Dataset Information

LC-MS/MS-based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma.

ABSTRACT: Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label-free proteomic analysis by LC-MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by MRM on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the United States and Egyptian cohorts. Among the 21 candidates, ten were previously reported as HCC-associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals upregulation of the complement and coagulation cascades pathway and downregulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers. All MS data have been deposited in the ProteomeXchange with identifier PXD001171 (http://proteomecentral.proteomexchange.org/dataset/PXD001171).

SUBMITTER: Tsai TH

PROVIDER: S-EPMC4490019 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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LC-MS/MS-based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma.

Tsai Tsung-Heng TH Song Ehwang E Zhu Rui R Di Poto Cristina C Wang Minkun M Luo Yue Y Varghese Rency S RS Tadesse Mahlet G MG Ziada Dina Hazem DH Desai Chirag S CS Shetty Kirti K Mechref Yehia Y Ressom Habtom W HW

Proteomics 20150429 13

Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label-free proteomic analysis by LC-MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with ...[more]

PMID: 25778709

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Project description:Although hepatocellular carcinoma (HCC) has been subjected to continuous investigation and its symptoms are well-known, early stage diagnosis of this disease remains difficult and the survival rate after diagnosis is typically very low (3-5%). Early and accurate detection of metabolic changes in the sera of patients with liver cirrhosis can help improve the prognosis of HCC and lead to a better understanding of its mechanism at the molecular level, thus providing patients with in-time treatment of the disease. In this study, we compared metabolite levels in sera of 40 HCC patients and 49 cirrhosis patients from Egypt by using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from cirrhotic controls are selected by statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. The identities of some of the putative identifications are verified by comparing their MS/MS fragmentation patterns and retention times with those from authentic compounds. Finally, the serum samples are reanalyzed for quantitation of selected metabolites as candidate biomarkers of HCC. This quantitation was performed using isotope dilution by selected reaction monitoring (SRM) on a triple quadrupole linear ion trap (QqQLIT) coupled to UPLC. Statistical analysis of the UPLC-QTOF data identified 274 monoisotopic ion masses with statistically significant differences in ion intensities between HCC cases and cirrhotic controls. Putative identifications were obtained for 158 ions by mass based search against databases. We verified the identities of selected putative identifications including glycholic acid (GCA), glycodeoxycholic acid (GDCA), 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine, and Phe-Phe. SRM-based quantitation confirmed significant differences between HCC and cirrhotic controls in metabolite levels of bile acid metabolites, long chain carnitines and small peptide. Our study provides useful insight into appropriate experimental design and computational methods for serum biomarker discovery using LC-MS/MS based metabolomics. This study has led to the identification of candidate biomarkers with significant changes in metabolite levels between HCC cases and cirrhotic controls. This is the first MS-based metabolic biomarker discovery study on Egyptian subjects that led to the identification of candidate metabolites that discriminate early stage HCC from patients with liver cirrhosis.

Project description:Rheumatoid arthritis is an autoimmune disorder of complex disease etiology. Currently available serological diagnostic markers lack in terms of sensitivity and specificity and thus additional biomarkers are warranted for early disease diagnosis and management. We aimed to screen and compare serum proteome profiles of rheumatoid arthritis serotypes with healthy controls in the Pakistani population for identification of potential disease biomarkers. Serum samples from rheumatoid arthritis patients and healthy controls were enriched for low abundance proteins using ProteoMinerTM columns. Rheumatoid arthritis patients were assigned to one of the four serotypes based on anti-citrullinated peptide antibodies and rheumatoid factor. Serum protein profiles were analyzed via liquid chromatography-tandem mass spectrometry. The changes in the protein abundances were determined using label-free quantification software ProgenesisQITM followed by pathway analysis. Findings were validated in an independent cohort of patients and healthy controls using an enzyme-linked immunosorbent assay. A total of 213 proteins were identified. Comparative analysis of all groups (false discovery rate < 0.05, >2-fold change, and identified with ≥2 unique peptides) identified ten proteins that were differentially expressed between rheumatoid arthritis serotypes and healthy controls including pregnancy zone protein, selenoprotein P, C4b-binding protein beta chain, apolipoprotein M, N-acetylmuramoyl-L-alanine amidase, catalytic chain, oncoprotein-induced transcript 3 protein, Carboxypeptidase N subunit 2, Apolipoprotein C-I and Apolipoprotein C-III. Pathway analysis predicted inhibition of liver X receptor/retinoid X receptor activation pathway and production of nitric oxide and reactive oxygen species pathway in macrophages in all serotypes. A catalogue of potential serum biomarkers for rheumatoid arthritis were identified. These biomarkers can be further evaluated in larger cohorts from different populations for their diagnostic and prognostic potential.

Project description:Chronic hepatitis B virus (CHB) infection is one of the primary risk factors associated with the development of hepatocellular carcinoma (HCC). Despite having been extensively studied, diagnosing early-stage HCC remains challenging, and diagnosed patients have a poor (3-5%) survival rate. Identifying new approaches to detect changes in the serum metabolic profiles of patients with CHB and liver cirrhosis (LC) may provide a valuable approach to better detect HCC at an early stage when it is still amenable to treatment, thereby improving patient prognosis and survival. In the present study, we, therefore, employed a liquid chromatography-mass spectrometry (LC-MS)-based approach to evaluate the serum metabolic profiles of 30 CHB patients, 29 LC patients, and 30 HCC patients. We then employed appropriate statistical methods to identify those metabolites that were best able to distinguish HCC cases from LC and CHB controls. A mass-based database was then used to putatively identify these metabolites. We then confirmed the identities of a subset of these metabolites through comparisons with the MS/MS fragmentation patterns and retention times of reference standards. The serum samples were then reanalyzed to quantify the levels of these selected metabolites and of other metabolites that have previously been identified as potential HCC biomarkers. Through this approach, we observed clear differences in the metabolite profiles of the CHB, LC, and HCC patient groups in both positive- and negative-ion modes. We found that the levels of taurodeoxy cholic acid (TCA) and 1,2-diacyl-3-β-d-galactosyl-sn-glycerol rose with the progression from CHB to LC to HCC, whereas levels of 5-hydroxy-6E,8Z,11Z,14Z,17Z-eicosapentaenoic acid, and glycyrrhizic acid were gradually reduced with liver disease progression in these groups. The ROC analysis showed that taurodeoxy cholic acid (TCA), 1,2-diacyl-3-β-d-galactosyl-sn-glycerol, 5-hydroxy-6E,8Z,11Z,14Z,17Z-eicosapentaenoic acid, and glycyrrhizic acid had a diagnosis performance with liver disease progression. These four metabolites have a significant correlation with alpha fetal protein (AFP) level and age. Our results highlight novel metabolic biomarkers that have the potential to be used for differentiating between CHB, LC, and HCC patients, thereby facilitating the identification and treatment of patients with early-stage HCC.

Project description:BackgroundImmunoglobulin A nephropathy (IgAN), a globally common primary chronic glomerulopathy, is one of the leading causes of end-stage renal disease. However, the underlying mechanisms of IgAN have yet to be demonstrated. There were no adequate and reliable plasma biomarkers for clinical diagnosis, especially at the early stage. In the present study, integrative proteomics and metabolomics were aimed at exploring the mechanism of IgAN and identifying potential biomarkers.MethodsPlasma from IgAN and healthy individuals were collected and analyzed in a randomized controlled manner. Data-independent acquisition quantification proteomics and mass spectrometry based untargeted metabolomics techniques were used to profile the differentially expressed proteins (DEPs) and differentially abundant metabolites (DAMs) between two groups and identify potential biomarkers for IgAN from health at the early stage. Disease-related pathways were screened out by clustering and function enrichment analyses of DEPs and DAMs. And the potential biomarkers for IgAN were identified through the machine learning approach. Additionally, an independent cohort was used to validate the priority candidates by enzyme-linked immunosorbent assay (ELISA).ResultsProteomic and metabolomic analyses of IgAN plasma showed that the complement and the immune system were activated, while the energy and amino acid metabolism were disordered in the IgAN patients. PRKAR2A, IL6ST, SOS1, and palmitoleic acid have been identified as potential biomarkers. Based on the AUC value for the training and test sets, the classification performance was 0.994 and 0.977, respectively. The AUC of the external validation of the four biomarkers was 0.91.ConclusionIn this study, we combined proteomics and metabolomics techniques to analyze the plasma of IgAN patients and healthy individuals, constructing a biomarker panel, which could provide new insights and provide potential novel molecular diagnoses for IgAN.

Project description:BackgroundHepatocellular carcinoma closely related to metabolic disorders is a common and aggressive liver malignancy. The dysregulation of bile acid homeostasis has emerged as a key factor for the development and progression of HCC. We aimed to investigate the relationship between bile acids and HCC diagnosis and progression.MethodsA total of 744 HBV-related patients (including 396 HCC patients and 348 patients with chronic liver diseases) were enrolled in the current study. The baseline characteristics of patients were collected from electronic medical records, and the levels of bile acid profiles were determined by LC-MS/MS. Propensity score matching analysis was conducted to reduce the effect of selection bias, and receiver operating characteristic analysis was performed to evaluate the clinical application values of bile acid.ResultsSignificant differences were observed for most characteristics between the HCC group and the CLD group before PSM analysis. Patients with HCC were older and fatter (p < 0.05). After adjusting with a 1:1 ratio for age, gender and BMI, 42 HCC patients and 42 non-HCC patients were matched in 2 groups, respectively. The total bile acid level in HCC patients was lower than that in patients with chronic liver diseases before and after PSM analysis (p < 0.05). However, patients with HCC had significantly higher levels of DCA, LCA, and GLCA and lower levels of TCDCA, GUDCA, and TUDCA (p < 0.05, respectively). Besides, the TCDCA, TUDCA, GLCA, and GUDCA were significantly correlated with tumor procession. Moreover, the BAs profiles had a superior predictive ability for predicting the development of HCC even in patients with low serum AFP levels.ConclusionPatients with HCC had significantly lower levels of total bile acid, but higher levels of secondary bile acids (DCA, LCA, and GLCA). The levels of primary bile acid (TCDCA) were closely related to tumor size and stage, which indicated that the bile acids were involved in the HCC procession and had important clinical application values.

Dataset Information

LC-MS/MS-based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma.

Publications

LC-MS/MS-based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma.

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