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Label-Free Quantitative Proteomics Unravels Carboxypeptidases as the Novel Biomarker in Pancreatic Ductal Adenocarcinoma.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, with a high mortality rate and poor prognosis. However, little is known concerning the molecular mechanism of PDAC at the proteomics level. Here we report a proteomics analysis of PDAC tumor and adjacent tissues by shotgun proteomics followed by label-free quantification, and in total, 3031 and 3306 proteins were identified in three pairs of PDAC tumor and adjacent tissues, respectively; 40 of them were differentially expressed for at least three-fold in PDAC tumor tissues. Ontological and interaction network analysis highlighted the dysregulation of a set of four proteins in the carboxypeptidase family: carboxypeptidase A1 (CPA1), A2 (CPA2), B1 (CPB1), and chymotrypsin C (CTRC). Western blotting confirmed the downregulation of the carboxypeptidase network in PDAC. Immunohistochemistry of tissue microarray from 90 PDAC patients demonstrated that CPB1 was downregulated 7.07-fold (P<.0001, n=81) in tumor comparing with the peritumor tissue. Further 208 pancreatic tissues from PDAC tumor, peritumor, and pancreatis confirmed the downregulation of CPB1 in the PDAC patients. In summary, our results displayed that the expression of carboxypeptidase is significantly downregulated in PDAC tumor tissues and may be novel biomarker in the patient with PDAC.

SUBMITTER: Song Y

PROVIDER: S-EPMC6154863 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Label-Free Quantitative Proteomics Unravels Carboxypeptidases as the Novel Biomarker in Pancreatic Ductal Adenocarcinoma.

Song Yang Y Wang Qing Q Wang Desheng D Junqiang Li Yang Jing J Li Hong H Wang Xiang X Jin Xuerong X Jing Ruirui R Yang Jing-Hua JH Su Haichuan H

Translational oncology 20180406 3

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, with a high mortality rate and poor prognosis. However, little is known concerning the molecular mechanism of PDAC at the proteomics level. Here we report a proteomics analysis of PDAC tumor and adjacent tissues by shotgun proteomics followed by label-free quantification, and in total, 3031 and 3306 proteins were identified in three pairs of PDAC tumor and adjacent tissues, respectively; 40 of them were differentially exp ...[more]

PMID: 29631213

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Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is an aggressive gastrointestinal cancer characterized by an extremely low survival rate because of early metastasis. Identifying satisfactory therapeutic targets associated with metastasis is crucial to improve the treatment effect of PDAC.Materials and methodsIn this research, we used stable isotope labeling by amino acids in cell culture, 1-dodecyl-3-methylimidazolium chloride-assisted sample preparation method preparing protein sample and nano-reversed-phase liquid chromatography-mass spectrometry/mass spectrometry analysis to perform the comparative proteomics of two homologous hamster pancreatic cancer cell lines that are different in metastatic ability: PC-1.0 (highly metastatic) and PC-1 (weakly metastatic). Verifications are through immunohistochemistry on clinical human PDAC pathologic tissues as well as by Western blot of human pancreatic cancer cell lines. siRNA silencing methods were used to study the effect of molecules on invasion and metastasis of pancreatic cancer cell lines.ResultsBioinformatic analysis indicated that a total of 141 differentially expressed proteins (82 upregulated and 59 downregulated in PC-1.0 cells) were identified showing obviously differential expression (>1.5-fold change). These differentially expressed proteins were involved in a number of different biologic functions, metabolic pathways, and pathophysiologic processes. Phosphoglycerate mutase 1 (PGAM1) and HSPE1 are the top two upregulated proteins, and PDIA3 and CALR are the top two downregulated proteins in PC-1.0 cells compared to PC-1 cells. PGAM1 and HSPE1 showed higher expressions in PDAC tissue with clinical metastasis and highly metastatic pancreatic cancer cell lines PC-1.0 and Aspc-1. PDIA3 and CALR showed higher expressions in weakly metastatic pancreatic cancer cell lines PC-1 and Capan-2. The Western blot results were consistent with the MS quantification data. Silencing PGAM1 was found to decrease the migration and invasion of pancreatic cancer cell lines with statistical significance, especially in highly metastatic PC-1.0 and Aspc-1 cell lines.ConclusionThese data indicated that PGAM1 may be a potential therapeutic target for PDAC metastasis.

Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Dataset Information

Label-Free Quantitative Proteomics Unravels Carboxypeptidases as the Novel Biomarker in Pancreatic Ductal Adenocarcinoma.

Publications

Label-Free Quantitative Proteomics Unravels Carboxypeptidases as the Novel Biomarker in Pancreatic Ductal Adenocarcinoma.

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