ArticleCancer research communications2024
Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States.
Article in Cancer research communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Metabolic convergence of diabetes and prostate cancer: from dysglycemia to tumor microenvironment reprogramming.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Clinicogenomic Insights for Progression-Free Survival in Prostate Cancer.International journal of environmental research and public health · 2026Article
- From pathways to prediction: a comparative machine learning framework for prostate cancer survival.Bioinformatics advances · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multidimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from The Cancer Genome Atlas study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer. SIGNIFICANCE: We developed and validated a progression model of prostate cancer using >1,000 tumor and normal tissue samples. The model provided a comprehensive view of prostate tumor evolution and progression.
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