Date

April 10, 2025

Source

Nature

Categories

Single-cell and spatial RNA sequencing identify divergent microenvironments and progression signatures in early- versus late-onset prostate cancer

Data availability

The scRNA-seq and ST data generated from this study are available via Dryad at https://doi.org/10.5061/dryad.4b8gthtqb (ref. 74) and OMIX (accession number OMIX008930). The publicly available data can be downloaded from TCGA (https://portal.gdc.cancer.gov), GEO (https://www.ncbi.nlm.nih.gov/gds), cBioPortal (https://www.cbioportal.org/study), and MiOncoCirc (https://mioncocirc.github.io). The specific accession numbers are listed in the ‘Publicly available data for validation’ section above. All other data supporting the findings of the study are available from the corresponding author upon request.

Code availability

The code used to generate the results are available via Github at https://github.com/ChengBioinfo/SC-AgeSpePCa (source codes are deposited in the ‘Source-codes’ branch).

References

  1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  PubMed  Google Scholar 

  2. Conti, D. V. et al. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat. Genet. 53, 65–75 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ugai, T. et al. Is early-onset cancer an emerging global epidemic? Current evidence and future implications. Nat. Rev. Clin. Oncol. 19, 656–673 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Merrill, R. M. & Bird, J. S. Effect of young age on prostate cancer survival: a population-based assessment (United States). Cancer Causes Control 13, 435–443 (2002).

    Article  PubMed  Google Scholar 

  5. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: an expanding universe. Cell 186, 243–278 (2023).

    Article  PubMed  Google Scholar 

  6. Schmitt, C. A., Wang, B. & Demaria, M. Senescence and cancer — role and therapeutic opportunities. Nat. Rev. Clin. Oncol. 19, 619–636 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lin, D. W., Porter, M. & Montgomery, B. Treatment and survival outcomes in young men diagnosed with prostate cancer: a population-based cohort study. Cancer 115, 2863–2871 (2009).

    Article  PubMed  Google Scholar 

  8. Grönberg, H., Damber, J. E., Jonsson, H. & Lenner, P. Patient age as a prognostic factor in prostate cancer. J. Urol. 152, 892–895 (1994).

    Article  PubMed  Google Scholar 

  9. Shih, H. J., Fang, S. C., An, L. & Shao, Y. J. Early-onset prostate cancer is associated with increased risks of disease progression and cancer-specific mortality. Prostate 81, 118–126 (2021).

    Article  CAS  PubMed  Google Scholar 

  10. Chahal, H. S. & Drake, W. M. The endocrine system and ageing. J. Pathol. 211, 173–180 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Nikolich-Žugich, J. The twilight of immunity: emerging concepts in aging of the immune system. Nat. Immunol. 19, 10–19 (2018).

    Article  PubMed  Google Scholar 

  12. Gerhauser, C. et al. Molecular evolution of early-onset prostate cancer identifies molecular risk markers and clinical trajectories. Cancer Cell 34, 996–1011.e1018 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cheng, Q. et al. Pre-existing castration-resistant prostate cancer-like cells in primary prostate cancer promote resistance to hormonal therapy. Eur. Urol. 81, 446–455 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Guo, W. et al. Single-cell transcriptomics identifies a distinct luminal progenitor cell type in distal prostate invagination tips. Nat. Genet. 52, 908–918 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Han, M. et al. FOXA2 drives lineage plasticity and KIT pathway activation in neuroendocrine prostate cancer. Cancer Cell 40, 1306–1323.e1308 (2022).

    Article  CAS  PubMed  Google Scholar 

  16. Kfoury, Y. et al. Human prostate cancer bone metastases have an actionable immunosuppressive microenvironment. Cancer Cell 39, 1464–1478.e1468 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chen, S. et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat. Cell Biol. 23, 87–98 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Henry, G. H. et al. A cellular anatomy of the normal adult human prostate and prostatic urethra. Cell Rep. 25, 3530–3542 e3535 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sun, H. et al. Single-cell transcriptome analysis indicates fatty acid metabolism-mediated metastasis and immunosuppression in male breast cancer. Nat. Commun. 14, 5590 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Li, D. et al. CST1 inhibits ferroptosis and promotes gastric cancer metastasis by regulating GPX4 protein stability via OTUB1. Oncogene 42, 83–98 (2023).

    Article  CAS  PubMed  Google Scholar 

  21. Perera, O. et al. Trefoil factor 3 (TFF3) enhances the oncogenic characteristics of prostate carcinoma cells and reduces sensitivity to ionising radiation. Cancer Lett. 361, 104–111 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Cheng, Y. et al. Depression-induced neuropeptide Y secretion promotes prostate cancer growth by recruiting myeloid cells. Clin. Cancer Res. 25, 2621–2632 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Yuan, H. et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 47, D900–D908 (2018).

    Article  PubMed Central  Google Scholar 

  24. Faubert, B., Solmonson, A. & DeBerardinis, R. J. Metabolic reprogramming and cancer progression. Science 368, eaaw5473 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dabravolski, S. A. et al. Heat shock protein 90 as therapeutic target for CVDs and heart ageing. Int. J. Mol. Sci. 23, 649 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Malta, T. M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173, 338–354.e315 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mellinghoff, I. K. et al. HER2/neu kinase-dependent modulation of androgen receptor function through effects on DNA binding and stability. Cancer Cell 6, 517–527 (2004).

    Article  CAS  PubMed  Google Scholar 

  28. Zhang, P. et al. NRP1 promotes prostate cancer progression via modulating EGFR-dependent AKT pathway activation. Cell Death Dis. 14, 159 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Han, H. et al. Mesenchymal and stem-like prostate cancer linked to therapy-induced lineage plasticity and metastasis. Cell Rep. 39, 110595 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Liu, Y. et al. The NLRP3 inflammasome in fibrosis and aging: the known unknowns. Ageing Res. Rev. 79, 101638 (2022).

    Article  CAS  PubMed  Google Scholar 

  31. Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809 e723 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Zhang, L. et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181, 442–459 e429 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Veglia, F., Sanseviero, E. & Gabrilovich, D. I. Myeloid-derived suppressor cells in the era of increasing myeloid cell diversity. Nat. Rev. Immunol. 21, 485–498 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Alegre, M. L., Frauwirth, K. A. & Thompson, C. B. T-cell regulation by CD28 and CTLA-4. Nat. Rev. Immunol. 1, 220–228 (2001).

    Article  CAS  PubMed  Google Scholar 

  35. Sharma, J. R., Agraval, H. & Yadav, U. C. S. Cigarette smoke induces epithelial-to-mesenchymal transition, stemness, and metastasis in lung adenocarcinoma cells via upregulated RUNX-2/galectin-3 pathway. Life Sci. 318, 121480 (2023).

    Article  CAS  PubMed  Google Scholar 

  36. Snaebjornsson, M. T., Janaki-Raman, S. & Schulze, A. Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer. Cell Metab. 31, 62–76 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Watt, M. J. et al. Suppressing fatty acid uptake has therapeutic effects in preclinical models of prostate cancer. Sci. Transl. Med. 11, eaau5758 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Johnson, W. T., Dorn, N. C., Ogbonna, D. A., Bottini, N. & Shah, N. J. Lipid-based regulators of immunity. Bioeng. Transl. Med. 7, e10288 (2022).

    Article  CAS  PubMed  Google Scholar 

  39. Pizer, E. S. et al. Increased fatty acid synthase as a therapeutic target in androgen-independent prostate cancer progression. Prostate 47, 102–110 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Zhang, X., Ji, L. & Li, M. O. Control of tumor-associated macrophage responses by nutrient acquisition and metabolism. Immunity 56, 14–31 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lu, Z. et al. Dissecting the genetic and microenvironmental factors of gastric tumorigenesis in mice. Cell Rep. 41, 111482 (2022).

    Article  CAS  PubMed  Google Scholar 

  42. Erdogan, B. et al. Cancer-associated fibroblasts promote directional cancer cell migration by aligning fibronectin. J. Cell Biol. 216, 3799–3816 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nastaly, P. et al. EGFR as a stable marker of prostate cancer dissemination to bones. Br. J. Cancer 123, 1767–1774 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Huang, P. et al. BMP-2 induces EMT and breast cancer stemness through Rb and CD44. Cell Death Discov. 3, 17039 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Xu, F. et al. HOXD13 suppresses prostate cancer metastasis and BMP4-induced epithelial-mesenchymal transition by inhibiting SMAD1. Int. J. Cancer 148, 3060–3070 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cheng, Y. et al. An early-onset specific polygenic risk score optimizes age-based risk estimate and stratification of prostate cancer: population-based cohort study. J. Transl. Med. 22, 366 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  48. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e324 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e3529 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ianevski, A., Giri, A. K. & Aittokallio, T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat. Commun. 13, 1246 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hu, C. et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 51, D870–D876 (2023).

    Article  CAS  PubMed  Google Scholar 

  54. Network, B. I. C. C. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021).

    Article  Google Scholar 

  55. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  57. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

  59. Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).

    Article  CAS  PubMed  Google Scholar 

  62. Ma, Y. & Zhou, X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat. Biotechnol. 40, 1349–1359 (2022).

    Article  CAS  PubMed  Google Scholar 

  63. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Zhang, M. J. et al. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat. Genet. 54, 1572–1580 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Zhao, S. G. et al. Associations of luminal and basal subtyping of prostate cancer with prognosis and response to androgen deprivation therapy. JAMA Oncol 3, 1663–1672 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Wu, Y. et al. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov. 12, 134–153 (2022).

    Article  CAS  PubMed  Google Scholar 

  71. Alghamdi, N. et al. A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data. Genome Res. 31, 1867–1884 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Schcolnik-Cabrera, A. et al. Calreticulin in phagocytosis and cancer: opposite roles in immune response outcomes. Apoptosis 24, 245–255 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Hirz, T. et al. Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses. Nat. Commun. 14, 663 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Cheng, Y. et al. Single-cell and spatial RNA sequencing identify divergent microenvironments and progression signatures in early- versus late-onset prostate cancer. Dryad https://doi.org/10.5061/dryad.4b8gthtqb (2025).

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Acknowledgements

This work was supported by the following: (1) the Medical Research Project of Jiangsu Commission of Health, M2022015 (G.C.) and (2) Fundamental Research Funds for the Central Universities, 2242023K5007 (B.X.). We gratefully acknowledge Y. Liu of the Nanjing Medical University School of Public Health for her assistance with the flow cytometry experiments.

Author information

Author notes

  1. These authors contributed equally: Yifei Cheng, Bingxin Liu, Junyi Xin.

Authors and Affiliations

  1. Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China

    Yifei Cheng, Bingxin Liu, Jiajin Wu & Meilin Wang

  2. Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China

    Yifei Cheng, Bingxin Liu, Jiajin Wu, Zhengdong Zhang & Meilin Wang

  3. Department of Urology, Southeast University Zhongda Hospital, Nanjing, China

    Yifei Cheng, Wenchao Li & Bin Xu

  4. Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China

    Junyi Xin

  5. Department of Pathology, The Affiliated Hospital of Nanjing University of Chinese Medicine & Jiangsu Province Hospital of Chinese Medicine, Nanjing, China

    Xiaobin Wu

  6. Department of Urology, The First Affiliated Hospital of Nanjing Medical University & Jiangsu Province People’s Hospital, Nanjing, China

    Jinwei Shang & Gong Cheng

  7. Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China

    Mulong Du

  8. The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China

    Meilin Wang

Contributions

Conceptualization: M.W. and G.C.; methodology: Y.C., B.X. and M.D.; formal analysis: Y.C. and J.X.; investigation: Y.C., B.L., W.L., M.D., and J.X.; resources: G.C., X.W. and J.S; project administration and supervision: M.W., G.C., M.D., B.X. and Z.Z.; validation: B.L., W.L., J.X. and M.D.; funding acquisition: M.W., B.X. and G.C.; writing—original draft: Y.C.; writing—review and editing: M.D., J.X., G.C. and J.W.

Corresponding authors

Correspondence to Bin Xu, Mulong Du, Gong Cheng or Meilin Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Dechao Feng, Kuan-lin Huang, Chia-Jung Li and Zishan Wang for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The top over-expressed genes and enriched biological pathways for myeloid clusters.

Heatmap showing top 50 over-expressed genes for each cluster (Middle), with the significant markers (Left) and top 5 enriched GO_BP pathways (Right).

Extended Data Fig. 2 MIF validating the existence and phenotypes of Macro_APOE in EOPC, related to Fig 4L.

(a) Representative image of mIF staining for CD68 and APOE (indicating Macro_APOE), CD36 (indicating fatty acid oxidation) and CD163 (indicating M2 polarization) on 11 EOPC sections from different human PCa tissues (single channel images see Supplementary Fig. 25c). (b, c) Representative image of mIF staining for CD68 and APOE (indicating Macro_APOE), SPP1 (indicating TAM_SPP1) and CD84 (indicating MDSC) on 11 EOPC sections (single channel images see Supplementary Fig. 25d, e).

Extended Data Fig. 3 Annotation of T-cells, related to Fig 4P.

(a) A UMAP view of 17,435 T-cells, colored by clusters. (b, c) A UMAP view of T-cells, colored by the expression of CD4 and CD8A. (d) Clustering plots of CD4+ T-cells based on the first 2 components derived from Monocle analysis, colored by pseudo-time. (e) The expression of T-cell differentiation-related markers in CD4+ T-cells trajectory. The pseudo-time was illustrated on the X axis with small on the left and larger on the right. (f) Clustering plots of CD8+ T-cells based on the first 2 components derived from Monocle analysis, colored by pseudo-time. (g) The expression of T-cell differentiation-related markers in CD8+ T-cells trajectory. The pseudo-time was illustrated on the X axis with small on the left and larger on the right.

Extended Data Fig. 4 Analyses of CAFs.

(a) Dot plot showing expression of all potential ligands predicted by NicheNet for each TME cell type, where dot size and color represent percentage of marker gene expression and the averaged scaled expression value, respectively. (b) Mesenchymal cell distribution of EOPC versus LOPC, with abundance represented by ratio of observed to expected (Ro/e). (c) Correlation plot of BMPs gene expression levels with AR-MP AUCell score in GSE21034. Error bands represented 95% confidence interval (two-sided Spearman’s correlation analysis). (d) Clustering of fibroblasts on the LOPC3 ST slide, related to Fig. 5f. (e) Representative image of multiplex immunofluorescence staining for KLK3 (indicating cancer cells), COL-3 (indicating fibroblasts), FAP (indicating inflammatory CAFs), BMPR1B and BMP4 in 11 EOPC sections from different human PCa tissues.

Extended Data Fig. 5 Analysis and validation of results concerning CAFs.

(a) Correlation plot of AR-MP AUCell scores with EMT GSVA score in TCGA. Error bands represented 95% confidence interval (two-sided Spearman’s correlation analysis). (b) Boxplots comparing the GSVA of EMT scores between patients with different clinical characteristics (n = 45, 242, 63, 130, 4, 13, 10, 164, 154, 126, 10, 339 and 76 for TCGA patients with Gleason score 6, 7, 8, 9, 10, pathologic T stage T2a, T2b, T2c, T3a, T3b, T4, pathologic N stage N0 and N1, respectively; n = 103 and 31 for GSE21034 patients with negative and positive surgical margins, respectively; n = 12, 167, 160 and 105 for PCa tissues extracted from prostate, lymph nodes, bones and other site in su2c dataset, respectively). Box-and-whisker plots showed the minimum, 25th percentile, median, 75th percentile and maximum (two-sided Wilcoxon tests). (c, d) Representative image and statistical analysis of multiplex immunofluorescence staining for CHD1, KRT8, CDH2 and MMP7 in 20 LOPC and 11 EOPC sections from different human PCa tissues. (e) Boxplot comparing the AUCell scores of iCAF clusters in stromal cells between lethal (n = 87) and nonlethal (n = 186) PCa samples from the GSE214940 dataset. Box-and-whisker plots showed the minimum, 25th percentile, median, 75th percentile and maximum (two-sided Wilcoxon tests). (f) Kaplan–Meier plot of overall survival of PCa patients from SU2C dataset, stratified by AUCell score of expression level of BMP4 (Left), BMP5 (Middle) and BMP7 (Right) (two-sided log-rank test). P-values were not adjusted for multiple comparisons.

Source data

Extended Data Fig. 6 Analysis and validation of results concerning CAFs.

(a) Dot plot showing marker gene expression for each cell type for GSE206962, where the dot size and colour represent the percentage of marker gene expression and the averaged scaled expression value, respectively. (b) Boxplots comparing the AUCell scores of the AR-MP between CRPC and HNPC tumours from three bulk datasets (n = 65, 21 and 17 for castration resistant, castrated and hormone-naïve PCa in GSE189343, respectively; n = 29 and 22 for metastatic CRPC and localized HNPC in GSE32269, respectively; n = 13 and 113 for CRPC and HNPC in GSE70768, respectively). Box-and-whisker plots showed the minimum, 25th percentile, median, 75th percentile and maximum (two-sided Kruskal-Wallis test for GSE189343; two-sided Wilcoxon tests for GSE32269 and GSE70768). (c) Representative image of multiplex immunofluorescence (mIF) staining showing the expression of iCAF markers (Collagen-III, FAP, PDGFRA) in the isolated CAFs from human PCa tissues (single channel images see Supplementary Fig. 27). (d–f) Western blotting evaluating and comparing the expression levels of iCAF-related proteins (D) and senescence-associated proteins (E-F) between CAFs isolated from 5 EOPC samples and 5 LOPC samples in the validation cohort. P-values were not adjusted for multiple comparisons.

Source data

Extended Data Fig. 7 Molecular differences of malignant epithelial cells between EOPC and LOPC in the validation.

(a) Boxplot comparing the AUCell scores of 6 MPs between EOPC (n = 10,249) and LOPC (n = 10,491) malignant epithelia (****PMP2 = 2.1 × 10−12, ****POthers < 2.2 × 10−16). (b) Correlation plot between hypoxia GSVA scores and AR-MP AUCell scores in malignant epithelia. (c) Boxplot of the difference in the ErbB pathways GSVA scores between AR-MP_High (n = 11,062) and AR-MP_Low (n = 9,606) malignant epithelia (****POthers < 2.2 × 10−16). (d) Boxplot of the difference in the lipid metabolism pathways GSVA scores between AR-MP_High and AR-MP_Low malignant epithelia (****POthers < 2.2 × 10−16). (e) Clustering plots of malignant epithelia based on the first 2 components derived from Monocle analysis, colored by pseudo-time (Upper) and AR-MP activation status (Below). (g) Boxplot of the difference in the mRNA stemness indices between AR-MP_High and AR-MP_Low malignant epithelia (****P < 2.2 × 10−16). (g) Boxplot of the difference in the EMT GSVA scores between AR-MP_High and AR-MP_Low malignant epithelia (****P < 2.2 × 10−16). (h) Spatial illustration of AR-MP scores on the Re-EOPC4-ST and Rep-LOPC1-ST slides. The box-and-whisker plots of A, C, D, F and G showed the minimum, 25th percentile, median, 75th percentile and maximum, and data were analysed by two-sided Wilcoxon tests. P-values were not adjusted for multiple comparisons.

Extended Data Fig. 8 Molecular differences of phagocytes between EOPC and LOPC in the validation.

(a) Phagocytes distribution of EOPC versus LOPC, with abundance represented by the ratio of observed to expected (Ro/e). (b) Heatmap showing the mean AUCell scores for macrophage subtypes by myeloid functional sets (M1/M2 polarization and suppressive, angiogenesis, and phagocytosis activity). (c) Boxplot comparing the AUCell scores of M2 and MDSC functional sets between EOPC (n = 1,718) and LOPC (n = 377) Macro_APOE cells. Box-and-whisker plots showed the minimum, 25th percentile, median, 75th percentile and maximum (two-sided Wilcoxon tests). (d) Boxplot comparing the GSVA scores of fatty acid and cholesterol pathways among myeloid subtypes (n = 434, 69, 1,182, 71, 729, 821, 166, 930, 1,145, 626, 2,095, 423 and 435 for pDC_JCHAIN, cDC1_CLEC9A, cDC2_CD1C, cDC3_LAMP3, Mono_IL1B, Mono_FCN1, Macro_CXCL11, Macro_C3, Macro_RGS1, Macro_FOLR2, Macro_APOE, Macro_MT1A and Macro_Ribosome, respectively). Box-and-whisker plots showed the minimum, 25th percentile, median, 75th percentile and maximum. (e) Chord plots showing the interactions between AR-MP_High malignant epithelia and Macro_APOE cells in EOPC versus LOPC inferred by CellPhoneDB, and Dot plot showing the ligand‒receptor pairs from Macro_APOE to AR-MP_High malignant epithelia shared in EOPC samples. (f) Spatially illustration of different cell types (Left), APOE expression level (Middle) and AR-MP AUCell score (Right) for Rep-EOPC4-ST. (g) Violin-and-box plots comparing the stimulatory and inhibitory AUCell scores of CD4+ T cells (n = 5,348 and 2,459 for EOPC and LOPC, respectively) and CD8+ T cells (n = 8,110 and 7,161 for EOPC and LOPC, respectively) between EOPC and LOPC. Box-and-whisker plots showed the minimum, 25th percentile, median, 75th percentile and maximum (two-sided Wilcoxon tests). P-values were not adjusted for multiple comparisons.

Extended Data Fig. 9 Molecular differences of mesenchymal cells between EOPC and LOPC in the validation.

(a) Mesenchymal cells distribution of EOPC versus LOPC, with abundance represented by the ratio of observed to expected (Ro/e). (b) Violin plots comparing the BMPs expression levels between different mesenchymal cell types and different sample types (n = 2,503, 2,205, 388, 395, 466, 493, 1,485 and 200 for SMC_PCP4, SMC_KLF2, SMC_HOPX, myCAF_CCL21, myCAF_RGS5 iCAF_FAP, iCAF_APOD and Progenitor_MKI67, respectively; n = 2,330 and 5,805 for EOPC and LOPC, respectively). (c) Correlation plot between iCAF_APOD abundances and malignant epithelia AR-MP scores in 5 EOPC and 5 LOPC samples from the validation cohort. Error bands represented 95% confidence interval (two-sided Spearman’s correlation analysis). (d) Correlation plot between average BMPs expression levels of iCAF_APOD and malignant epithelia AR-MP scores in in 5 EOPC and 5 LOPC samples from the validation cohort. Error bands represented 95% confidence interval (two-sided Spearman’s correlation analysis). (e) Violin plot comparing the senescence AUCell score between mesenchymal subtypes. (f) Violin plots comparing the expression levels of key senescence associated genes between CAF subtypes, including those concerning cell cycle arrest, senescence associated secretory phenotype, DNA damage repair, nuclear change and anti-apoptosis (two-sided Wilcoxon tests). (g) Spatially illustration of different cell types (Left), PDGFRA expression level (Middle) and AR-MP AUCell score (Right) for Rep-LOPC1-ST. P-values were not adjusted for multiple comparisons.

Extended Data Fig. 10 Schematic diagram of the distinct tumour microenvironments in EOPC and LOPC.

The heterogeneity between EOPC and LOPC malignant epithelia could be primarily described by the differential activation of the AR-MP, which was positively associated with the androgen response and negatively related to tumour stemness. In EOPC, APOE+ TAMs coexisted with AR-MP-activated malignant epithelia in a hypoxic and LMR niche, promoting PCa progression through direct interaction with malignant epithelia and regulation of the immune TME. In contrast, through BMP-BMPR interactions, iCAFs, which exhibited cellular senescence characteristics, downregulated the AR-MP, activated the EMT process, and promoted a castration-resistant phenotype, thereby enhancing tumour metastasis in LOPC.

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Cheng, Y., Liu, B., Xin, J. et al. Single-cell and spatial RNA sequencing identify divergent microenvironments and progression signatures in early- versus late-onset prostate cancer. Nat Aging (2025). https://doi.org/10.1038/s43587-025-00842-0

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