Diagnostics (Basel). 2019 Dec 11;9(4). pii: E219. doi: 10.3390/diagnostics9040219.
A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer.
Hamzeh O1, Alkhateeb A1, Zheng JZ1, Kandalam S2, Leung C3, Atikukke G4, Cavallo-Medved D2, Palanisamy N5, Rueda L1.
- 1
- School of Computer Science, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada.
- 2
- Department of Biomedical Sciences, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada.
- 3
- Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St, London, ON N6A 5C1, Canada.
- 4
- ITOS Oncology Inc., 1453 Prince Rd, Ste: 4125, Windsor, ON N9C 3Z4, Canada.
- 5
- Department of Urology, Henry Ford Health System, One Ford Place, Detroit, MI 48202, USA.
Abstract
(1) Background:One of the most common cancers that affect North American men and men worldwide is prostate cancer. The Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. Advancements in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason scores more accurately and effectively. (2) Methods: In this study, we used a novel machine learning method to analyse gene expression of prostate tumours with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository, and categorised patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as nodes, was developed to identify and predict nodes based on their mRNA or gene expression. In each node, patient samples were analysed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome from analysis of each node was a set of genes that could differentiate each Gleason group from the remaining groups. To validate the proposed method, the set of identified genes were used to classify a second dataset of 499 prostate cancer patients collected from cBioportal. (3) Results: The overall accuracy of applying this novel method to the first dataset was 93.3%; the method was further validated to have 87% accuracy using the second dataset. This method also identified genes that were not previously reported as potential biomarkers for specific Gleason groups. In particular, PIAS3 was identified as a potential biomarker for Gleason score 4 + 3 = 7, and UBE2V2 for Gleason score 6. (4) Insight: Previous reports show that the genes predicted by this newly proposed method strongly correlate with prostate cancer development and progression. Furthermore, pathway analysis shows that both PIAS3 and UBE2V2 share similar protein interaction pathways, the JAK/STAT signaling process.
KEYWORDS:
Gleason score detection; classification; next generation sequencing; prostate cancer; supervised learning; transcriptomics
- PMID:
- 31835700
- DOI:
- 10.3390/diagnostics9040219
