ArticleTranslational andrology and urology2025
Omics integration identified CRIP2 as a key mediator of olaparib resistance in prostate cancer.
Article in Translational andrology and urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Single-cell and spatial omics of metabolic-immune ecosystems in prostate cancer: from androgen signaling to therapy resistance.Frontiers in immunology · 2026Review
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14 authors.
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Abstract
Background: The mechanisms driving resistance to poly(ADP-ribose) polymerase inhibitors (PARPis), such as Olaparib, in castration-resistant prostate cancer (CRPC) that emerge after androgen deprivation therapy (ADT) remain largely undefined. This study aimed to systematically identify tumor epithelial cell subpopulations associated with Olaparib resistance by integrating single-cell transcriptomics, spatial transcriptomics (ST), and bulk RNA-sequencing (RNA-seq) data, and to construct a risk prognostic model. Methods: Single-cell RNA-sequencing (scRNA-seq) data from six CRPC patients were subjected to quality control, clustering, annotation, and dimensionality reduction to identify distinct epithelial cell subpopulations. The Scissor algorithm, combined with Olaparib half-maximal inhibitory concentration (IC Results: The Uniform Manifold Approximation and Projection (UMAP) analysis of the single-cell data revealed significant cellular heterogeneity in the CRPC tissues, and the Scissor algorithm identified the epithelial cell subpopulations (Scissor+) strongly linked to Olaparib resistance. The BayesPrism analysis showed that the increased proportion of Scissor+ cells in the bulk samples was significantly associated with a poor prognosis (P=0.04). ST further validated that this subpopulation was predominantly enriched in tumor regions, displaying a clear spatially specific expression pattern. In seven independent bulk RNA-seq cohorts, the ORAG model, which was built using least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF) algorithms, showed excellent prognostic predictive performance [with the highest concordance index (C-index) of 0.722]. Conclusions: This study was the first to systematically identify olaparib resistance-associated tumor epithelial cell subpopulations in CRPC by integrating single-cell, spatial, and bulk multi-omics data. The constructed ORAG risk-prediction model has great potential for clinical application.
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