ArticleScientific reports2020
Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed, 85 citations in OpenAlex.
- Advances in spatial omics for the analysis of prostate cancer.Analytical and bioanalytical chemistry · 2026Review
- Dysregulation of the urea cycle - potential targets for treatment and diagnosis in prostate cancer.Expert reviews in molecular medicine · 2026Review
- Co-targeting EGFR and SUCLG2 disrupts a nuclear transcriptional program driving neuroendocrine differentiation and TKI resistance in castration-resistant prostate cancer.Cell communication and signaling : CCS · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Single-cell and spatial omics of metabolic-immune ecosystems in prostate cancer: from androgen signaling to therapy resistance.Frontiers in immunology · 2026Review
- Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon.Biostatistics (Oxford, England) · 2025Article
- Applications and prospects of spatial transcriptomics in prostate cancer research: A narrative review.Current urology · 2025Article
- Current Role and Future Frontiers of Spatial Transcriptomics in Genitourinary Cancers.Cancers · 2025Review
- Review
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- From morphology to single-cell molecules: high-resolution 3D histology in biomedicine.Molecular cancer · 2025Review
- STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes.Genome medicine · 2025Article
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- Addressing the mean-variance relationship in spatially resolved transcriptomics data withbioRxiv : the preprint server for biology · 2024Article
- Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective.Molecular cancer · 2024Review
- Targeting IL-8 and Its Receptors in Prostate Cancer: Inflammation, Stress Response, and Treatment Resistance.Cancers · 2024Review
- Advances in spatial transcriptomics and its applications in cancer research.Molecular cancer · 2024Review
- spVC for the detection and interpretation of spatial gene expression variation.Genome biology · 2024Article
- Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment.Nature reviews. Urology · 2024Review
- Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment.PLoS computational biology · 2024Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling spatial heterogeneity in neoplastic metabolic aberrations is critical for tumor treatment. Genome-scale metabolic network models have been used successfully to simulate cancer metabolic networks. However, most models use bulk gene expression data of entire tumor biopsies, ignoring spatial heterogeneity in the TME. To account for spatial heterogeneity, we performed spatially-resolved metabolic network modeling of the prostate cancer microenvironment. We discovered novel malignant-cell-specific metabolic vulnerabilities targetable by small molecule compounds. We predicted that inhibiting the fatty acid desaturase SCD1 may selectively kill cancer cells based on our discovery of spatial separation of fatty acid synthesis and desaturation. We also uncovered higher prostaglandin metabolic gene expression in the tumor, relative to the surrounding tissue. Therefore, we predicted that inhibiting the prostaglandin transporter SLCO2A1 may selectively kill cancer cells. Importantly, SCD1 and SLCO2A1 have been previously shown to be potently and selectively inhibited by compounds such as CAY10566 and suramin, respectively. We also uncovered cancer-selective metabolic liabilities in central carbon, amino acid, and lipid metabolism. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer and other cancers where spatial transcriptomics datasets are available.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.