Deep Learning Based Metabolite Annotation.

Metabolite annotation is a major bottleneck in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Machine learning and deep learning methods provide the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank candidate metabolite IDs obtained based on predicted formula or measured precursor m/z of the unknown metabolite. This approach is particularly useful to help annotate metabolites whose corresponding MS/MS spectra cannot be matched with those in spectral libraries. We previously reported application of a convolutional neural network (CNN) for molecular fingerprint prediction using MS/MS spectra obtained from the MoNA repository and NIST 20. In this paper, we investigate high-dimensional representation of the spectral data and molecular fingerprints to improve accuracy in molecular fingerprint prediction.

Biomarker Discovery for Hepatocellular Carcinoma in Patients with Liver Cirrhosis Using Untargeted Metabolomics and Lipidomics Studies.

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the disease. In addition, conventional biomarkers perform suboptimally when identifying HCC in its early stages, heightening the need for the identification of new and more effective biomarkers. Using metabolomics and lipidomics approaches, this study aims to identify serum biomarkers for identification of HCC in patients with liver cirrhosis (LC). Serum samples from 20 HCC cases and 20 patients with LC were analyzed using ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q-Exactive-MS). Metabolites and lipids that are significantly altered between HCC cases and patients with LC were identified. These include organic acids, amino acids, TCA cycle intermediates, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids. The most significant variability was observed in the concentrations of bile acids, fatty acids, and glycerophospholipids. In the context of HCC cases, there was a notable increase in the levels of phosphatidylethanolamine and triglycerides, but the levels of fatty acids and phosphatidylcholine exhibited a substantial decrease. In addition, it was observed that all of the identified metabolites exhibited a superior area under the receiver operating characteristic (ROC) curve in comparison to alpha-fetoprotein (AFP). The pathway analysis of these metabolites revealed fatty acid, lipid, and energy metabolism as the most impacted pathways. Putative biomarkers identified in this study will be validated in future studies via targeted quantification.

Deconvolution of immune cell composition and biological age of hepatocellular carcinoma using DNA methylation.

Hepatocellular carcinoma (HCC) has been an approved indication for the administration of immunotherapy since 2017, but biomarkers that predict therapeutic response have remained limited. Understanding and characterizing the tumor immune microenvironment enables better classification of these tumors and may reveal biomarkers that predict immunotherapeutic efficacy. In this paper, we applied a cell-type deconvolution algorithm using DNA methylation array data to investigate the composition of the tumor microenvironment in HCC. Using publicly available and in-house datasets with a total cohort size of 57 patients, each with tumor and matched normal tissue samples, we identified key differences in immune cell composition. We found that NK cell abundance was significantly decreased in HCC tumors compared to adjacent normal tissue. We also applied DNA methylation "clocks" which estimate phenotypic aging and compared these findings to expression-based determinations of cellular senescence. Senescence and epigenetic aging were significantly increased in HCC tumors, and the degree of age acceleration and senescence was strongly associated with decreased NK cell abundance. In summary, we found that NK cell infiltration in the tumor microenvironment is significantly diminished, and that this loss of NK abundance is strongly associated with increased senescence and age-related phenotype. These findings point to key interactions between NK cells and the senescent tumor microenvironment and offer insights into the pathogenesis of HCC as well as potential biomarkers of therapeutic efficacy.

Mapping the low abundant plasma glycoproteome using Ranachrome-5 immobilized magnetic terpolymer as improved HILIC sorbent.

HILIC (hydrophilic interaction liquid chromatography) materials enrich glycopeptides. The non-specific interactions because of support material and inadequate hydrophilicity render loss of less abundant glycopeptides in SPE-based enrichments. In this work, magnetic terpolymer (Fe3O4@MAA/DVB/1,2-Epoxy-5-hexene) is functionalized with Ranachrome-5 to generate enhanced hydrophilicity. Amine, carboxylic, and amide groups of ranachrome-5 provide zwitterionic chemistry. Material's magnetic core contributes to ease of operation while higher surface area 97.0711 m2 g-1 immobilizes better quantities of Ranachrome-5. Homogeneous morphology, nano-size, and super hydrophilicity enhance enrichment. Ranachrome-5 functionalized polymeric core-shell beads enrich 25, 18 and 16 N-linked glycopeptides via SPE strategy from tryptic digests of model glycoproteins i.e., immunoglobulin G (IgG), horseradish peroxidase (HRP) and chicken avidin, respectively. Zwitterionic chemistry of ranachrome-5 helps in achieving higher selectivity (1:250, HRP / Bovine Serum Albumin), and lower detection limit (100 attomole, HRP digest) with complete glycosylation profile of each standard digest. High binding capacity (137.1 mg/g) and reuse of affinity material up to seven cycles reduce the cost and amount of affinity material for complex sample analysis. A recovery of 91.76% and relative standard deviation (RSD) values less than 1 define the application of HILIC beads for complex samples like plasma. 508 N-linked intact low abundant glycopeptides corresponding to 50 glycoproteins are identified from depleted human plasma samples via nano-Liquid Chromatography-Tandem Mass Spectrometry (nLC-MS/MS). Using Single Nucleotide Variances (BioMuta) for low abundant plasma glycoproteins, the potential association of proteins to four cancers, i.e., breast, lung, uterine, and melanoma is evaluated. Via the bottom-up approach, HILIC beads can analyze clinically important low-abundant glycoproteins.

MicroRNA-483-5p Inhibits Hepatocellular Carcinoma Cell Proliferation, Cell Steatosis, and Fibrosis by Targeting PPARα and TIMP2.

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind with the 3' untranslated regions (UTRs) of genes to regulate expression. Downregulation of miR-483-5p (miR-483) is associated with the progression of hepatocellular carcinoma (HCC). However, the significant roles of miR-483 in nonalcoholic fatty liver disease (NAFLD), alcoholic fatty liver diseases (AFLD), and HCC remain elusive. In the current study, we investigated the biological significance of miR-483 in NAFLD, AFLD, and HCC in vitro and in vivo. The downregulation of miR-483 expression in HCC patients' tumor samples was associated with Notch 3 upregulation. Overexpression of miR-483 in a human bipotent progenitor liver cell line HepaRG and HCC cells dysregulated Notch signaling, inhibited cell proliferation/migration, induced apoptosis, and increased sensitivity towards antineoplastic agents sorafenib/regorafenib. Interestingly, the inactivation of miR-483 upregulated cell steatosis and fibrosis signaling by modulation of lipogenic and fibrosis gene expression. Mechanistically, miR-483 targets PPARα and TIMP2 gene expression, which leads to the suppression of cell steatosis and fibrosis. The downregulation of miR-483 was observed in mice liver fed with a high-fat diet (HFD) or a standard Lieber-Decarli liquid diet containing 5% alcohol, leading to increased hepatic steatosis/fibrosis. Our data suggest that miR-483 inhibits cell steatosis and fibrogenic signaling and functions as a tumor suppressor in HCC. Therefore, miR-483 may be a novel therapeutic target for NAFLD/AFLD/HCC management in patients with fatty liver diseases and HCC.

Formalin Fixation, Delay to Fixation, and Time in Fixative Adversely Impact Copy Number Variation Analysis by aCGH.

Although molecular profiling of DNA isolated from formalin-fixed, paraffin-embedded (FFPE) tumor specimens has become more common in recent years, it remains unclear how discrete FFPE processing variables may affect detection of copy number variation (CNV). To better understand such effects, array comparative genomic hybridization (aCGH) profiles of FFPE renal cell carcinoma specimens that experienced different delays to fixation (DTFs; 1, 2, 3, and 12 hours) and times in fixative (TIFs; 6, 12, 23, and 72 hours) were compared to snap-frozen tumor and blood specimens from the same patients. A greater number of regions containing CNVs relative to commercial reference DNA were detected in DNA from FFPE tumor specimens than snap-frozen tumor specimens even though they originated from the same tumor blocks. Extended DTF and TIF affected the number of DNA segments with a copy number status that differed between FFPE and frozen tumor specimens; a DTF ≥3 hours led to more segments, while a TIF of 72 hours led to fewer segments. Importantly, effects were not random as a higher guanine-cytosine (GC) content and/or a higher percentage of repeats were observed among stable regions. While limiting aCGH analysis to FFPE specimens with a DTF <3 hours and a TIF <72 hours may circumvent some effects, results from FFPE specimens should be validated against fresh or frozen specimens whenever possible.

A Bayesian two-step integrative procedure incorporating prior knowledge for the identification of miRNA-mRNAs involved in hepatocellular carcinoma.

Recent studies have confirmed the role of miRNA regulation of gene expression in oncogenesis for various cancers. In parallel, prior knowledge about relationships between miRNA and mRNA have been accumulated from biological experiments or statistical analyses. Improved identification of disease-associated miRNA-mRNA pairs may be achieved by incorporating prior knowledge into integrative genomic analyses. In this study we focus on 39 patients with hepatocellular carcinoma (HCC) and 25 patients with liver cirrhosis and use a flexible Bayesian two-step integrative method. We found 66 significant miRNA-mRNA pairs, several of which contain molecules that have previously been identified as potential biomarkers. These results demonstrate the utility of the proposed approach in providing a better understanding of relationships between different biological levels, thereby giving insights into the biological mechanisms underlying the diseases, while providing a better selection of biomarkers that may serve as diagnostic, prognostic, or therapeutic biomarker candidates.

Human serum N-glycome profiling via the newly developed asparagine immobilized cellulose/polymer nanohybrid.

Human serum N-linked glycans expression levels change during the disease progression. The low abundance, structural diversity, and coexisting matrices hinder their detection in mass spectrometry analysis. Considering the hydrophilic nature of N-glycans, cellulose/polymer (1,2-Epoxy-5-hexene) nanohybrid is fabricated with oxirane groups functionalized of asparagine to develop solid phase extraction based hydrophilic interaction liquid chromatography sorbent (cellulose/1,2-Epoxy-5-hexene/asparagine). The morphology, elemental analysis, and surface properties are studied through scanning electron microscopy, energy dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy. The large surface area of cellulose/polymer nanohybrid (2.09 × 102  m2 /g) facilitates the high density of asparagine immobilization resulting in better hydrophilic interaction liquid chromatography enrichment under optimized conditions. The enrichment capability of nanohybrid/asparagine is assessed by the N-Linked glycans released from ovalbumin and immunoglobulin G where 23 and 13 N-glycans are detected respectively. The nanohybrid/asparagine shows selectivity of 1:1200 with spiked bovine serum albumin and sensitivity down to 100 attomole. Human serum profiling for N-glycans identifies 52 glycan structures. This new enrichment strategy enriches serum N-linked glycans in the presence of salts, proteins, endogenous serum peptides, and so forth.

Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.

Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known metabolites. Machine learning provides the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank putative metabolite IDs obtained by using either the precursor mass or the formula of the unknown metabolite. This method is particularly useful to help annotate metabolites whose corresponding MS/MS spectra are missing or cannot be matched with those in accessible spectral libraries. We investigated a convolutional neural network (CNN) for molecular fingerprint prediction based on data acquired by MS/MS. We used more than 680,000 MS/MS spectra obtained from the MoNA repository and NIST 20, representing about 36,000 compounds for training and testing our CNN model. The trained CNN model is implemented as a python package, MetFID. The package is available on GitHub for users to enter their MS/MS spectra and corresponding putative metabolite IDs to obtain ranked lists of metabolites. Better performance is achieved by MetFID in ranking putative metabolite IDs using the CASMI 2016 benchmark dataset compared to two other machine learning-based tools (CSI:FingerID and ChemDistiller).

Terpolymeric platform with enhanced hydrophilicity via cysteic acid for serum intact glycopeptide analysis.

A new polymeric (methyl methacrylate/ethylene glycol dimethacrylate/1,2-epoxy-5-hexene) base/matrix has been fabricated and decorated with zwitterionic hydrophilic cysteic acid (Cya) for the enrichment of intact N-glycopeptides from standards and biological samples. Terpolymer-Cya provides good enrichment efficiency, improved hydrophilicity, and selectivity by virtue of better surface area (2.09 × 102 m2/g) provided by terpolymer and the zwitterionic property offered by cysteic acid. Cysteic acid-functionalized polymeric hydrophilic interaction liquid chromatography (HILIC) sorbent enriches 35 and 24 N-linked glycopeptides via SPE (solid phase extraction) mode from tryptic digests of model glycoproteins, i.e., immunoglobulin G (IgG) and horseradish peroxidase (HRP), respectively. Zwitterionic chemistry of cysteine helps in achieving higher selectivity with BSA digest (1:200), and lower detection limit down to 100 attomoles with a complete glycosylation profile of each standard digest. The recovery of 81% and good reproducibility define the application of terpolymer-Cya for complex samples like a serum. Analysis of human serum provides a profile of 807 intact N-linked glycopeptides via nano-liquid chromatography-tandem mass spectrometry (nLC-MS/MS). To the best of our knowledge, this is the highest number of glycopeptides enriched by any HILIC sorbent. Selected glycoproteins are evaluated in link to various cancers including the breast, lung, uterine, and melanoma using single-nucleotide variances (BioMuta). This study represents the complete idea of using an in-house developed strategy as a successful tool to help analyze, relate, and answer glycoprotein-based clinical issues regarding cancers.

Metabolomic Analysis of Plasma from Breast Cancer Patients Using Ultra-High-Performance Liquid Chromatography Coupled with Mass Spectrometry: An Untargeted Study.

Breast cancer (BC) is one of the leading causes of cancer mortality in women worldwide, and therefore, novel biomarkers for early disease detection are critically needed. We performed herein an untargeted plasma metabolomic profiling of 55 BC patients and 55 healthy controls (HC) using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS). Pre-processed data revealed 2494 ions in total. Data matrices' paired t-tests revealed 792 ions (both positive and negative) which presented statistically significant changes (FDR < 0.05) in intensity levels between cases versus controls. Metabolites identified with putative names via MetaboQuest using MS/MS and mass-based approaches included amino acid esters (i.e., N-stearoyl tryptophan, L-arginine ethyl ester), dipeptides (ile-ser, met-his), nitrogenous bases (i.e., uracil derivatives), lipid metabolism-derived molecules (caproleic acid), and exogenous compounds from plants, drugs, or dietary supplements. LASSO regression selected 16 metabolites after several variables (TNM Stage, Grade, smoking status, menopausal status, and race) were adjusted. A predictive conditional logistic regression model on the 16 LASSO selected ions provided a high diagnostic performance with an area-under-the-curve (AUC) value of 0.9729 (95% CI 0.96−0.98) on all 55 samples. This study proves that BC possesses a specific metabolic signature that could be exploited as a novel metabolomics-based approach for BC detection and characterization. Future studies of large-scale cohorts are needed to validate these findings.

Cell-type Deconvolution and Age Estimation Using DNA Methylation Reveals NK Cell Deficiency in the Hepatocellular Carcinoma Microenvironment.

Hepatocellular carcinoma (HCC) has been an approved indication for the administration of immunotherapy since 2017, but biomarkers that predict therapeutic response have remained limited. Understanding and characterizing the tumor immune microenvironment enables better classification of these tumors and may reveal biomarkers that predict immunotherapeutic efficacy. In this paper, we applied a cell-type deconvolution algorithm using DNA methylation array data to investigate the composition of the tumor microenvironment in HCC. Using two publicly available datasets with a total cohort size of 57 patients, each with tumor and matched normal tissue samples, we identified key differences in immune cell composition. We found that NK cell abundance was significantly decreased in HCC tumors compared to adjacent normal tissue. We also applied DNA methylation "clocks" which estimate phenotypic aging and compared these findings to expression-based determinations of cellular senescence. Senescence and epigenetic aging was significantly increased in HCC tumors, and the degree of age acceleration and senescence was strongly associated with decreased NK cell abundance. In summary, we found that NK cell infiltration in the tumor microenvironment is significantly diminished, and that this loss of NK abundance is strongly associated with increased senescence and age-related phenotype. These findings point to key interactions between NK cells and the senescent tumor microenvironment and offer insights into the pathogenesis of HCC as well as potential biomarkers of therapeutic efficacy.

Iminodiacetic acid (IDA)-generated mesoporous nanopolymer: a template to relate surface area, hydrophilicity, and glycopeptides enrichment.

A three-step strategy is introduced to develop inherent iminodiacetic (IDA)-functionalized nanopolymer. SEM micrographs show homogenous spherical beads with a particle size of 500 nm. Further modification to COOH-functionalized 1,2-epoxy-5-hexene/DVB mesoporous nanopolymer enriches glycopeptides via hydrophilic interactions followed by their MS determination. Significantly high BET surface area 433.4336 m2 g-1 contributes to the improved surface hydrophilicity which is also shown by high concentration of ionizable carboxylic acids, 14.59 ± 0.25 mmol g-1. Measured surface area is the highest among DVB-based polymers and in general much higher in comparison to the previously reported BET surface areas of co-polymers, terpolymers, MOFs, and graphene-based composites. Thirty-one, 19, and 16 N-glycopeptides are enriched/identified by nanopolymer beads from tryptic digests of immunoglobulin G, horseradish peroxidase, and chicken avidin, respectively, without additional desalting steps. Material exhibits high selectivity (1:400 IgG:BSA), sensitivity (down to 0.1 fmol), regeneration ability up to three cycles, and batch-to-batch reproducibility (RSD > 1%). Furthermore, from 1 µL of digested human serum, 343 N-glycopeptide characteristics of 134 glycoproteins including 30 FDA-approved serum biomarkers are identified via nano-LC-MS/MS. The developed strategy to self-generate IDA on polymeric surface with improved surface area, porosity, and ordered morphology is insignia of its potential as chromatographic tool contributing to future developments in large-scale biomedical glycoproteomics studies.

Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma.

Pathologic alterations in epigenetic regulation have long been considered a hallmark of many cancers, including hepatocellular carcinoma (HCC). In a healthy individual, the relationship between DNA methylation and microRNA (miRNA) expression maintains a fine balance; however, disruptions in this harmony can aid in the genesis of cancer or the propagation of existing cancers. The balance between DNA methylation and microRNA expression and its potential disturbance in HCC can vary by race. There is emerging evidence linking epigenetic events including DNA methylation and miRNA expression to cancer disparities. In this paper, we evaluate the epigenetic mechanisms of racial heterogenity in HCC through an integrated analysis of DNA methylation, miRNA, and combined regulation of gene expression. Specifically, we generated DNA methylation, mRNA-seq, and miRNA-seq data through the analysis of tumor and adjacent non-tumor liver tissues from African Americans (AA) and European Americans (EA) with HCC. Using mixed ANOVA, we identified cytosine-phosphate-guanine (CpG) sites, mRNAs, and miRNAs that are significantly altered in HCC vs. adjacent non-tumor tissue in a race-specific manner. We observed that the methylome was drastically changed in EA with a significantly larger number of differentially methylated and differentially expressed genes than in AA. On the other hand, the miRNA expression was altered to a larger extent in AA than in EA. Pathway analysis functionally linked epigenetic regulation in EA to processes involved in immune cell maturation, inflammation, and vascular remodeling. In contrast, cellular proliferation, metabolism, and growth pathways are found to predominate in AA as a result of this epigenetic analysis. Furthermore, through integrative analysis, we identified significantly differentially expressed genes in HCC with disparate epigenetic regulation, associated with changes in miRNA expression for AA and DNA methylation for EA.

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.

Epigenetic changes associated with mechanisms of disparities in hepatocellular carcinoma.

In addition to socioeconomic influences, biological factors are believed to play a role in health disparities. In this paper, we investigate miRNA, mRNA, and DNA methylation patterns that contribute to disparities in hepatocellular carcinoma (HCC). This is accomplished by integration of mRNA-Seq, miRNA-Seq, and DNA methylation data we acquired by analysis of liver tissues from 30 HCC patients consisting of European Americans (EAs), African Americans (AAs), and Asian Americans (Asians). Mixed-ANOVA models are applied to identify miRNAs, mRNAs, and DNA methylation sites that are significantly altered in tumor vs. adjacent normal tissues in a race-specific manner. Through integrated analysis, a refined list of differentially expressed mRNAs is obtained by selecting those that are targets of differentially expressed miRNAs and consist of promoter regions that are differentially methylated.

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.

INTRODUCTION: Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds. OBJECTIVE: The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries. METHODS: We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study. RESULTS: We observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential to improve the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag. CONCLUSION: MetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.

MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery.

The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods.

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.