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ZENODO
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Data sources: Datacite
ZENODO
Software . 2025
Data sources: Datacite
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snFLARE-seq and mrFRIGID for the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins

Authors: Li, Zhenfei; xiaokai20220628;

snFLARE-seq and mrFRIGID for the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins

Abstract

snFLARE-seq and mrFRIGID for the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins 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 developed the snFLARE-seq and mrFRIGID methodologies to directly investigate the transcriptomic and metabolomic landscape of prostate cancer patients utilizing formalin-fixed paraffin-embedded (FFPE) specimens. A retrospective analysis revealed 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, unveiled distinct cell type distributions and signaling pathways between PZ and TZ samples. Hormone therapy substantially affected cancer cells and microenvironment, leading to a polarized feature of epithelial cells and a subverted immune microenvironment. With improvements on metabolite extraction, mrFRIGID revealed unique metabolic features of prostate cancer from different origins. The metabolomic results indicate that PZ cancer cells were in a metabolic-dormant status, which were probably awaken by hormone therapy. Integrative analysis of results from snFLARE-seq, mrFRIGID, and TCGA database uncovered four metabolic pathways and related genes associated with disease aggressiveness. Our work would accelerate investigations on disease heterogeneity and evolution in real-world clinical settings, stimulating patient-specific precision healthcare solutions. This study utilized the Dynamic Network Biomarkers (DNB) model developed by ChenLab at the Chinese Academy of Sciences (CAS) to analyze single-cell data. For specific details, please visit https://github.com/Kaiyu-W/DNBr. To install this package, use the following command in R: devtools::install_github("Kaiyu-W/DNBr") The associated datasets are publicly available at Zenodo under the following persistent link: https://zenodo.org/records/15671856

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Related to Research communities
Cancer Research