Document Type
Article
Publication Title
Cancer Informatics
Publication Date
3-1-2019
Volume
18
Keywords
machine learning, prostate cancer progression, RNA-Seq analysis, transcriptomics signature
DOI
10.1177/1176935119835522
Abstract
Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers. Our study also supports PTGFR, NREP, SCARNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease.
E-ISSN
11769351
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Alkhateeb, Abedalrhman; Rezaeian, Iman; Singireddy, Siva; Cavallo-Medved, Dora; Porter, Lisa A.; and Rueda, Luis. (2019). Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer. Cancer Informatics, 18.
https://scholar.uwindsor.ca/biomedpub/5