Related Experiment Video
Updated: Jun 16, 2026
10:16
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics.
Yongjun Quan1, Mingdong Wang1, Fan Zou1
1Department of Urology, Beijing Tongren Hospital, Capital Medical University, Beijing 100176, China.
Genes & Diseases
|June 15, 2026
Summary
Spatial transcriptomics identified key genes like SLC4A4 and H2AFJ linked to prostate cancer (PCa) progression. This approach aids in discovering potential biomarkers for advanced PCa, improving diagnostic capabilities.
Area of Science:
- Oncology
- Genomics
- Biotechnology
Background:
- Prostate cancer (PCa) transcriptomic profiling is challenging due to glandular epithelial (GE) cell distribution.
- Spatial transcriptomics (ST) offers a novel approach to analyze tissue heterogeneity.
Purpose of the Study:
- To identify genes associated with PCa progression using ST.
- To establish an ST-based framework for predicting PCa advancement.
Main Methods:
- Analyzed 12 PCa tissue samples using ST.
- Applied PCA, UMAP, and Louvain clustering for transcriptomic classification.
- Utilized inferCNV, DPT, and PAGA to assess GE cluster progression and trajectories.
- Validated key gene expression with immunohistochemistry (IHC).
Main Results:
- Identified spatially resolved histological structures and GE clusters.
- Discovered oncogenes (TFF3, OR51E2, FOLH1, AMACR, FOS, SLC4A4, EGR1, NDUFB9, H2AFJ) positively correlated with PCa progression.
- Confirmed elevated SLC4A4 and H2AFJ expression in advanced PCa via IHC.
Conclusions:
- ST provides a robust framework for predicting PCa progression.
- Identified promising progression-associated genes as potential clinical biomarkers.
