Spatial transcriptomics: Decoding the molecular evolution of prostate cancer

Prostate cancer (PCa) is a highly prevalent malignancy, heavily characterized by complex cellular heterogeneity and highly variable clinical outcomes. The Gleason score (GS) remains the fundamental histological grading system for evaluating tumor aggressiveness and predicting patient prognosis. However, the precise spatial dynamics and molecular transitions driving glandular epithelial (GE) cells from localized, low-grade lesions to highly advanced carcinomas have remained incompletely understood.

A new study published in Genes & Diseases by researchers from Beijing Tongren Hospital, Capital Medical University, harnesses the power of spatial transcriptomics to map the molecular evolution of PCa and identifies key genes associated with disease progression, offering new opportunities for biomarker development and precision oncology.

By employing Visium spatial transcriptomics (ST) sequencing alongside advanced inferCNV and trajectory analyses across a cohort of PCa tissue samples, the research team successfully decoded the localized expression patterns within distinct tumor microenvironments. The high-resolution data revealed that the genetic malignancy of specific GE regions tightly aligns with increasing clinical Gleason scores. Comprehensive differential gene expression (DEG) and pseudotime mapping uncovered exactly how cellular states shift during tumor advancement.

Crucially, the researchers identified a distinct cluster of oncogenes consistently driving this progression. Pathway enrichment analysis demonstrated that these specific oncogenes are heavily involved in biogenic amine and amine metabolic processes, alongside arginine and proline metabolism, fueling the metabolic demands of the growing tumor. Among the identified disease drivers, the study highlighted well-established clinical biomarkers such as FOLH1, AMACR, and KLK3, decisively validating this advanced spatial approach. More importantly, the researchers isolated powerful novel progression markers, particularly the bicarbonate transporter SLC4A4 and the histone variant H2AFJ.

Extensive histological analyses confirmed that the cellular staining index for both SLC4A4 and H2AFJ increases significantly in direct correlation with elevated Gleason scores and advanced pathological T-stages (pT-stage). Because H2AFJ is heavily enriched in luminal epithelial gland cells, its targeted up-regulation indicates a massive structural and epigenetic transformation accelerating the cancer's spread. While these comprehensive spatial data robustly highlight the critical advantage of mapping the tumor microenvironment to uncover hidden oncogenic drivers, additional large-scale clinical evaluations are necessary to translate these pathways into targeted human therapies.

In conclusion, deciphering the precise molecular transition of glandular epithelial cells offers a powerful new strategy to predict prostate cancer severity. This significant finding directly positions SLC4A4, H2AFJ, and their associated metabolic networks as highly compelling prognostic biomarkers and therapeutic targets for the next generation of precision prostate cancer treatments.

Reference

Title of Original Paper: Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics
Journal: Genes & Diseases
Genes & Diseases is a journal for molecular and translational medicine. The journal primarily focuses on publishing investigations on the molecular bases and experimental therapeutics of human diseases. Publication formats include full length research article, review article, short communication, correspondence, perspectives, commentary, views on news, and research watch.
DOI: https://doi.org/10.1016/j.gendis.2025.101983

Funding Information:
The National Natural Science Foundation of China (No. 82272864)
The Capital's Funds for Health Improvement and Research (No. 2024-2-2059)

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Genes & Diseases publishes rigorously peer-reviewed and high quality original articles and authoritative reviews that focus on the molecular bases of human diseases. Emphasis is placed on hypothesis-driven, mechanistic studies relevant to pathogenesis and/or experimental therapeutics of human diseases. The journal has worldwide authorship, and a broad scope in basic and translational biomedical research of molecular biology, molecular genetics, and cell biology, including but not limited to cell proliferation and apoptosis, signal transduction, stem cell biology, developmental biology, gene regulation and epigenetics, cancer biology, immunity and infection, neuroscience, disease-specific animal models, gene and cell-based therapies, and regenerative medicine.
Scopus Cite Score: 10.4 | Impact Factor: 14.6

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Print ISSN: 2352-4820
eISSN: 2352-3042
CN: 50-1221/R
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(A) Cryosectioned prostate cancer (PCa) tissue samples were mounted on Superfrost slides (VWR) containing a spatial microarray of 4992 barcoded spots (55 μm in diameter, 100 μm center-to-center spacing) to enable spatially resolved gene expression profiling. (B) Transcriptomic read count matrices were generated following the CytAssist ST platform. (C) Dimensionality reduction and clustering of the spatial spots were performed through PCA, UMAP and Louvain clustering analyses. (D) The clusters were annotated as distinct histological structures based on pathological evaluation. (E) Malignancy-associated features of the classified clusters were assessed through inferCNV analysis. (F) Developmental trajectories and progression stages were further characterized using DPT and PAGA analyses. (G) To identify genes associated with PCa progression, DEG analysis was conducted by comparing GE clusters aligned with GS progression patterns that met all predefined criteria. (H) Commonly dysregulated DEGs across 12 PCa samples were identified and summarized. (I) The protein expression of candidate DEGs (H2AFJ, SLC4A4, TFF3) was validated in PCa tissues spanning a spectrum of GSs and PIN using IHC staining.
(A) Developmental progression was reconstructed using DPT analysis. Diffusion maps of spatially resolved spots for each sample are presented, displaying coordinates specific to varying GE clusters (left panel) and corresponding dpt_pseudotime values (right panel). (B) The quantitative distribution of dpt_pseudotime values within GE clusters for each sample is visualized as violin plots. (C) Developmental trajectories of GE clusters for each sample are visualized using PAGA graphs, displaying individual spatial spots (left panel) and GE clusters (right panel). (D) The CNV score (left panel) and dpt_pseudotime values (right panel) were integrated into the PAGA graph to illustrate developmental dynamics within each GE cluster.
(A) Representative images of IHC staining for TFF3, SLC4A4, and H2AFJ in prostate cancer (PCa) tissues are presented, illustrating distinct histological classifications, including PIN, GS 3 + 3, GS 3 + 4, GS 4 + 4, and GS 5 + 4. (B) The percentages of positively stained cells and the corresponding staining indices for these proteins were quantified and compared across PCa tissues stratified by GSs (GS 7) and pT stages (pT2, pT3, and pT4). The results are displayed as histograms, with data represented as the means ± standard errors of the means (SEMs). ns, not significant. p > 0.05; ∗p

10.08.2026 Compuscript Ltd

Regions: Europe, Ireland, Asia, China

Keywords: Science, Life Sciences

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