Urinary Proteomics and Metabolomics Studies to Monitor Bladder Health and Urological Diseases

Zhaohui Chen; Jayoung Kim


BMC Urol. 2016;16(11) 

In This Article


Personalized medicine aims for a customized healthcare for each patient to match treatments with the right patients at the perfect timing. Gene-specific data (SNP genotyping as well as epigenetics) is too static to enable such timed treatments. It is therefore essential to collect variable biomarker, along with other clinical information, data to achieve accurate diagnostic assessment for individual patients.[1–3] Multi-omic readouts of cellular and organ phenotypes (RNA-Seq, proteomics and metabolomics) will be indispensible in the era of personalized medicine. Only through a combination of exact genotypic and molecular phenotypic information we will improve the development of custom and precision therapies.[4–6] Sub-grouping of patients is necessary to define the evidence-based protocol for matching treatments to the right patients with appropriate timing.[5,7] The necessity of compiling molecular information and clinical outcomes in personalized medicine prompted us to believe that the use of multi-omic data in conjunction with clinical outcome data is ever more important not only at the time of medical intervention, but throughout patients' lives. The need for and possibilities associated with big data approach to gain insight into biological processes driving diseases and to identify novel diagnostics is enlarging. In this review, we will discuss how far metabolomic and proteomic approaches have come to aid in this long-term goal.

Urological diseases including urological cancers and benign bladder dysfunctions are complex in nature and require powerful, precise treatments. Tests to find patient candidates for a specific or combination of therapy and to identify biomarkers are incredibly challenging to determine.[6,8,9] Urine contains information not only from the urinary track, but also from other organs, providing biomarkers for bladder and other systemic diseases.[10–12] Looking at urine data in conjunction with other available patient clinical data may enable us to understand the molecular signature, which helps monitor the stages of the diseases and responses to therapies. This is particularly true in urological diseases, where urine samples provide the primary window for diagnosis and drug behavior observation.[13]

A common definition of the proteome is the entire set of proteins expressed by a cell, tissue or organism at a certain time. Since proteomics is the large-scale study of proteome, it can contribute to expanding the understanding of biological systems and functions in cells or organs. Proteomes are directly responsible for cell functions, and therefore, abnormal protein expression is an indication of cellular disruption due to the pathological conditions.[14,15] Current global proteomic technologies may provide a comprehensive understanding of urological diseases, characteristics of the disease's state, and novel approaches to relieve the clinical symptoms.[16–18]

Metabolomics provides a global chemical fingerprint of the metabolism of cells and indicates physiological and pathological states of biological samples.[19–21] Thus, the power of metabolomics opens up an unparalleled opportunity to query the molecular mechanisms of the disease. Metabolites are not merely the end products of gene/protein expression, rather, they are the result of the interactions of the genome and proteome with their environment in the cells. They play as powerful mediators of cellular events both in long-distance actions (e.g. hormones), stress and physiological actors (e.g. oxylipins)[22] and as cell-internal mediators (e.g. α-ketoglutarate in pluripotency).[23] Thus, analyzing metabolic differences between pathological and normal conditions could provide undiscovered insights into the underlying disease pathology.

In addition to the advancements in multi-omics data acquisitions, novel bioinformatics methods enable an integrated view to identify the combined action of biomarkers as well as to develop drugs.[24–27] A significant volume of data with various omics data, including genetic, epigenetic, transcriptomic, proteomic, metabolomic and clinical outcome data, provides researchers with the capability to see a broader perspective and make discoveries that couldn't previously be delivered.[28–31] Integrative approaches have become the essential part of experimental designs aimed at better understanding the biology of bladder diseases.

The main goal of this article is to provide the reader with an up-to-date summary of the main molecular variations taking place in biofluids with respect to various urological diseases including urological cancers (e.g., prostate cancer (hereafter PCa) and bladder cancer (BCa)) and benign bladder dysfunctions (e.g., benign prostatic hyperplasia (BPH), interstitial cystitis/pelvic bladder syndrome, bladder pain syndrome (IC)), as well as of the analytical strategies employed to unveil urinary biomarkers.

We here focus on mainly two omics analyses—proteomics and metabolomics—and associated data integration strategies. These approaches enable researchers to: (a) identify unknown molecular mechanisms; (b) select molecular markers that can be used for drug discovery, preclinical, and clinical drug development; (c) develop diagnostic tools. First, we present a short review on the urine-based studies. Second, we discuss analytical techniques that are used in urinary omics analyses, including computational methods for data processing. Next, we present studies that have used proteomics or metabolomics approaches to reveal the fingerprints of urological diseases. Finally, we discuss the future research directions and prospective how to apply to diagnosis and precision medicine for patients to summarize the review.