Analysis of the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins using snFLARE-seq and mxFRIZNGRND.
1/5 보강
Prostate cancer cells of different anatomical locations display remarkable heterogeneity.
APA
He D, Hu H, et al. (2026). Analysis of the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins using snFLARE-seq and mxFRIZNGRND.. Nature communications, 17(1). https://doi.org/10.1038/s41467-026-69347-7
MLA
He D, et al.. "Analysis of the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins using snFLARE-seq and mxFRIZNGRND.." Nature communications, vol. 17, no. 1, 2026.
PMID
41651840
Abstract
Prostate cancer cells of different anatomical locations display remarkable heterogeneity. This poses a challenge to the clinical relevance of pre-clinical models and the efficacy of contemporary therapeutic approaches. Here we develop the snFLARE-seq and mxFRIZNGRND methodologies to directly investigate the transcriptomic and metabolomic landscape of prostate cancer patients utilizing formalin-fixed paraffin-embedded (FFPE) specimens. A retrospective analysis reveals the clinical disparities of prostate cancer from peripheral zone (PZ), transition zone (TZ), and across PZ and TZ. The snFLARE-seq, refined for enhanced single-nucleus sequencing, unveils distinct cell type distributions and signaling pathways between PZ and TZ samples. Hormone therapy substantially affects cancer cells and microenvironment, leading to a polarized feature of epithelial cells and a subverted immune microenvironment. With improvements in metabolite extraction, mxFRIZNGRND reveals unique metabolic features of prostate cancer from different origins. The metabolomic results indicate that PZ cancer cells are in a metabolic-dormant status, which are probably awaken by hormone therapy. Integrative analysis of results from snFLARE-seq, mxFRIZNGRND, and TCGA database uncovers four metabolic pathways and related genes associated with disease aggressiveness. Our work could accelerate investigations on disease heterogeneity and evolution in real-world clinical settings, stimulating patient-specific precision healthcare solutions.
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