonal therapies, underlying metabolic and endocrine mechanisms, and its potential application as an adjunctive strategy in integrated oncologic care. Results: The KD, characterized by high fat and very low carbohydrate intake, induces a metabolic state of ketosis that reduces circulating glucose, insulin, and insulin-like growth factor 1 (IGF-1), potentially counteracting metabolic alterations associated with PCa and its treatments. Preclinical studies consistently demonstrate that carbohydrate restriction and KD can slow tumor growth, modulate key oncogenic pathways such as PI3K/AKT/mTOR, reduce systemic insulin signaling, and enhance survival in prostate cancer models. Additionally, emerging evidence suggests possible synergistic effects when KD is combined with standard therapies, including ADT and immunotherapy. Clinical data, although limited, indicate that low-carbohydrate dietary interventions may improve metabolic parameters and could delay biochemical progression, as suggested by increased prostate-specific antigen (PSA) doubling time. However, results across studies remain heterogeneous, and robust evidence on long-term oncologic outcomes is lacking. Conclusions: Overall, the KD represents a promising but still experimental strategy in PCa management, requiring careful nutritional supervision to avoid adverse effects such as unintended weight loss or sarcopenia. Further well-designed randomized clinical trials are needed to clarify its safety, efficacy, and role in routine clinical practice.
    Keywords:  androgens; dietary patterns; ketogenic diet; low-carbohydrate diets; prostate cancer
    DOI:  https://doi.org/10.3390/jcm15103946
  7. J Cancer. 2026 ;17(5): 1002-1017
    Integrative Machine Learning Framework Revealing TRPM4-Associated Signatures and Identifying SPATA6 as a Potential Biomarker in Prostate Cancer.
    Hang Zhou, Wangli Mei, Jichen Wang, Xiang Liu.
       Background: The non-selective cation channel TRPM4 can induce necrotic cell death through sodium overload, yet its role in prostate cancer (PCa) progression remains poorly characterized.
    Materials and methods: Using TCGA-PCa transcriptomic data centered on TRPM4, we identified transcriptional signatures linked to sodium overload. Leveraging prognostic features, we developed robust prognostic models via ten machine learning algorithms and their combinations, training on TCGA data and validating on internal validation set, GSE46602 and GSE116918. We assessed the model's associations with clinicopathological features, prognosis, immune infiltration, and drug response. Expression of the 10 key model genes was validated in PCa cell lines versus a normal prostate epithelial cell. For SPATA6-the top-contributing gene-we overexpressed it in PCa cells to assess its functional impact.
    Results: We identified 91 overlapping genes from TRPM4-associated and PCa-related differentially expressed genes. Functional enrichment implicated these genes in small GTPase activity, Rap1 signaling, and cAMP signaling. A TRPM4-related signature model (TRSM) comprising 10 key genes demonstrated strong prognostic performance across training and validation cohorts. TRSM-based risk stratification revealed significant differences in disease-free survival, clinicopathological features, immune infiltration, and immunotherapy response. Drug sensitivity analysis indicated heightened docetaxel sensitivity in the high-risk group. In vitro assays confirmed downregulation of all 10 key genes in PCa. SPATA6 overexpression suppressed PCa cell proliferation and migration.
    Conclusion: Our findings underscore the importance of TRPM4-associated molecular features in PCa prognosis. TRSM shows potential as a predictive tool for patient outcomes and a guide for personalized therapy.
    Keywords:  Machine Learning; Prostate cancer; SPATA6; TRPM4-realted signatures model; sodium overload
    DOI:  https://doi.org/10.7150/jca.129356
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