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Trends in Online Biomonitoring Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Predicting Tumor Locations in Prostate Cancer Tissue Using Gene Expression

Authors : Osama Hamzeh, Abedalrhman Alkhateeb, Luis Rueda

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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Abstract

Prostate cancer can be missed due to the limited number of biopsies or the ineffectiveness of standard screening methods. Finding gene biomarkers for prostate cancer location and analyzing their transcriptomics can help clinically understand the development of the disease and improve treatment efficiency. In this work, a classification model is built based on gene expression measurements of samples from patients who have cancer on the left, right, and both lobes of the prostate as classes.
A hybrid feature selection is used to select the best possible set of genes that can differentiate the three classes. Standard machine learning classifiers with the one-versus-all technique are used to select potential biomarkers for each laterality class. RNA-sequencing data from The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) was used. This dataset consists of 450 samples from different patients with different cancer locations. There are three primary locations within the prostate: left, right and bilateral. Each sample in the dataset contains expression levels for each of the 60,488 genes; the genes are expressed in Transcripts Per Kilobase Million (TPM) values.
The results show promising prediction prospect for prostate cancer laterality. With 99% accuracy, a support vector machine (SVM) based on a radial basis function kernel (SVM-RBF) was able to identify each group from the others using the subset of genes. Three groups of genes (RTN1, HLA-DMB, MRI1 and others) were found to be differentially expressed among the three different tumor locations. The findings were validated using multiple findings in the literature, which confirms the relationship between those genes and prostate cancer.

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Metadata
Title
Predicting Tumor Locations in Prostate Cancer Tissue Using Gene Expression
Authors
Osama Hamzeh
Abedalrhman Alkhateeb
Luis Rueda
Copyright Year
2018
Book
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-319-78722-0

Electronic ISBN: 978-3-319-78723-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-78723-7_29

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