Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression
Abstract
1. Introduction
2. Results
2.1. Dysregulation of Stemness-Associated Genes Across Multiple PCa Comparisons
2.2. Association of Stemness Markers with PCa Patients’ Survival
2.3. Modeling a Stemness-Associated Signature with Prognostic Value
2.4. Consistent Performance Across Validation Datasets
2.5. The Stemness-Associated Gene Signature Captures Neuroendocrine Disease Heterogeneity in the MDA PCa PDX Series
2.6. Our Stemness Score Adds Value to Pre-Existing NEPC Score
2.7. The Seven-Gene Signature Effectively Classifies Large-Cell Neuroendocrine Carcinomas
3. Discussion
4. Materials and Methods
4.1. Stemness-Associated Genes
4.2. Gene Expression Analyses in Human Patients
4.2.1. Dataset Selection Criteria
4.2.2. Differential Gene Expression Analyses
4.3. Association Between Gene Expression and Patients’ Outcomes
4.3.1. Dataset Selection Criteria
4.3.2. Survival Analyses
4.4. Selection of Candidate Genes for Modeling a Risk Score
4.5. Gene Signature and Risk Score Calculation
4.6. Transcriptome Analysis of MDA PCa PDX Series
4.7. Unsupervised Clustering and Principal Component Analysis (PCA)
4.8. Receiver Operating Characteristic (ROC) Curve for NEPC Classification
4.9. NEPC Patients’ Samples Dataset
4.10. Statistical Analyses
5. Conclusions
Limitations
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Samples |
---|---|
GSE35988 [46] | Localized PCa (n = 59), matched benign prostate tissues (n = 28), and metastatic CRPC (n = 35). |
GSE3933 [47,48] | Localized PCa (n = 62) and normal prostate (n = 41). |
GSE46602 [49] | PCa (n = 36) and benign tissue (n = 14). |
GSE6956 [50] | Primary PCa (n = 69) and normal adjacent prostate (n = 18). |
GSE70768 [51] | Primary PCa (n = 112), benign tissue (n = 74) and CRPC (n = 13). |
TCGA-PRAD [52] | Primary PCa (n = 497) and normal adjacent tissue samples (n = 51). |
GSE21034 [53] | Primary PCa (n = 131) and metastatic tissue samples (n = 19). |
Dataset | Samples | Survival Endpoint | Covariates | Cohort |
---|---|---|---|---|
TCGA-PRAD [52] | 497 PCa (RNAseq) | Disease Progression Disease-Free Time (n = 337) | Gleason Group, PSA levels, Clinical T Stage, Targeted Molecular/Radiation Therapy | Training |
GSE70768 [51] | 111 PCa (Microarray) | Biochemical Relapse | Age, Gleason Group, PSA levels, T Stage | Training |
GSE70769 [51] | 92 PCa (Microarray) | Biochemical Relapse | Gleason Group, PSA levels, T Stage | Training |
GSE116918 [56] | 248 PCa (Microarray) | Metastasis Development Relapse | Age, Gleason Score, PSA levels, T Stage | Training |
GSE16560 [57] | 281 PCa (Microarray) | Death | Age, Gleason Group | Training |
GSE54460 [58] | 106 PCa (RNA-seq) | Biochemical Relapse | Age, Gleason Score, PSA levels, T Stage | Validation |
GSE94767 [59] | 233 PCa (Microarray) | Biochemical Relapse | Gleason Group, PSA levels, T Stage | Validation |
DKFZ [60] | 81 PCa (RNA-seq) | Biochemical Relapse | Age, Gleason Score, PSA levels, T Stage | Validation |
SU2C-PCF [61] | 81 metastatic CRPC (RNA-seq) | Death | Age, Gleason Score, PSA levels | Validation |
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Sabater, A.; Sanchis, P.; Seniuk, R.; Pascual, G.; Anselmino, N.; Alonso, D.F.; Cayol, F.; Vazquez, E.; Marti, M.; Cotignola, J.; et al. Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression. Int. J. Mol. Sci. 2024, 25, 11356. https://doi.org/10.3390/ijms252111356
Sabater A, Sanchis P, Seniuk R, Pascual G, Anselmino N, Alonso DF, Cayol F, Vazquez E, Marti M, Cotignola J, et al. Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression. International Journal of Molecular Sciences. 2024; 25(21):11356. https://doi.org/10.3390/ijms252111356
Chicago/Turabian StyleSabater, Agustina, Pablo Sanchis, Rocio Seniuk, Gaston Pascual, Nicolas Anselmino, Daniel F. Alonso, Federico Cayol, Elba Vazquez, Marcelo Marti, Javier Cotignola, and et al. 2024. "Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression" International Journal of Molecular Sciences 25, no. 21: 11356. https://doi.org/10.3390/ijms252111356
APA StyleSabater, A., Sanchis, P., Seniuk, R., Pascual, G., Anselmino, N., Alonso, D. F., Cayol, F., Vazquez, E., Marti, M., Cotignola, J., Toro, A., Labanca, E., Bizzotto, J., & Gueron, G. (2024). Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression. International Journal of Molecular Sciences, 25(21), 11356. https://doi.org/10.3390/ijms252111356