Cancer management and research · 2026
A Platform-Independent Binary Gene-Pair Signature Derived from CRPC-Enriched Single-Cell Transcriptomics for Predicting Recurrence-Free Survival in Prostate Cancer.
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He Yu, Li Boyang, Tan Zhaojie, Jiang Kuanyao, Ju Wenyue, Yu Weidong, Zhang Meng, Liang Chaozhao
Аннотация
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 TP53 mutation frequency and greater tumor mutational burden, and shared significant transcriptional similarities with responders to anti-PD-1 immunotherapy. These immunotherapy-related findings are hypothesis-generating and require prospective validation. Virtual knockout identified CKS2 as a risk-associated candidate gene linked to an androgen-responsive network, suggesting CKS2's potential role in the molecular reprogramming associated with PCa progression.
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 CKS2 emerges as a prognostic hub warranting validation.
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 TP53 mutation frequency and greater tumor mutational burden, and shared significant transcriptional similarities with responders to anti-PD-1 immunotherapy. These immunotherapy-related findings are hypothesis-generating and require prospective validation. Virtual knockout identified CKS2 as a risk-associated candidate gene linked to an androgen-responsive network, suggesting CKS2's potential role in the molecular reprogramming associated with PCa progression.
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 CKS2 emerges as a prognostic hub warranting validation.