Urinary RNA Biomarkers in Extracellular Vesicles from Post-DRE Urine to Predict Aggressive Prostate Cancer Open Access

Abstract

The concentration of Prostate Specific Antigen (PSA) in blood serum is the current diagnostic method of choice in detecting prostate cancer. However, between concentrations of 2.5 ng/mL and 10 ng/mL, the PSA test is a poor predictor of prostate cancer. During this project, mRNA was extracted from extracellular vesicles in post-Digital Rectal Exam (DRE) urine from 659 male patients who were about to undergo a prostate biopsy due to suspicion of prostate cancer. The mRNA was then processed and the expression levels of over 20,000 genes was quantified using a microarray gene chip. A differential gene expression analysis was conducted on the samples to identify potential genes of interest. These gene lists, and their expression levels, were then used to train elastic net models to identify aggressive prostate cancer. The models significantly outperformed PSA according to a DeLong test (p < 0.05). Logistic combination with other diagnostic methods, like the Prostate Biopsy Collaborative Group (PBCG) risk calculator and an elastic net model using only the expression levels of PCA3 and ERG, yielded significant performance improvements. This model significantly outperformed PBCG, PSA, and the PCA3/ERG elastic net model according to a DeLong test (p < 0.05) and achieved an Area Under Curve (AUC) of 0.798 and specificities of 0.49 and 0.40 at sensitivities of 90% and 95%, respectively.

Table of Contents

Introduction 1

Prostate Cancer Biology 1

Diagnostic Methods 3

Treatments 5

Research Goals 7

Methodology 10

Urine Acquisition and Processing 10

mRNA Reading and Normalization 13

Selecting Significant Genes 17

Model Creation 19

Model Selection and Validation 22

Results 27

Discussion 36

Conclusion 38

Acronym Table 40

Citations 41

About this Honors Thesis

Rights statement

• Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Emory College
Department	Chemistry
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Biology, Bioinformatics Biology, Biostatistics Biology, Genetics
Keyword	Prostate AI Urine Cancer mRNA Machine Learning GeneChip
Committee Chair / Thesis Advisor	Dr. Carlos Moreno, Emory University
Committee Members	Dr. David Lynn, Emory University Dr. Emily Wall, Emory University

Last modified

Primary PDF

Thumbnail	Title	Date Uploaded	Actions
	Urinary RNA Biomarkers in Extracellular Vesicles from Post-DRE Urine to Predict Aggressive Prostate Cancer ()	2024-04-02 20:40:19 -0400	Press to Select an action Download

Supplemental Files

Thumbnail	Title	Date Uploaded	Actions