A microfluidic chip-based capillary zone electrophoresis-mass spectrometry method for measuring adenosine 5'-Triphosphate and its similar nucleotide analogues.

BACKGROUND: Extracellular ATP is involved in disorders that cause inflammation of the airways and cough, thus limiting its release has therapeutic benefits. Standard luminescence-based ATP assays measure levels indirectly through enzyme degradation and do not provide a simultaneous readout for other nucleotide analogues. Conversely, mass spectrometry can provide direct ATP measurements, however, common RPLC and HILIC methods face issues because these molecules are unstable, metal-sensitive analytes which are often poorly retained. These difficulties have traditionally been overcome using passivation or ion-pairing chromatography, but these approaches can be problematic for LC systems. As a result, more effective analytical methods are needed. RESULTS: Here, we introduce a new application that uses microfluidic chip-based capillary zone electrophoresis-mass spectrometry (µCZE-MS) to measure ATP and its analogues simultaneously in biofluids. The commercially available ZipChip Interface and a High-Resolution Bare-glass microchip (ZipChip, HRB, 908 Devices Inc.) coupled to a Thermo Scientific Tribrid Orbitrap, were successfully used to separate and detect various nucleotide standards, as well as ATP, ADP, AMP, and adenosine in plasma and BALF obtained from naïve Brown Norway rats. The findings demonstrate that this approach can rapidly and directly detect ATP and its related nucleotide analogues, while also highlighting the need to preserve these molecules in biofluids with chelators like EDTA. In addition, we demonstrate that this µCZE-MS method is also suitable for detecting a variety of metabolites, revealing additional potential future applications. SIGNIFICANCE: This innovative µCZE-MS approach provides a robust new tool to directly measure ATP and other nucleotide analogues in biofluids. This can enable the study of eATP in human disease and potentially contribute to the creation of ATP-targeting therapies for airway illnesses.

Metabolomic Profiling of Human Urine Samples Using LC-TIMS-QTOF Mass Spectrometry.

The identification of metabolites in biological samples is challenging due to their chemical and structural diversity. Ion mobility spectrometry (IMS) separates ionized molecules based on their mobility in a carrier buffer gas giving information about the ionic shape by measuring the rotationally averaged collision cross-section (CCS) value. This orthogonal descriptor, in combination with the m/z, isotopic pattern distribution, and MS/MS spectrum, has the potential to improve the identification of molecular molecules in complex mixtures. Urine metabolomics can reveal metabolic differences, which arise as a result of a specific disease or in response to therapeutic intervention. It is, however, complicated by the presence of metabolic breakdown products derived from a wide range of lifestyle and diet-related byproducts, many of which are poorly characterized. In this study, we explore the use of trapped ion mobility spectrometry (TIMS) via LC parallel accumulation with serial fragmentation (PASEF) for urine metabolomics. A total of 362 urine metabolites were characterized from 80 urine samples collected from healthy volunteers using untargeted metabolomics employing HILIC and RP chromatography. Additionally, three analytes (Trp, Phe, and Tyr) were selected for targeted quantification. Both the untargeted and targeted data was highly reproducible and reported CCS measurements for identified metabolites were robust in the presence of the urine matrix. A comparison of CCS values among different laboratories was also conducted, showing less than 1.3% ΔCCS values across different platforms. This is the first report of a human urine metabolite database compiled with CCS values experimentally acquired using an LC-PASEF TIMS-qTOF platform.

Integrative Analysis to Identify Race-Associated Metabolite Biomarkers for Hepatocellular Carcinoma.

Compared to European-Americans (EAs), the incidence of hepatocellular carcinoma (HCC) is higher in African-Americans (AAs) and is associated with more advanced tumor stage at diagnosis and lower survival rates. The increasing burden makes discovery of novel diagnostic, prognostic, and therapeutic biomarkers distinguishing HCC from underlying cirrhosis a significant focus. In this study, we analyzed tissue and serum samples from 40 HCC cases and 25 patients with liver cirrhosis to identify candidate biomarkers that distinguish HCC from cirrhotic patients in a race specific manner. Through integrative analysis of transcriptomic and metabolomic data, we investigated candidate metabolite biomarkers that are specific to AAs and EAs. The results from this demonstrate the utility of integrating transcriptomic and metabolomic data to prioritize clinically and biologically relevant metabolite biomarkers that can increase understanding of molecular mechanisms driving HCC in different racial groups.

Identification of miRNA-mRNA associations in hepatocellular carcinoma using hierarchical integrative model.

BACKGROUND: The established role miRNA-mRNA regulation of gene expression has in oncogenesis highlights the importance of integrating miRNA with downstream mRNA targets. These findings call for investigations aimed at identifying disease-associated miRNA-mRNA pairs. Hierarchical integrative models (HIM) offer the opportunity to uncover the relationships between disease and the levels of different molecules measured in multiple omic studies. METHODS: The HIM model we formulated for analysis of mRNA-seq and miRNA-seq data can be specified with two levels: (1) a mechanistic submodel relating mRNAs to miRNAs, and (2) a clinical submodel relating disease status to mRNA and miRNA, while accounting for the mechanistic relationships in the first level. RESULTS: mRNA-seq and miRNA-seq data were acquired by analysis of tumor and normal liver tissues from 30 patients with hepatocellular carcinoma (HCC). We analyzed the data using HIM and identified 157 significant miRNA-mRNA pairs in HCC. The majority of these molecules have already been independently identified as being either diagnostic, prognostic, or therapeutic biomarker candidates for HCC. These pairs appear to be involved in processes contributing to the pathogenesis of HCC involving inflammation, regulation of cell cycle, apoptosis, and metabolism. For further evaluation of our method, we analyzed miRNA-seq and mRNA-seq data from TCGA network. While some of the miRNA-mRNA pairs we identified by analyzing both our and TCGA data are previously reported in the literature and overlap in regulation and function, new pairs have been identified that may contribute to the discovery of novel targets. CONCLUSION: The results strongly support the hypothesis that miRNAs are important regulators of mRNAs in HCC. Furthermore, these results emphasize the biological relevance of studying miRNA-mRNA pairs.

DNA Methylation Activates TP73 Expression in Hepatocellular Carcinoma and Gastrointestinal Cancer.

The complexity of TP73 expression and its functionality, as well as the role of TP73 in tumorigenesis, unlike its cousin TP53, which is an established tumor suppressor, have remained elusive to date. In this study, we isolated two stem cell lines (HepCY & HepCO) from normal young and old human liver tissues. We determined TP73 expression in HepCY and HepCO, hepatocellular cancer (HCC) cell lines (HepG2, SNU398, SNU449 and SNU475), gastrointestinal cancer (GI) cell lines (Caco2 and HCT116) and normal skin fibroblasts cell line (HS27). Immunohistochemical analyses of TP73 expression was also performed in non-cancerous and adjacent cancerous liver tissues of HCC patients. The results show that TP73 expression is exclusive to the cancer cell lines and not the adjacent normal liver tissues. Moreover, methylation-specific PCR and bisulfite sequencing studies revealed that TP73 promoter is activated only in cancer cell lines by DNA methylation. Furthermore, ChIP assay results demonstrated that a chromosomal networking protein (CTCF) and tumor protein p53 (TP53) bind to TP73 promoter and regulate TP73 expression. Our observations demonstrate that a positive correlation in tumorigenesis exists between TP73 expression and DNA methylation in promoter regions of TP73. These findings may prove significant for the development of future diagnostic and therapeutic applications.

Establishment of ornithine transcarbamylase deficiency-derived primary human hepatocyte with hepatic functions.

A long-term hepatocyte culture maintaining liver-specific functions is very essential for both basic research and the development of bioartificial liver devices in clinical application. However, primary hepatocytes rapidly lose their proliferation and hepatic functions over a few days in culture. This work is to establish an ornithine transcarbamylase deficiency (OTCD) patient-derived primary human hepatocyte (OTCD-PHH) culture with hepatic functions for providing an in vitro cell model. Liver tissue from an infant with OTCD was dispersed into single cells. The cells were cultured using conditional reprogramming. To characterize the cells, we assessed activities and mRNA expression of CYP3A4, 1A1, 2C9, as well as albumin and urea secretion. We found that the OTCD-PHH can be subpassaged for more than 15 passages. The cells do not express mRNA of fibroblast-specific maker, whereas they highly express markers of epithelial cells and hepatocytes. In addition, the OTCD-PHH retain native CYP3A4, 1A1, 2C9 activities and albumin secretion function at early passages. The OTCD-PHH at passages 2, 6, 9 and 13 have identical DNA fingerprint as the original tissue. Furthermore, under 3D culture environment, low urea production and hepatocyte marker staining of the OTCD-PHH were detected. The established OTCD-PHH maintain liver-specific functions at early passages and can be long-term cultured in vitro. We believe the established long-term OTCD-PHH culture is highly relevant to study liver diseases, particularly in infants with OTCD.

Metabolomic Analysis of Liver Tissues for Characterization of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) causes more than half a million annual deaths worldwide. Understanding the mechanisms contributing to HCC development is highly desirable for improved surveillance, diagnosis, and treatment. Liver tissue metabolomics has the potential to reflect the physiological changes behind HCC development. Also, it allows identification of biomarker candidates for future evaluation in biofluids and investigation of racial disparities in HCC. Tumor and nontumor tissues from 40 patients were analyzed by both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) platforms to increase the metabolome coverage. The levels of the metabolites extracted from solid liver tissue of the HCC area and adjacent non-HCC area were compared. Among the analytes detected by GC-MS and LC-MS with significant alterations, 18 were selected based on biological relevance and confirmed metabolite identification. These metabolites belong to TCA cycle, glycolysis, purines, and lipid metabolism and have been previously reported in liver metabolomic studies where high correlation with HCC progression is implied. We demonstrated that metabolites related to HCC pathogenesis can be identified through liver tissue metabolomic analysis. Additionally, this study has enabled us to identify race-specific metabolites associated with HCC.

YAP/TAZ Inhibition Induces Metabolic and Signaling Rewiring Resulting in Targetable Vulnerabilities in NF2-Deficient Tumor Cells.

Merlin/NF2 is a bona fide tumor suppressor whose mutations underlie inherited tumor syndrome neurofibromatosis type 2 (NF2), as well as various sporadic cancers including kidney cancer. Multiple Merlin/NF2 effector pathways including the Hippo-YAP/TAZ pathway have been identified. However, the molecular mechanisms underpinning the growth and survival of NF2-mutant tumors remain poorly understood. Using an inducible orthotopic kidney tumor model, we demonstrate that YAP/TAZ silencing is sufficient to induce regression of pre-established NF2-deficient tumors. Mechanistically, YAP/TAZ depletion diminishes glycolysis-dependent growth and increases mitochondrial respiration and reactive oxygen species (ROS) buildup, resulting in oxidative-stress-induced cell death when challenged by nutrient stress. Furthermore, we identify lysosome-mediated cAMP-PKA/EPAC-dependent activation of RAF-MEK-ERK signaling as a resistance mechanism to YAP/TAZ inhibition. Finally, unbiased analysis of TCGA primary kidney tumor transcriptomes confirms a positive correlation of a YAP/TAZ signature with glycolysis and inverse correlations with oxidative phosphorylation and lysosomal gene expression, supporting the clinical relevance of our findings.

Identification of race-associated metabolite biomarkers for hepatocellular carcinoma in patients with liver cirrhosis and hepatitis C virus infection.

Disparities in hepatocellular carcinoma (HCC) incidence and survival have been observed between ethnic groups including African-Americans (AA) and European-Americans (EA). The evaluation of the changes in the levels of metabolites in samples stratified by race could provide a snapshot of ethnically diverse disease related pathways and identify reliable biomarkers. In this study, we considered AA and EA to investigate metabolites that may be associated with HCC in a race-specific manner. The levels of 46 metabolites in plasma samples, collected from patients recruited at MedStar Georgetown University Hospital, were analyzed by Agilent GC-qMS in selected ion monitoring (SIM) mode. A least absolute shrinkage and selection operator (LASSO) regression model was applied to select metabolites with significant changes in HCC vs. cirrhosis in three groups: (1) AA and EA combined; (2) AA separately; and (3) EA separately. In addition, metabolites that distinguish HCC cases from cirrhosis in these three groups were selected by excluding those without HCV infection. The performances of the metabolites selected by LASSO in each group were evaluated through a leave-one-out cross-validation. We identified race-specific metabolites that differentiated HCC cases from cirrhotic controls, yielding better area under the receiver operating characteristics (ROC) curve (AUC) compared to alpha-fetoprotein (AFP), the serological marker widely used for the diagnosis of HCC. This study sheds light on metabolites that could potentially be used as biomarkers for HCC by monitoring their levels in high-risk population of cirrhotic patients in a race-specific manner.

Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery.

Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675-83. ©2016 AACR.

INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery.

Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that builds sparse differential network based on partial correlation and integrates DE and DN analyses for biomarker discovery. We applied this approach on real proteomic and glycomic data generated by liquid chromatography coupled with mass spectrometry for hepatocellular carcinoma (HCC) biomarker discovery study. For each omic data, we used one dataset to select biomarker candidates, built a disease classifier and evaluated the performance of the classifier on an independent dataset. The biomarker candidates, selected by INDEED, were more reproducible across independent datasets, and led to a higher classification accuracy in predicting HCC cases and cirrhotic controls compared with those selected by separate DE and DN analyses. INDEED also identified some candidates previously reported to be relevant to HCC, such as intercellular adhesion molecule 2 (ICAM2) and c4b-binding protein alpha chain (C4BPA), which were missed by both DE and DN analyses. In addition, we applied INDEED for survival time prediction based on transcriptomic data acquired by analysis of samples from breast cancer patients. We selected biomarker candidates and built a regression model for survival time prediction based on a gene expression dataset and patients' survival records. We evaluated the performance of the regression model on an independent dataset. Compared with the biomarker candidates selected by DE and DN analyses, those selected through INDEED led to more accurate survival time prediction.

Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

BACKGROUND: A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. METHODS: We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. RESULTS: The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (<7 %) between estimation and ground truth. By applying the topic model-based purification to mass spectrometric data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. CONCLUSIONS: We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed that incorporation of scan-level features have the potential to lead to more accurate purification results by alleviating the loss in information as a result of integrating peaks. We believe cancer biomarker discovery studies that use mass spectrometric analysis of human biospecimens can greatly benefit from topic model-based purification of the data prior to statistical and pathway analyses.

Multi-omic approaches for characterization of hepatocellular carcinoma.

Multi-omic approaches offer the opportunity to characterize complex diseases such as cancer at various molecular levels. In this paper, we present transcriptomic, proteomic/glycoproteomic, glycomic, and metabolomic (TPGM) data we acquired by analysis of liver tissues from hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. We evaluated changes in the levels of transcripts, proteins, glycans, and metabolites between tumor and cirrhotic tissues by statistical methods. We demonstrated the potential of multi-omic approaches and network analysis to investigate the interactions among these biomolecules in the progression of liver cirrhosis to HCC. Also, we showed the significance of multi-omic approaches to identify pathways altered in HCC.

SIMAT: GC-SIM-MS data analysis tool.

BACKGROUND: Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software tools specifically designed for analysis of GC-SIM-MS data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS data can be imported in netCDF or NIST mass spectral library (MSL) formats. RESULTS: We evaluated the performance of SIMAT using two GC-SIM-MS datasets obtained by targeted analysis of: (1) plasma samples from 86 patients in a targeted metabolomic experiment; and (2) mixtures of internal standards spiked in plasma samples at varying concentrations in a method development study. Our results demonstrate that SIMAT offers alternative solutions to AMDIS and MetaboliteDetector to achieve accurate detection of targets and estimation of their relative intensities by analysis of GC-SIM-MS data. CONCLUSIONS: We introduce a new R package called SIMAT that allows the selection of the optimal set of fragments and retention time windows for target analytes in GC-SIM-MS based analysis. Also, various functions and algorithms are implemented in the tool to: (1) read and import raw data and spectral libraries; (2) perform GC-SIM-MS data preprocessing; and (3) plot and visualize EICs and TICs.

GC-MS Based Plasma Metabolomics for Identification of Candidate Biomarkers for Hepatocellular Carcinoma in Egyptian Cohort.

This study evaluates changes in metabolite levels in hepatocellular carcinoma (HCC) cases vs. patients with liver cirrhosis by analysis of human blood plasma using gas chromatography coupled with mass spectrometry (GC-MS). Untargeted metabolomic analysis of plasma samples from participants recruited in Egypt was performed using two GC-MS platforms: a GC coupled to single quadruple mass spectrometer (GC-qMS) and a GC coupled to a time-of-flight mass spectrometer (GC-TOFMS). Analytes that showed statistically significant changes in ion intensities were selected using ANOVA models. These analytes and other candidates selected from related studies were further evaluated by targeted analysis in plasma samples from the same participants as in the untargeted metabolomic analysis. The targeted analysis was performed using the GC-qMS in selected ion monitoring (SIM) mode. The method confirmed significant changes in the levels of glutamic acid, citric acid, lactic acid, valine, isoleucine, leucine, alpha tocopherol, cholesterol, and sorbose in HCC cases vs. patients with liver cirrhosis. Specifically, our findings indicate up-regulation of metabolites involved in branched-chain amino acid (BCAA) metabolism. Although BCAAs are increasingly used as a treatment for cancer cachexia, others have shown that BCAA supplementation caused significant enhancement of tumor growth via activation of mTOR/AKT pathway, which is consistent with our results that BCAAs are up-regulated in HCC.

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

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).

LC-MS profiling of N-Glycans derived from human serum samples for biomarker discovery in hepatocellular carcinoma.

Defining clinically relevant biomarkers for early stage hepatocellular carcinoma (HCC) in a high-risk population of cirrhotic patients has potentially far-reaching implications for disease management and patient health. Changes in glycan levels have been associated with the onset of numerous diseases including cancer. In the present study, we used liquid chromatography coupled with electrospray ionization mass spectrometry (LC-ESI-MS) to analyze N-glycans in sera from 183 participants recruited in Egypt and the U.S. and identified candidate biomarkers that distinguish HCC cases from cirrhotic controls. N-Glycans were released from serum proteins and permethylated prior to the LC-ESI-MS analysis. Through two complementary LC-ESI-MS quantitation approaches, global profiling and targeted quantitation, we identified 11 N-glycans with statistically significant differences between HCC cases and cirrhotic controls. These glycans can further be categorized into four structurally related clusters, matching closely with the implications of important glycosyltransferases in cancer progression and metastasis. The results of this study illustrate the power of the integrative approach combining complementary LC-ESI-MS based quantitation approaches to investigate changes in N-glycan levels between HCC cases and patients with liver cirrhosis.

Evaluation of metabolite biomarkers for hepatocellular carcinoma through stratified analysis by gender, race, and alcoholic cirrhosis.

BACKGROUND: The effects of hepatocellular carcinoma on liver metabolism and circulating metabolites have been subjected to continuing investigation. This study compares the levels of selected metabolites in sera of hepatocellular carcinoma cases versus patients with liver cirrhosis and evaluates the influence of gender, race, and alcoholic cirrhosis on the performance of the metabolites as candidate biomarkers for hepatocellular carcinoma. METHODS: Targeted quantitation of 15 metabolites is performed by selected research monitoring in sera from 89 Egyptian subjects (40 hepatocellular carcinoma cases and 49 cirrhotic controls) and 110 U.S. subjects (56 hepatocellular carcinoma cases and 54 cirrhotic controls). Logistic regression models are used to evaluate the ability of these metabolites in distinguishing hepatocellular carcinoma cases from cirrhotic controls. The influences of gender, race, and alcoholic cirrhosis on the performance of the metabolites are analyzed by stratified logistic regression. RESULTS: Two metabolites are selected on the basis of their significance to both cohorts. Although both metabolites discriminate hepatocellular carcinoma cases from cirrhotic controls in males and Caucasians, they are insignificant in females and African Americans. One metabolite is significant in patients with alcoholic cirrhosis and the other in nonalcoholic cirrhosis. CONCLUSIONS: The study demonstrates the potential of two metabolites as candidate biomarkers for hepatocellular carcinoma by combining them with α-fetoprotein (AFP) and gender. Stratified statistical analyses reveal that gender, race, and alcoholic cirrhosis affect the relative levels of small molecules in serum. IMPACT: The findings of this study contribute to a better understanding of the influence of gender, race, and alcoholic cirrhosis in investigating small molecules as biomarkers for hepatocellular carcinoma.

Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards.

MOTIVATION: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various '-omic' studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data. RESULTS: We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information. AVAILABILITY AND IMPLEMENTATION: MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html. CONTACT: hwr@georgetown.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.