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A single cell transcriptional profile of benign prostatic hyperplasia

GSE290213 Homo sapiens Expression profiling by high throughput sequencing 27 samples 2026/02/25 GPL11154
Summary
Benign prostatic hyperplasia (BPH) is characterized by excessive cell proliferation and inflammation and affects most aging men. The development of new therapies for BPH requires a deeper understanding of the underlying pathophysiology and cellular components of BPH. Single-cell RNA-sequencing was performed on prostate tissue from 15 patients undergoing holmium laser enucleation of the prostate for treatment of BPH. Clustering and differential expression analysis on aligned single cell RNA-seq data was performed to annotate all cell types. 16,234 cells were analyzed and specific stromal, epithelial, and immune subgroups were found to be strongly associated with inflammation. A rare luminal subgroup was identified and pseudotime analysis indicated this luminal subgroup might give rise to other luminal cells. Using a gene set derived from epithelial stem cells, we found that this luminal subgroup had a significantly higher stem cell signature score than all other epithelial subgroups, suggesting this subgroup is a luminal precursor state. Ligand-receptor interactions between stromal, epithelial, and immune cells were explored with CellPhoneDB. Significant interactions involving MIF, a pro-inflammatory cytokine that promotes epithelial cell growth and inflammatory response in the prostate, were identified between the progenitor-like luminal subgroup and both fibroblasts and macrophages. Our single-cell profiling of BPH provides a roadmap for investigating inflammation-linked cell subgroups and highlights a progenitor-like luminal subgroup interacting with other cell groups via MIF that may contribute to the inflammation and cell proliferation phenotype associated with BPH.
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NCBI GEO page ↗ Paper (PMID 41832222) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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