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Distinct treatment-induced neuroendocrine prostate cancer subtypes predict prognosis

GSE306625 Homo sapiens Expression profiling by high throughput sequencing 4 samples Submitted 2026/06/02 Platform GPL24676
Summary
Second-generation hormonal therapy inhibits castration-resistant prostate cancer (CRPC), but the tumor eventually recurs as neuroendocrine prostate cancer (NEPC) and turns lethal. With limited treatment options for advanced CRPC exhibiting an NEPC phenotype, we aimed to elucidate the mechanisms of adeno-to-neuroendocrine lineage transition to guide the development of effective therapeutic strategies. By integrating single-cell RNA sequencing data from fresh human CRPC cases, we identified three distinct neuroendocrine subtypes in CRPC: a REST-dependent subtype (NE I), an N-Myc-dependent subtype (NE II), and a combined N-Myc/REST subtype (NE I+II) among 5,797 neuroendocrine-like epithelial cells. These subtypes were validated through multiplex immunofluorescence staining. Single-cell trajectory analysis uncovered N-Myc and REST as key transcription factors driving these distinct neuroendocrine lineages, and multi-omics time course analysis of publicly available transcriptomic data further recapitulated the N-Myc and REST lineage progression. Notably, we observed PSMA loss in the N-Myc lineage and identified STMN1 as a biomarker for PSMA-negative NEPC. We further validated the prognostic value of STMN1 in the TCGA dataset and in 60 in-house CRPC tissues. Our analyses provide valuable insights into the adeno-to-neuroendocrine lineage transition in prostate cancer, offering a powerful tool for early detection of lethal NEPC phenotypes and guiding 177Lu-PSMA-617 therapy for advanced CRPC.
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Direct links to NCBI, no account and no request form: the whole study as GSE306625_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 4 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1311508 and SRA study SRP613607. Searching any of these in the dataset finder brings you back here.

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