# Analysis of the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins using snFLARE-seq and mxFRIZNGRND

## TL;DR

This paper introduces new methods to study prostate cancer differences in tissue origins, revealing distinct genetic and metabolic features that could improve personalized treatment.

## Contribution

The development of snFLARE-seq and mxFRIZNGRND for analyzing FFPE samples to uncover prostate cancer heterogeneity.

## Key findings

- Prostate cancer from different anatomical zones shows distinct transcriptomic and metabolomic profiles.
- Hormone therapy alters cancer cell metabolism and immune microenvironment.
- Four metabolic pathways are linked to disease aggressiveness through integrative analysis.

## 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.

Prostate cancer heterogeneity limits the efficacy of current therapeutic approaches. Here, the authors develop two methods, snFLARE-seq and mxFRIZNGRND, using formalin-fixed paraffin embedded samples for analysing the transcriptomic and metabolomic landscape of different anatomical origins.