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Mass spectrometry-based iTRAQ analysis of serum markers in patients with pancreatic cancer.

ABSTRACT: Early biomarkers for pancreatic cancer (PC) detection are required in order to improve patient outcomes. The present study aimed to identify serum biomarkers for PC diagnosis using proteomics, unveil the underlying pathological mechanisms and provide reliable data for the early diagnosis of PC. Isobaric tags for relative and absolute quantification and two-dimensional-liquid chromatography-tandem mass spectometry were used to compare serum samples from patients with PC and healthy individuals. Mascot and Scaffold were used for raw data processing, and Panther for gene ontology (GO) analysis. Igenuity® Pathway Analysis (IPA) was utilized to assess canonical pathways and protein-protein interactions. In total, 76 differentially expressed proteins were identified. The candidate protein DNA repair protein 50 (RAD50) was elevated in patients with PC compared with healthy individuals. In addition, transforming growth factor-β1 (TGF-β1) and apoptotic protease activating factor 1 (APAF-1) were downregulated in PC. GO analysis revealed that the extracellular matrix was increased in PC, as well as receptor function and enzyme regulation; additionally, reduced nucleic acid binding transcription factor activity was observed. IPA analysis demonstrated that the significantly altered canonic pathways were liver X receptor/retinoid X receptor (RXR) activation, the production of nitric oxide and reactive oxygen species in macrophages, the coagulation system, acute phase response signaling and lipopolysaccharide/interleukin-1 mediated inhibition of RXR function. To conclude, RAD50, TGF-β1 and APAF1 are candidate biomarkers for the diagnosis of early PC. The results from the present study could help identify future therapeutic drugs for PC.

SUBMITTER: Jia K

PROVIDER: S-EPMC7202281 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Pancreatic cyst fluids (PCFs) enriched in tumour-derived proteins are considered a potential source of new biomarkers. This study aimed to determine compositional and quantitative differences between the degradome and proteome of PCFs aspirated from different types of pancreatic cyst lesions (PCLs). 91 patients who underwent endoscopic ultrasound-fine needle aspiration under routine clinical diagnosis of PCLs were enrolled. Four cysts were malignant (CAs), and 87 were nonmalignant and consisted of 18 intraductal papillary mucinous neoplasms (IPMNs), 14 mucinous cystic neoplasms (MCNs), nine serous cystic neoplasms (SCNs), 29 pseudocysts (PCs), and 17 unclassified. Profiles of the <5 kDa fraction, the degradome, and the trypsin-digested proteome were analysed using an LTQ-Orbitrap Elite mass spectrometer coupled with a nanoACQUITY LC system. Qualitative analyses identified 796 and 366 proteins in degradome and proteome, respectively, and 689 (77%) and 285 (78%) of them were present in the Plasma Proteome Database. Gene Ontology analysis showed a significant overrepresentation of peptidases and peptidases inhibitors in both datasets. In the degradome fraction, quantitative values were obtained for 6996 peptides originating from 657 proteins. Of these, 2287 peptides were unique to a single type, and 515 peptides, derived from 126 proteins, were shared across cyst types. 32 peptides originating from 12 proteins had differential (adjusted p-value ?0.05, FC ?1.5) abundance in at least one of the five cysts types. In proteome, relative expression was measured for 330 proteins. Of them, 33 proteins had significantly (adjusted p-value ?0.05, FC ?1.5) altered abundance in at least one of the studied groups and 19 proteins appeared to be unique to a given cyst type. PCFs are dominated by blood proteins and proteolytic enzymes. Although differences in PCF peptide composition and abundance could aid classification of PCLs, the unpredictable inherent PCF proteolytic activity may limit the practical applications of PCF protein profiling.

Project description:Background: Once prostate cancer developed bone metastasis, the quality of life and prognosis of patients are seriously affected as no effective treatment is currently available. It is necessary to explore the mechanism of bone metastasis and new therapeutic targets. Purpose: To find out the differentially expressed serum proteins in prostate cancer patients with bone metastasis and analyze the expression of key proteins in prostate cancer tissues, serum and prostate cancer cell lines. So as to provide a basis for revealing the mechanism of bone metastasis and designing new therapeutic targets. Methods: iTRAQ-based proteomics method was used to compare serum differential proteins in prostate cancer patients with and without bone metastasis. Three key proteins (CD59, haptoglobin and tetranectin) which had significant fold changes were selected to validate the results of mass spectrometry. Immunohistochemistry and ELISA were applied to tissues and serum samples from prostate cancer patients, respectively, for validation. Finally, western blot, flow cytometry, and immunocytochemistry were used to analyze the expression of the three differentially expressed proteins in the prostate cancer cell lines PC3, LNCap, and Du145. Results: Thirty-two differentially expressed proteins related to bone metastasis of prostate cancer were identified, of which 11 were up-regulated and 21 were down-regulated. CD59 and haptoglobin were up-regulated in prostate cancer with bone metastasis while tetranectin was down-regulated. Tetranectin showed differential expression in epithelial and stromal cells of prostate cancer and hyperplasia tissues.The expression of CD59 was highest in PC3 and lowest in LNCap, while the expression of haptoglobin and tetranectin was the highest in DU145 and lowest in PC3. Conclusion: Mass spectrometry analysis showed that there were more differentially expressed proteins in the serum of patients with bone metastasis than those without metastasis. It has been verified that CD59, haptoglobin and tetranectin are prostate cancer bone metastasis related proteins.

Project description:BackgroundOsteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.MethodsSerum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography-tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.ResultsIn total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).ConclusionOur study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

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Mass spectrometry-based iTRAQ analysis of serum markers in patients with pancreatic cancer.

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