Dataset Information

Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients.

ABSTRACT: Seminal plasma, because of its proximity to prostate, is a promising fluid for biomarker discovery and noninvasive diagnostics. In this study, we investigated if seminal plasma proteins could increase diagnostic specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 147 most promising biomarker candidates, we combined proteins identified through five independent experimental or data mining approaches: tissue transcriptomics, seminal plasma proteomics, cell line secretomics, tissue specificity, and androgen regulation. A rigorous biomarker development pipeline based on selected reaction monitoring assays was designed to evaluate the most promising candidates. As a result, we qualified 76, and verified 19 proteins in seminal plasma of 67 negative biopsy and 152 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which predicted prostate cancer on biopsy and outperformed age and serum Prostate-Specific Antigen (PSA). A machine-learning approach for data analysis provided improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an in-house immunoassay, TGM4 protein was upregulated 3.7-fold (p = 0.006) and revealed AUC = 0.66 for detecting prostate cancer on biopsy for patients with serum PSA ?4 ng/ml and age ?50. Very low levels of TGM4 (120 pg/ml) were detected in blood serum. Collectively, our study demonstrated rigorous evaluation of one of the remaining and not well-explored prostate-specific proteins within the medium-abundance proteome of seminal plasma. Performance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.

SUBMITTER: Drabovich AP

PROVIDER: S-EPMC6731075 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients.

Drabovich Andrei P AP Saraon Punit P Drabovich Mikalai M Karakosta Theano D TD Dimitromanolakis Apostolos A Hyndman M Eric ME Jarvi Keith K Diamandis Eleftherios P EP

Molecular & cellular proteomics : MCP 20190627 9

Seminal plasma, because of its proximity to prostate, is a promising fluid for biomarker discovery and noninvasive diagnostics. In this study, we investigated if seminal plasma proteins could increase diagnostic specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 147 most promising biomarker candidates, we combined proteins identified through five independent experimental or data mining approaches: tissue transcriptomics, seminal plasm ...[more]

PMID: 31249104

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Project description:BackgroundTranslational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary.MethodsWe developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers.ResultsWe applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063-1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results.ConclusionsWe developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

Project description:BackgroundExosomes or extracellular vesicles have the potential as a diagnostic marker for various diseases including cancer. In order to identify novel exosomal markers for prostate cancer (PC), we performed proteomic analysis of exosomes isolated from PC cell lines and examined the usefulness of the marker in patients.MethodsExosomes isolated by differential centrifugation from the culture medium of androgen-dependent LNCaP prostate cancer cell line and its sublines of partially androgen-independent C4, androgen-independent C4-2 and bone metastatic C4-2B were subjected to iTRAQ-based proteomic analysis. Exosomes were also isolated by immunocapture and separated by size exclusion chromatography and density gradient centrifugation. Protein expression was determined by Western blot analysis. GGT activity was measured using a fluorescent probe, γ-glutamyl hydroxymethyl rhodamine green (gGlu-HMRG). Immunohistochemical analysis of tissues was performed using anti-GGT1 antibody.ResultsAmong proteins upregulated in C4-2 and C4-2B cells than in LNCaP cells, we focused on gamma-glutamyltransferase 1 (GGT1), a cell-surface enzyme that regulates the catabolism of extracellular glutathione. The levels of both GGT1 large and small subunits were elevated in exosomes isolated from C4-2 and C4-2B cells by differential centrifugation and by immunocapture with anti-CD9 or -prostate-specific membrane antigen (PSMA) antibody. In cell lysates and exosomes, GGT1 expression correlated with GGT activity. Size exclusion chromatography of human serum demonstrated the presence of GGT activity and GGT1 subunits in fractions positive for CD9. Density gradient centrifugation revealed the co-presence of GGT1 subunits with CD9 in exosomes isolated by differential centrifugation from human serum. Since GGT activity correlated with GGT1 expression in serum exosomes isolated by differential centrifugation, we measured serum exosomal GGT activity in patients. Unexpectedly, we found that serum exosomal GGT activity was significantly higher in PC patients than in benign prostatic hyperplasia (BPH) patients. In support of this finding, immunohistochemical analysis showed increased GGT1 expression in PC tissues compared with BPH tissues.ConclusionsOur results suggest that serum exosomal GGT activity could be a useful biomarker for PC.

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Dataset Information

Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients.

Publications

Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients.

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