A Platform-Independent Binary Gene-Pair Signature Derived from CRPC-Enriched Single-Cell Transcriptomics for Predicting Recurrence-Free Survival in Prostate Cancer
Jul 2026· Cancer Management and Research· Vol 18, pp. 1-18· 0 citations· 45 references
Medicine
TL;DR
The 36-gene-pair binary signature provides robust RFS risk stratification, and high-risk individuals exhibit transcriptional similarity to reported anti-PD-1 therapy responders, and CKS2 emerges as a prognostic hub warranting validation.
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 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.
Background: Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide, highlighting the need for improved prognostic tools. The integration of artificial intelligence (AI) and machine learning (ML) with multi-omics data offers new opportunities for biomarker discovery and risk stratification. Methods: We integrated bulk transcriptomic data from GSE116918 (training, n = 248) and three cross-cohort consistency evaluation cohorts (TCGA-PRAD, GSE70769, GSE46602), focusing on 1087 epithelial–mesenchymal transition (EMT)-associated genes. Using consensus clustering, weighted gene co-expression network analysis (WGCNA), and 91 machine learning algorithm combinations (including Random Forest, Lasso, and CoxBoost), we constructed a prognostic signature. SHAP analysis was used for model interpretability. Single-cell RNA sequencing (scRNA-seq, GSE268307, 10,672 cells) and spatial transcriptomics (10× Genomics Visium FFPE) provided hypothesis-generating evidence; spatial analysis was based on one tissue section. Results: A three-gene signature (INHBA, FAP, ITGBL1) effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly worse metastasis-free survival (HR = 1.61, 95% CI: 1.39–1.87; 4-year AUC = 0.93 in the training cohort; external AUCs ranged from 0.62 to 0.77). CytoTRACE inferred high differentiation potential of COMP+ fibroblasts, and Monocle3 inferred a transcriptional transition from COMP+ toward NELL2+ fibroblasts. BayesPrism deconvolution suggested that high inferred COMP+ fibroblast abundance was associated with poor prognosis and advanced T stage. NicheNet analysis prioritized BMP7 as a key upstream ligand, with downstream targets enriched in TGF-β signaling and stem cell pluripotency pathways. Conclusions: This study presents a machine learning-based multi-omics framework for prostate cancer risk stratification. The three-gene signature provides a new exploratory prognostic model while inferring a COMP+ to NELL2+ transcriptional transition. These findings may inform future hypothesis-driven studies of treatment sensitivity, pending experimental validation, and demonstrate the value of AI-driven multi-omics integration for precision oncology.
A READ-derived response-associated gene signature for recurrence stratification and exploratory cross-cohort evaluation in additional colorectal cancer cohorts, while further exploring its association with treatment-response phenotypes.
Shuai Li, Jingxian Li, Xianyue Bu et al.· Human Cell· 0 citations
The 36-gene signature developed in this study demonstrates favorable predictive performance and stability across both the training and independent Chinese cohorts, and underscores the critical roles of energy metabolism and ECM remodeling in NMIBC recurrence.
Liyuan Dong, Yun Peng, Yuxuan Song et al.· Beijing da xue xue bao. Yi x...· 0 citations
Background Prostate cancer (PCa) is the second most prevalent malignancy in men worldwide, and accurate stratification of biochemical recurrence (BCR) risk remains challenging using conventional clinicopathological parameters alone. Identification of robust molecular biomarkers and integrated prognostic models is therefore of high clinical priority. Methods RNA-seq count data and clinical annotations for 554 TCGA-PRAD samples were obtained and normalized to log2(CPM+1). Weighted gene co-expression network analysis (WGCNA) identified co-expression modules correlated with Gleason score, PSA, and pathologic T stage. Protein-protein interaction (PPI) network analysis with CytoHubba topological scoring defined consensus hub genes. Four machine learning algorithms - LASSO Cox regression, random forest, SVM, and XGBoost - were applied to construct and validate a prognostic risk model. Immune cell infiltration was quantified and a prognostic nomogram was constructed and evaluated by decision curve analysis. Hub gene expression was experimentally validated by qRT-PCR and ELISA in prostate cancer and normal prostatic epithelial cell lines. Results Five hub genes - EZH2, CDK1, AURKA, TOP2A, and CCNB1 - were identified within the turquoise WGCNA module, which showed the strongest correlations with Gleason score (r = 0.78), PSA (r = 0.68), and pathologic T stage (r = 0.62). LASSO Cox regression and random forest consensus selected EZH2, CDK1, and AURKA for a three-gene risk score (Risk Score = 0.312xEZH2 + 0.285xCDK1 + 0.241xAURKA). High-risk patients demonstrated markedly inferior BCR-free survival (HR = 3.21, 95% CI: 2.05–5.03; log-rank P < 0.0001), with time-dependent AUCs of 0.821, 0.842, and 0.836 at 1, 3, and 5 years, respectively. Multivariate Cox regression confirmed the risk score as an independent prognostic factor (HR = 2.87; P < 0.001). A nomogram integrating the risk score with clinical parameters showed superior net benefit by decision curve analysis. Hub-high tumors exhibited reduced CD8+ T cell infiltration, elevated M2 macrophage abundance, and upregulated immune checkpoints (PD-L1, CTLA4, TIM-3, LAG3). All hub genes were confirmed overexpressed at both mRNA and protein levels in PCa cell lines by qRT-PCR and ELISA. Conclusion EZH2, CDK1, and AURKA constitute an internally validated prognostic risk signature in PCa that links cell cycle dysregulation to an immunosuppressive tumor microenvironment. This signature provides clinically actionable risk stratification and highlights candidate therapeutic targets in prostate cancer.
Gu-Quan Chen, Jie-Feng Zhang, Lin-Fu Zhao et al.· Frontiers in Genetics· 0 citations
A 13-gene prognostic cluster associated with poor survival in EGFR-mutant LUAD is identified and the EGFR-regulated USP15/KRT17 axis is position as a novel framework for patient risk stratification, with potential implications for guiding targeted therapeutic strategies.
Jiamin Huang, Yi Huang, Qiurong Yuan et al.· Respiratory Research· 0 citations
Triple-negative breast cancer (TNBC) exhibits pronounced molecular heterogeneity, yet the majority of published transcriptomic prognostic signatures suffer from limited reproducibility and have not achieved clinical translation. We systematically benchmarked 62 published TNBC prognostic signatures across 6 independent cohorts (n=1,357) using a unified analytical framework spanning multiple scoring algorithms, survival endpoints, and threshold strategies. While 17 signatures demonstrated consistent univariate prognostic associations, only 4 remained independently prognostic after adjustment for clinicopathological variables, and none achieved robustness across all analytical conditions-underscoring the fragility of existing classifiers. Leveraging genes with concordant survival associations across all 6 discovery cohorts, we identified a reproducible 15-gene directionally concordant gene set (DCGS) signature and distilled it into MetaSig-EFS, a 13-gene prognostic model optimized using a cohort-aware DeepSurv framework. MetaSig-EFS demonstrated robust cross-cohort generalizability, achieving validation concordance indices of 0.89 in GSE19615 and 0.69 in JBordet, with corresponding 3- and 5-year time-dependent AUROCs of 0.89 and 0.96 in GSE19615 and 0.80 and 0.71 in JBordet, respectively, while retaining independent prognostic value across established TNBC molecular sub-typing systems. Leveraging the TAHOE-100M transcriptomic perturbation atlas, we systematically prioritized candidate therapeutics through large-scale drug repurposing, identifying Paclitaxel as the top-ranked compound, followed by Venetoclax and Tucatinib. Together, these findings provide biologically informed and clinically actionable therapeutic hypotheses that extend the translational utility of our validated prognostic framework for TNBC risk stratification.
L. Dhingra, Manulaya Singh, Simran Jit et al.· bioRxiv· 0 citations