ArticleCancer management and research2026
A Platform-Independent Binary Gene-Pair Signature Derived from CRPC-Enriched Single-Cell Transcriptomics for Predicting Recurrence-Free Survival in Prostate Cancer.
Article in Cancer management and research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Recurrence-free survival (RFS) following radical prostatectomy is a pivotal measure of therapeutic success in prostate cancer (PCa), yet conventional clinicopathological tools offer limited discriminative accuracy. We sought to construct a platform-independent prognostic signature to predict RFS by capturing early molecular traces of advanced disease potential. Methods: Single-cell RNA sequencing data were analyzed to identify malignant epithelial subclusters and evaluate their compositional changes during the transition to castration-resistant prostate cancer (CRPC). We benchmarked 12 machine learning algorithms and 104 algorithmic combinations to develop a robust binary gene-pair signature in TCGA-PRAD cohort (n = 493) and validated in five external cohorts (n = 694). Downstream analyses included functional enrichment, immune and mutational profiling, drug sensitivity prediction and virtual knockouts. Results: A 36-gene-pair signature was established, showing robust performance in predicting RFS across five external validation cohorts, with an average C-index of 0.725. Distinct signatures in signaling and metabolic processes were identified between the two risk groups through enrichment analysis. High-risk patients exhibited an immune-inflamed microenvironment with elevated Conclusion: The 36-gene-pair binary signature provides robust RFS risk stratification. High-risk individuals exhibit transcriptional similarity to reported anti-PD-1 therapy responders, and
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