Proteomic identification of potential cancer markers in human urine using subtractive analysis
- Authors:
- Published online on: March 7, 2016 https://doi.org/10.3892/ijo.2016.3424
- Pages: 1921-1932
Abstract
Introduction
Screening of human tissues for cancer biomarkers is an important task in cancer diagnosis and treatment, which is hindered by the complexity of the sample systems studied. A less complex system such as urine is a preferred medium to screen for protein or peptide biomarkers due to the non-invasive sampling of patients, ease of sampling and the unrestricted quantities obtainable. Urine is relatively stable in terms of protein/peptide composition and fragmentation compared with other bodily fluids such as serum, where proteolytic degradation by endogenous proteases has been shown to occur during or after sample collection (1).
Several investigations have been published describing the urinary peptidome and proteome (as well as biomarker discoveries for several diseases) using methodologies ranging from traditional 2D gel electrophoresis alone (2), or coupled with mass spectrometry (2-DE-MS) (3), immunohistochemistry (4), liquid chromatography mass spectrometry (LC-MS) (5), and surface enhanced laser desorption ionisation-time of flight mass spectrometry (SELDI-TOF-MS) (6–9).
The proteomic screening of urine for potential cancer markers has shown several proteins to be differentially present in ovarian cancer (10). Bladder cancer biomarkers constitute a different non-overlapping set of molecules (11–13), as do potential biomarkers for upper gastrointestinal cancers (9). An improvement in the reliability of diagnostic tests is to employ more than one biomarker synchronously (9,14). For example, one previous study employed an antibody-based array of 810 different antibodies to define peptide patterns in urine associated with cancer (15). A different approach was used successfully in recent years, combining urinary mass spectroscopy with protein/peptide pattern analysis to identify kidney disease (16).
There is a clear need to collect and cross-correlate the wealth of data published in the scientific literature. Currently, there are a number of urinary databases available. The majority consist of lists of identified proteins derived from tryptic digests analysed by liquid chromatography tandem mass spectrometry (LC-MS/MS), such as the Max-Planck Unified Proteome Database (MAPU) (17) and Sys-BodyFluid (18). More recently, a urinary database combining chromatographic reverse-phase retention times and m/z values has been established (19).
However, there is no database available which integrates all of the data. In order to fill this gap, we have assembled datasets from 100 urinary proteomic studies in our novel proteomic database termed the Large Scale Screening Resource (LSSR). LSSR is accessible and downloadable through the Proteomic Analysis DataBase (PADB) portal at www.PADB.org.
In this study, we explore the possibility of discovering novel cancer-associated molecular markers in human urine by subtractive analysis using a novel dataset of the human cancer urinary proteome [derived from patients with upper gastrointestinal (GI) cancer] and comparing it to non-cancer urinary datasets.
Materials and methods
Materials
Tris/Tricine peptide gels, gel-running buffers, CM and IMAC resins, and chromatography buffers were from Bio-Rad (Hemel Hempstead, UK). All other chemicals were obtained from Sigma-Aldrich (Gillingham, UK).
Sample collection
Urine samples were obtained from upper GI cancer patients (n=41) and non-cancer controls (n=21) as described previously (9). Summary participant demographics are shown in Table I. Participant age ranged between 21 and 84 (control group), and 43 and 82 (cancer group). Random morning urine samples were collected over a time period of 2 years. Cancer urine samples were collected prior to surgery if the patient was being considered for resection. All procedures were approved by the local research ethics committee, and written informed consent was obtained. The study conformed to the standards set by the Declaration of Helsinki. All urine samples were kept at −40°C for short-term or −80°C for long-term storage.
Chromatographic enrichment of urine proteins and peptides, and sample preparation
Aliquots of 0.5 ml from individual cancer or control urine samples was added to either 30 μl CM10 (n=33 cancer urines, n=8 control urines) or 30 μl IMAC30 (Cu2+-chelated) (n=21 cancer urines, n=19 control urines) spin column resin (Bio-Rad) and 0.75 ml binding buffer (either 0.1 M NaH3C2O2 pH 4.0 for CM resin, or 0.1 M NaHPO4 pH 7.0 including 0.5 M NaCl for IMAC30 resin) and incubated for 1 h at room temperature under constant agitation. Sample and resin combinations were chosen based on independent analyses using peak stratification by SELDI mass spectrometry (9). Unbound material was removed and the resin washed four times with 0.3 ml binding buffer. Bound material was separated by electrophoresis on a 16.5% Tris-Tricine gel (Bio-Rad), and gel bands in the region of 2–10 kDa were excised after Coomassie staining (BioSafe Coomassie; Bio-Rad). The molecular mass range of 2–10 kDa was selected since many urinary proteins are derived from proteolytic processing and urinary shedding as described (20). Additionally, we previously observed potential urinary cancer markers in this mass range (9).
LC-MS/MS mass spectrometry
Proteins and peptides from gel bands were digested in situ with trypsin. The resulting peptides were eluted with acetonitrile (ACN), and analysed by LC-MS/ MS (21). The LC-MS system consisted of an Agilent 1200 Series HPLC (Agilent Technologies, Yarnton, UK) with a Kasil sealed fused silica pre-column (Next Advance, New York, NY, USA) packed to a length of ~3 cm with Pursuit C18, 5 μm particle size (Varian, Crawley, UK) and PicoTip Emitter analytical column PF 360-75-15-N-5 (New Objective, Woburn, MA, USA) packed to a length of ~20 cm with Pursuit C18, 5 μm particle size (Varian). The column was equilibrated with solvent A (0.1% formic acid in 2.5% acetonitrile) and eluted with a linear gradient from 0 to 10% over 6 to 8 min; from 8 to 60% over 8 to 35 min; from 60 to 100% over 35 to 40 min; solvent B (0.1% formic acid, 0.025% TFA in 90% acetonitrile) over 45 min at a flow rate of 5 μl/min. The LTQ mass spectrometer (Thermo Scientific, Epsom, UK) was fitted with a NanoLC ESI source. Data-dependent acquisition was controlled by XCalibur software. Fragmentation spectra were then processed by XCalibur and BioWorks software (Thermo Fisher Scientific, Loughborough, UK) and submitted to the Mascot search engine (Matrix Science, London, UK) using UniProt/SwissProt (release May 2011, Homo sapiens, 18055 sequences) as the reference database. Mascot search parameters were: enzyme specificity trypsin, maximum missed cleavage 1, fixed modifications cysteine carbamidomethylation, variable modification methionine oxidation, precursor mass tolerance +/−3 kDa, fragment ion mass tolerance +/−0.4 kDa. Only Mascot hits with a false discovery rate (FDR) ≤0.05 were taken into consideration.
Meta-analysis and subtractive data analysis
Proteins with at least two peptide matches were analysed further by comparing molecules that were only observed in urine samples from cancer patients with a database consisting of proteins found by other studies in urine, blood and kidney. This database was assembled from 136 publications, listing 146 tissue-specific datasets. The blood datasets covered plasma, serum and erythrocytes; the kidney studies were derived from analyses of cortex, medulla, epithelium, glomerulus, inner medullary collecting duct, mesangium, parenchyma, peroxisomal membrane, peroxisome, basolateral membrane vesicles, brush border membrane vesicles, urothelial mucosa and whole kidney; and urine datasets described either the whole or exosomal proteomes. All entries were then matched to the UniProt database, followed by clustering to individual (unique) entries by annotating splice and variant entries to common parent molecules and ultimately assigning each unique cluster an in-house specific accession number. Additionally, all proteins mapping to immunoglobulins were clustered into one generic cluster, as well as all proteins belonging to the Major Histocompatibility Complex (MHC). Merging and subtraction analysis was done using software written in-house. We also manually added our own functional classification tags to each molecular cluster, based on known properties of each molecule, giving an abridged view of proteome compositions.
Results
Urine samples were extracted from 21 healthy non-cancer controls and 41 patients with upper GI cancer (n=41) (Table I). Of the 41 cancer patients, staging investigations demonstrated that at least 29 (70.7%) had nodal or metastatic disease. We analysed all 62 urine samples by LC-MS/MS in the region of 2–10 kDa by chromatographic enrichment using either CM10, IMAC30, or both resin types individually, resulting in a total of 81 chromatographic enrichments, followed by gel analysis, tryptic digestion and mass spectrometry. All molecular weight regions cut from gels were identical in at least three samples from each cohort group, thus also allowing comparison of identified molecules on a gel-region by gel-region basis After data extraction by Mascot searching (resulting in 35,801 peptides covering 7,639 proteins) and applying discovery criteria of a FDR ≤0.05 and a minimal Mascot score of 13, the resulting 81 datasets were further analysed by merging all protein lists. This yielded 1,228 unique non-redundant entries (data not shown). Additionally, all molecules relating to either immunoglobulins or MHC were also merged into two individual clusters since members of these two families are well known to show a great degree of hypervariability, and therefore they may skew any analysis towards single entries from those classes, since they are not expected to show any duplications across the datasets analysed in this study. The final list consisted of 1,161 molecular clusters. Furthermore, we re-classified all molecules in the datasets available by manually annotating every protein with a single molecular property or functionality tag as listed in the legend of Fig. 1. The properties or functionalities were assigned based on known properties of each individual protein, either from original publications or derived from database annotations, such as enzyme nomenclatures, sequence homologies and domain analysis. The compositional analysis of the merged datasets of blood, urine and kidney proteomes, as well as our urinary dataset is shown in Fig. 2. It was clear that all merged datasets consist of ~25% enzymes, 10% cell-shape molecules, 10% transcriptional or translational elements and 10% transport molecules. However, our novel dataset appeared to contain more cell-shape and transcriptional/translational proteins and less transport molecules, which may reflect an association with disease, rather than a general breakdown of cellular components.
The 1,161 molecules were then split into groups depending on whether they were observed in cancer urine samples, or urine from healthy individuals (Fig. 3A). The 745 proteins only found in cancer urine samples were then tagged and the entire dataset compared to data of 31,743 unmerged entries derived from 146 tissue-specific datasets from 137 publications (data not shown). This external data consisted of 9,707 merged entries, covering proteomic studies from urine, kidney and blood (Table II). A comparative analysis of our dataset with the three largest urinary proteome profiling datasets showed a 46% overlap of our data with the dataset from Kentsis et al (22), a 41% overlap with the study by Adachi et al (23), and a 21% with the urinary exosome dataset from Gonzales et al (24) (Fig. 3B). A global comparison between proteomes from urine, kidney and blood (Fig. 3C) demonstrated a slightly larger overlap of the urinary proteome with the kidney proteome than the blood proteome.
Table IINumber of entries listed in the LSSR database for analysed samples derived from blood, urine and kidney. |
We then performed subtractive analysis on our urinary proteome data by eliminating any potential cancer candidate molecule if it was found in any of the urinary datasets unrelated to cancer. This reduced dataset of 268 proteins (data not shown) was further condensed by removing any entries which did not have a spectral count of at least two, resulting in 44 proteins, of which 24 were found uniquely in our study (in comparison to all other datasets), and 20 which were also found in the other tissues (Table III). All 44 of these proteins were then analysed by searching the Online Mendelian Inheritance in Man (OMIM) database for publications where these molecules were reported to be directly associated with human disease or cancer. Fourteen proteins were annotated in OMIM to be causative for a disease, and 30 were known to be involved in cancer.
Discussion
Proteomic large-scale analysis of tissues to define a cancer state can be time- and resource-consuming, especially in light of an unknown end-point. Therefore, it could be helpful to compare a novel dataset with known data in order to establish whether potential disease markers are observable, and thereby analyse a simplified dataset for the disease in question. This approach does not address the issue of quantitative comparisons, but it is rather a qualitative approach. However, the resulting list of potential candidate molecules will have a specificity of 100%. Here, we test this hypothesis by applying a subtractive analysis method in conjunction with large-scale meta-analysis of urinary datasets to screen for potential novel cancer markers observable in human urine.
An initial comparison of functional profiles of urine, blood and kidney proteomes showed no major discernible difference between those datasets. This finding, in itself is not surprising, since it is expected that these systems should reflect an overall similar composition through a combination of immediate environment and source. Blood, containing a substantial amount of cells, is also expected to show a reasonably uniform functional composition profile compared with other tissues e.g. kidney. Our novel urinary dataset, having an expected bias towards an aberrant functional profile due to overexpressed molecules associated with disease, contains more molecules involved in cellular contacts, morphology and cytoskeletal aspects, as well as transcriptional/translational components, which may be directly linked to abnormal and uncontrolled cellular growth.
Comparison of our dataset with known non-cancer urinary proteomes yielded a set of only 44 molecules specific for our cancer data, of which 68% are already known to be involved in cancer. The functional profile of those 44 proteins in comparison to the merged urinary proteome profile showed mainly an enrichment of developmental proteins (5%), signaling molecules (7%) and, most strikingly, transcriptional/ translational proteins (20%). The known cancer-associated molecules described have been suggested to be involved in hepatocellular carcinoma [κ actin (POTEKP) (25); BolA-like protein 2 (BOLA2) (26); fragile X mental retardation 1 protein (FMR1) (27)]; mammary carcinogenesis [polypeptide N-acetylgalactosaminyltrans ferase 6 (GALNT6) (28); protein Daple (CCDC88C) (29); G-protein-signaling modulator 2 (GPSM2) (30); phospho-lipase DDHD2 (DDHD2) (31); downstream of tyrosine kinase 7 (DOK7) (32); suppressor of tumorigenicity 14 protein (ST14) (33); coronin-1A (CORO1A) (34)], lung cancer [tight junction protein ZO-1 (TJP1) (35)], prostate cancer [phos-pholipase A1 member A (PLA1A) (36); transcriptional enhancer factor TEF-4 (TEAD2) (37); nuclear receptor corepressor 1 (NCOR1) (38)], ovarian cancer [A-kinase anchor protein 2 (AKAP2) (39)], colorectal cancer [sterile α and TIR motif-containing protein 1 (SARM1) (40); neuron navigator 2 (NAV2) (41); histone-lysine N-methyltransferase MLL3 (MLL3) (42)], pancreatic cancer [pleckstrin homology domain-containing family G member 2 (PLEKHG2) (43); glial cell line-derived neurotrophic factor (GDNF) (44); carboxypeptidase B (CPB1) (45); α-actinin-2 (ACTN2) (46)], gastric cancer [mRNA-decapping enzyme 1A (DCP1A) (47), a co-activator in TGF-β signaling (48)], melanoma [DNA polymerase α subunit B (POLA2) (49)], multiple myeloma [TEL2-interacting protein 1 homolog (TTI1) (50)], endometrial cancer cells [Histone H1.4 (HIST1H1E (51)], laryngeal squamous cell carcinoma [protocadherin-17 (PCDH17) (52)], and adenocarcinoma [microsomal triglyceride transfer protein large subunit (MTTP) (53)]. Additionally, the latter protein was also described to be a pivotal element in the cancer-associated muscle-wasting disease cachexia (54). Some of these proteins may be differentially regulated across a range of different cancer types and may therefore represent key cancer markers. For example, receptor tyrosine-protein kinase erbB-2 (ERBB2) has been described to be a marker for various cancer types, such as gastroesophageal (55), breast (56), lung (57), gallbladder (58) and pancreatic cancer (59), as well as uterine serous adenocarcinoma (60), and others. Another known protein to be involved in cancer progression is the mitochondrial cytochrome c oxidase subunit 4 isoform 2 (COX4I2), which is part of the Warburg effect, where cancer cells show higher propensity to produce lactate independent of oxygen presence or absence (61).
Of the proteins not previously described in association with cancer, transcription factor Bax antagonists selected in Saccharomyces 1 (SON), homeobox protein Mohawk (MKX) and CUGBP Elav-like family member 5 (CELF5) may represent other potential lead candidates in cancer stratification. Other important markers may include developmental molecules, such as guanylate-binding protein 4 GBP4, which is a negative regulator of virus-triggered cellular responses (62) and is involved in GTP hydrolysis, or neuron navigator NAV1, which has been reported to be a neuronal guidance molecule (63). However, its role in cancer or outside the neuronal environment remains to be elucidated.
In conclusion, we have demonstrated that a subtractive analysis of proteomic datasets can yield a number of potential diagnostic cancer targets in human urine. Further specific screening of urine, based on our findings, using, for example, an antibody-based approach, will establish whether our potential markers are associated with a general cancer status, or if they are specific for a defined cancer type such as pancreatic or esophageal cancer. Additionally, since the data in our database can easily be expanded to contain further datasets, there are other, as yet undefined diseases, which can be addressed by establishing and comparing a relatively small disease-specific dataset. This approach also has the advantage of rapid turnover and increased cost-effectiveness relating to large-scale analyses of tissue and cell proteomes for the discovery of novel molecular markers. In this regard, we are encouraging researchers to submit their published datasets to be incorporated in the LSSR database. All data will be freely available through the PADB portal at www.PADB.org.
Acknowledgements
We thank C.A. Greig, N.A. Stephens and H. Wackerhage for patient recruitment. Funding of this study was provided by the University of Edinburgh.
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