Short-Read RNA-Seq.

RNA sequencing (RNA-Seq) has emerged as a powerful and versatile tool for the comprehensive analysis of transcriptomes and has been widely used to investigate gene expression, copy number variation, alternative splicing, and novel transcript discovery. This chapter outlines the methodology for conducting short-read RNA-Seq, starting from RNA enrichment to library preparation and sequencing. Throughout the chapter, practical tips and best practices are provided to guide researchers in order to optimize each step of the RNA-Seq workflow. Multiple quality control steps throughout the workflow that are critical to obtain high-quality RNA-Seq data are also discussed.

RNA-Seq Data Analysis.

RNA-Seq data analysis stands as a vital part of genomics research, turning vast and complex datasets into meaningful biological insights. It is a field marked by rapid evolution and ongoing innovation, necessitating a thorough understanding for anyone seeking to unlock the potential of RNA-Seq data. In this chapter, we describe the intricate landscape of RNA-seq data analysis, elucidating a comprehensive pipeline that navigates through the entirety of this complex process. Beginning with quality control, the chapter underscores the paramount importance of ensuring the integrity of RNA-seq data, as it lays the groundwork for subsequent analyses. Preprocessing is then addressed, where the raw sequence data undergoes necessary modifications and enhancements, setting the stage for the alignment phase. This phase involves mapping the processed sequences to a reference genome, a step pivotal for decoding the origins and functions of these sequences.Venturing into the heart of RNA-seq analysis, the chapter then explores differential expression analysis-the process of identifying genes that exhibit varying expression levels across different conditions or sample groups. Recognizing the biological context of these differentially expressed genes is pivotal; hence, the chapter transitions into functional analysis. Here, methods and tools like Gene Ontology and pathway analyses help contextualize the roles and interactions of the identified genes within broader biological frameworks. However, the chapter does not stop at conventional analysis methods. Embracing the evolving paradigms of data science, it delves into machine learning applications for RNA-seq data, introducing advanced techniques in dimension reduction and both unsupervised and supervised learning. These approaches allow for patterns and relationships to be discerned in the data that might be imperceptible through traditional methods.

Suppressor T helper type 17 cell responses in intestinal transplant recipients with allograft rejection.

BACKGROUND: Intestinal transplant (ITx) rejection is associated with memory T helper type 17 cell (Th17) infiltration of grafted tissues. Modulation of Th17 effector cell response is facilitated by T regulatory (Treg) cells, but a phenotypic characterization of this process is lacking in the context of allograft rejection. METHODS: Flow cytometry was performed to examine the expression of surface receptors, cytokines, and transcription factors in Th17 and Treg cells in ITx control (n = 34) and rejection patients (n = 23). To elucidate key pathways guiding the rejection biology, we utilized RNA sequencing (RNAseq) and assessed epigenetic stability through pyrosequencing of the Treg-specific demethylated region (TSDR). RESULTS: We found that intestinal allograft rejection is characterized by Treg cellular infiltrates, which are polarized toward Th17-type chemokine receptor, ROR-γt transcription factor expression, and cytokine production. These Treg cell subsets have maintained epigenetic stability, as defined by FoxP3-TSDR methylation status, but displayed upregulation of functional Treg and purinergic signaling genes by RNAseq analysis such as CD39, in keeping with suppressor Th17 properties. CONCLUSION: We show that ITx rejection is associated with increased polarized cells that express a Th17-like phenotype concurrent with regulatory purinergic markers.

Non-fatal outcomes of COVID-19 disease in pediatric organ transplantation associates with down-regulation of senescence pathways.

This is a cross-sectional study examining kinetics and durability of immune response in children with solid organ transplants (SOTs) who had COVID-19 disease between November 2020 through June 2022, who were followed for 60-days at a single transplant center. Blood was collected between 1-14 (acute infection), and 15-60 days of a positive PCR (convalescence). SOT children with peripheral blood mononuclear cells (PBMC) cryopreserved before 2019 were non-infected controls (ctrls). PBMCs stimulated with 15-mer peptides from spike protein and anti-CD49d/anti-CD28. Testing done included mass cytometry, mi-RNA sequencing with confirmatory qPCR. 38 children formed the study cohort, 10 in the acute phase and 8 in the convalescence phase. 20 subjects were non-infected controls. Two subjects had severe disease. Subjects in the acute and convalescent phases were different subjects. The median age and tacrolimus level at blood draw was not significantly different. There was no death, and no subject was lost to follow-up. During acute infection CD57 expression was low in NKT, Th17 effector memory, memory Treg, CD4-CD8-, and γδT cells (p = 0.01, p = 0.04, p = 0.03, p = 0.03, p = 0.004 respectively). The frequencies of NK and Th2 effector memory cells increased (p = 0.01, p = 0.02) during acute infection. Non-switched memory B and CD8 central memory cell frequencies were decreased during acute infection (p = 0.02; p = 0.02), but the decrease in CD8 central memory cells did not persist. CD4-CD8- and CD14 monocyte frequencies increased during recovery (p = 0.03; p = 0.007). Our observations suggest down regulation of CD57 with absence of NK cell contraction protect against death from COVID-19 disease in children with SOTs.

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.

Inhibition of discoidin domain receptor (DDR)-1 with nilotinib alters CSF miRNAs and is associated with reduced inflammation and vascular fibrosis in Alzheimer's disease.

Discoidin Domain Receptor (DDR)-1 is activated by collagen. Nilotinib is a tyrosine kinase inhibitor that is FDA-approved for leukemia and potently inhibits DDR-1. Individuals diagnosed with mild-moderate Alzheimer's disease (AD) treated with nilotinib (versus placebo) for 12 months showed reduction of amyloid plaque and cerebrospinal fluid (CSF) amyloid, and attenuation of hippocampal volume loss. However, the mechanisms are unclear. Here, we explored unbiased next generation whole genome miRNA sequencing from AD patients CSF and miRNAs were matched with their corresponding mRNAs using gene ontology. Changes in CSF miRNAs were confirmed via measurement of CSF DDR1 activity and plasma levels of AD biomarkers. Approximately 1050 miRNAs are detected in the CSF but only 17 miRNAs are specifically altered between baseline and 12-month treatment with nilotinib versus placebo. Treatment with nilotinib significantly reduces collagen and DDR1 gene expression (upregulated in AD brain), in association with inhibition of CSF DDR1. Pro-inflammatory cytokines, including interleukins and chemokines are reduced along with caspase-3 gene expression. Specific genes that indicate vascular fibrosis, e.g., collagen, Transforming Growth Factors (TGFs) and Tissue Inhibitors of Metalloproteases (TIMPs) are altered by DDR1 inhibition with nilotinib. Specific changes in vesicular transport, including the neurotransmitters dopamine and acetylcholine, and autophagy genes, including ATGs, indicate facilitation of autophagic flux and cellular trafficking. Inhibition of DDR1 with nilotinib may be a safe and effective adjunct treatment strategy involving an oral drug that enters the CNS and adequately engages its target. DDR1 inhibition with nilotinib exhibits multi-modal effects not only on amyloid and tau clearance but also on anti-inflammatory markers that may reduce cerebrovascular fibrosis.

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.

Alteration of Autophagy and Glial Activity in Nilotinib-Treated Huntington's Disease Patients.

Nilotinib is a tyrosine kinase inhibitor that is safe and tolerated in neurodegeneration, it achieves CSF concentration that is adequate to inhibit discoidin domain receptor (DDR)-1. Nilotinib significantly affects dopamine metabolites, including Homovanillic acid (HVA), resulting in an increase in brain dopamine. HD is a hereditary disease caused by mutations in the Huntingtin's (HTT) gene and characterized by neurodegeneration and motor and behavioral symptoms that are associated with activation of dopamine receptors. We explored the effects of a low dose of nilotinib (150 mg) on behavioral changes and motor symptoms in manifest HD patients and examined the effects of nilotinib on several brain mechanisms, including dopamine transmission and gene expression via cerebrospinal fluid (CSF) miRNA sequencing. Nilotinib, 150 mg, did not result in any behavioral changes, although it significantly attenuated HVA levels, suggesting reduction of dopamine catabolism. There was no significant change in HTT, phosphorylated neuro-filament and inflammatory markers in the CSF and plasma via immunoassays. Whole miRNA genome sequencing of the CSF revealed significant longitudinal changes in miRNAs that control specific genes associated with autophagy, inflammation, microglial activity and basal ganglia neurotransmitters, including dopamine and serotonin.

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.

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).

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.

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.

Transcriptome of rhesus macaque (Macaca mulatta) exposed to total-body irradiation.

The field of biodosimetry has seen a paradigm shift towards an increased use of molecular phenotyping technologies including omics and miRNA, in addition to conventional cytogenetic techniques. Here, we have used a nonhuman primate (NHP) model to study the impact of gamma-irradiation on alterations in blood-based gene expression. With a goal to delineate radiation induced changes in gene expression, we followed eight NHPs for 60 days after exposure to 6.5 Gy gamma-radiation for survival outcomes. Analysis of differential gene expression in response to radiation exposure yielded 26,944 dysregulated genes that were not significantly impacted by sex. Further analysis showed an increased association of several pathways including IL-3 signaling, ephrin receptor signaling, ErbB signaling, nitric oxide signaling in the cardiovascular system, Wnt/ß-catenin signaling, and inflammasome pathway, which were associated with positive survival outcomes in NHPs after acute exposure to radiation. This study provides novel insights into major pathways and networks involved in radiation-induced injuries that may identify biomarkers for radiation injury.

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.

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.