Integrated single-cell multi-omics analysis unveils heterogeneity in the prostate cancer tumor microenvironment.
[BACKGROUND] Prostate cancer (PCa) is a heterogeneous disease characterised by a highly complex cellular ecosystem within its tumour microenvironment (TME).
APA
Mei Z, Chen C, et al. (2026). Integrated single-cell multi-omics analysis unveils heterogeneity in the prostate cancer tumor microenvironment.. Discover oncology, 17(1). https://doi.org/10.1007/s12672-026-04410-9
MLA
Mei Z, et al.. "Integrated single-cell multi-omics analysis unveils heterogeneity in the prostate cancer tumor microenvironment.." Discover oncology, vol. 17, no. 1, 2026.
PMID
41729213
Abstract
[BACKGROUND] Prostate cancer (PCa) is a heterogeneous disease characterised by a highly complex cellular ecosystem within its tumour microenvironment (TME). However, the crosstalk patterns between tumour and immune cells remain poorly understood.
[METHODS] Single cell RNA sequencing, spatial transcriptomics and bulk RNA-seq data were integrated to explore key Tregs cells modules and genes involved in PCa processes in the tumour microenvironment.
[RESULTS] We found that the oxidative phosphorylation and apoptosis signaling pathways of Tregs cells in PCa patients is significantly activated. Tregs cells and Tumour cells may interact through PPIA-BSG ligand-receptor pairs to form an immunosuppressive microenvironment. Using high-dimensional weighted gene co-expression network analysis, we identified 6 key functional modules in Tregs from PCa. The spatial localization of these modules in tumor tissues was further validated using spatial transcriptomics data. Subsequent differential modules analysis revealed that the M2 module exhibited the most significant changes. A Cox proportional hazards model was then constructed based on this module, leading to the identification of 8 key prognostic genes, including ANKRD37, CHORDC1, DOK2, HSPA6, RGCC, SERPINH1, STIP1, and UBB. These genes hold promise as potential molecular markers for the diagnosis and prognostic evaluation of PCa.
[CONCLUSION] These findings reveal potential ligand-receptor interactions in the tumour microenvironment of PCa patients and key Tregs cell module genes involved in the formation of an immunosuppressive microenvironment. Targeting these cells and genes will help advance the treatment of PCa.
[METHODS] Single cell RNA sequencing, spatial transcriptomics and bulk RNA-seq data were integrated to explore key Tregs cells modules and genes involved in PCa processes in the tumour microenvironment.
[RESULTS] We found that the oxidative phosphorylation and apoptosis signaling pathways of Tregs cells in PCa patients is significantly activated. Tregs cells and Tumour cells may interact through PPIA-BSG ligand-receptor pairs to form an immunosuppressive microenvironment. Using high-dimensional weighted gene co-expression network analysis, we identified 6 key functional modules in Tregs from PCa. The spatial localization of these modules in tumor tissues was further validated using spatial transcriptomics data. Subsequent differential modules analysis revealed that the M2 module exhibited the most significant changes. A Cox proportional hazards model was then constructed based on this module, leading to the identification of 8 key prognostic genes, including ANKRD37, CHORDC1, DOK2, HSPA6, RGCC, SERPINH1, STIP1, and UBB. These genes hold promise as potential molecular markers for the diagnosis and prognostic evaluation of PCa.
[CONCLUSION] These findings reveal potential ligand-receptor interactions in the tumour microenvironment of PCa patients and key Tregs cell module genes involved in the formation of an immunosuppressive microenvironment. Targeting these cells and genes will help advance the treatment of PCa.