AI Article Synopsis
- * This study utilized mutation data from the TCGA database to develop refined profiles using a random walk algorithm, which were then analyzed with Graph-regularized Nonnegative Matrix Factorization to categorize patients.
- * The findings highlighted the link between mutation profiles and clinical outcomes, suggesting that classifying prostate cancer by these profiles could lead to improved treatment strategies in the future.
Article Abstract
Prostate cancer is one of the most common cancers in men and a leading cause of cancer death worldwide, displaying a broad range of heterogeneity in terms of clinical and molecular behavior. Increasing evidence suggests that classifying prostate cancers into distinct molecular subtypes is critical to exploring the potential molecular variation underlying this heterogeneity and to better treat this cancer. In this study, the somatic mutation profiles of prostate cancer were downloaded from the TCGA database and used as the source nodes of the random walk with restart algorithm (RWRA) for generating smoothed mutation profiles in the STRING network. The smoothed mutation profiles were selected as the input matrix of the Graph-regularized Nonnegative Matrix Factorization (GNMF) for classifying patients into distinct molecular subtypes. The results were associated with most of the clinical and pathological outcomes. In addition, some bioinformatics analyses were performed for the robust subtyping, and good results were obtained. These results indicated that prostate cancers can be usefully classified according to their mutation profiles, and we hope that these subtypes will help improve the treatment stratification of this cancer in the future.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5429686 | PMC |
| http://dx.doi.org/10.1038/s41598-017-00872-8 | DOI Listing |
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