PubMeth: a cancer methylation database combining text-mining and expert annotation

CancerMHL: the database of integrating key DNA methylation, histone modifications and lncRNAs in cancer

The discovery of key epigenetic modifications in cancer is of great significance for the study of disease biomarkers. Through the mining of epigenetic modification data relevant to cancer, some researches on epigenetic modifications are accumulating. In order to make it easier to integrate the effects of key epigenetic modifications on the related cancers, we established CancerMHL (http://www.positionprediction.cn/), which provide key DNA methylation, histone modifications and lncRNAs as well as the effect of these key epigenetic modifications on gene expression in several cancers. To facilitate data retrieval, CancerMHL offers flexible query options and filters, allowing users to access specific key epigenetic modifications according to their own needs. In addition, based on the epigenetic modification data, three online prediction tools had been offered in CancerMHL for users. CancerMHL will be a useful resource platform for further exploring novel and potential biomarkers and therapeutic targets in cancer. Database URL: http://www.positionprediction.cn/.

Environment factors, DNA methylation, and cancer

Today, the rapid development of science and technology and the rapid change in economy and society are changing the way of life of human beings and affecting the natural, living, working, and internal environment on which human beings depend. At the same time, the global incidence of cancer has increased significantly yearly, and cancer has become the number one killer that threatens human health. Studies have shown that diet, living habits, residential environment, mental and psychological factors, intestinal flora, genetics, social factors, and viral and non-viral infections are closely related to human cancer. However, the molecular mechanisms of the environment and cancer development remain to be further explored. In recent years, DNA methylation has become a key hub and bridge for environmental and cancer research. Some environmental factors can alter the hyper/hypomethylation of human cancer suppressor gene promoters, proto-oncogene promoters, and the whole genome, causing low/high expression or gene mutation of related genes, thereby exerting oncogenic or anticancer effects. It is expected to develop early warning markers of cancer environment based on DNA methylation, thereby providing new methods for early detection of cancers, diagnosis, and targeted therapy. This review systematically expounds on the internal mechanism of environmental factors affecting cancer by changing DNA methylation, aiming to help establish the concept of cancer prevention and improve people's health.

Unlocking the secrets: the power of methylation-based cfDNA detection of tissue damage in organ systems

BACKGROUND

Detecting organ and tissue damage is essential for early diagnosis, treatment decisions, and monitoring disease progression. Methylation-based assays offer a promising approach, as DNA methylation patterns can change in response to tissue damage. These assays have potential applications in early detection, monitoring disease progression, evaluating treatment efficacy, and assessing organ viability for transplantation. cfDNA released into the bloodstream upon tissue or organ injury can serve as a biomarker for damage. The epigenetic state of cfDNA, including DNA methylation patterns, can provide insights into the extent of tissue and organ damage.

CONTENT

Firstly, this review highlights DNA methylation as an extensively studied epigenetic modification that plays a pivotal role in processes such as cell growth, differentiation, and disease development. It then presents a variety of highly precise 5-mC methylation detection techniques that serve as powerful tools for gaining profound insights into epigenetic alterations linked with tissue damage. Subsequently, the review delves into the mechanisms underlying DNA methylation changes in organ and tissue damage, encompassing inflammation, oxidative stress, and DNA damage repair mechanisms. Next, it addresses the current research status of cfDNA methylation in the detection of specific organ tissues and organ damage. Finally, it provides an overview of the multiple steps involved in identifying specific methylation markers associated with tissue and organ damage for clinical trials. This review will explore the mechanisms and current state of research on cfDNA methylation-based assay detecting organ and tissue damage, the underlying mechanisms, and potential applications in clinical practice.

ADmeth: A Manually Curated Database for the Differential Methylation in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease. More and more evidence show that DNA methylation is closely related to the pathological mechanism of AD. Many AD-associated differentially methylated genes, regions and CpG sites have been identified in recent researches, which may have great potential in clinical research. However, there is no dedicated database to collect AD-related differential methylation up to now. To provide a reference to researchers, we design a database named ADmeth by manually curating relevant articles, which contains a total of 16,709 AD-related differentially methylated items identified from different brain regions and different cell types in the blood, involving 209 genes, 2,229 regions and 14,271 CpG sites. The ADmeth database provides user-friendly pages to search, submit and download data. We hope that the ADmeth database can facilitate researchers to select candidate AD-associated methylation markers in revealing the pathological mechanism of AD and promote the cell-free DNA based non-invasive diagnosis of AD. The ADmeth database is available at http://www.biobdlab.cn/ADmeth.

ImmuMethy, a database of DNA methylation plasticity at a single cytosine resolution in human blood and immune cells

Differential DNA methylation is a feature of numerous physiological and pathological processes. However, the extent to which single-base cytosine methylation modifies cellular responses to various stimuli has not been well characterized. In this study, we carried out a systematic analysis of methylome data derived from human blood and immune cells and constructed the ImmuMethy database. ImmuMethy allows interrogation of DNA methylation plasticity (MPL) at the single cytosine level. MPL, which refers to the variability of DNA methylation, is quantitatively measured in multiple ways, such as quartiles and standard deviations. ImmuMethy comprises over 36 000 samples from the Human Methylation450 and MethylationEPIC BeadChips platforms and provides multiple applications, such as an overview of methylation status and plasticity, differential methylation analysis, identification of methylation markers and sample stratification. An analysis of all datasets revealed that DNA methylation is generally stable, with minimal changes in beta values. This further supports the characteristics of DNA methylation homeostasis. Based on the beta value distribution, we identified three types of methylation sites: methylation tendency sites, unmethylation tendency sites and dual tendency or nonbiased methylation sites. These sites represent different methylation tendentiousness of DNA methylation across samples. The occurrence of multiple methylation tendencies in a site means split methylation, which generally corresponds to high MPL. Inverted methylation tendencies from methylation tendency sites to unmethylation tendency sites, or vice versa, represent strong differential methylation in response to conditions. All these sites can be identified in ImmuMethy, making it a useful tool for omics-based data-driven knowledge discovery.

Database URL: http://immudb.bjmu.edu.cn/immumethy/

Glioblastoma in the Elderly: Review of Molecular and Therapeutic Aspects

Glioblastoma (GBM) is the most aggressive primary brain tumour. As GBM incidence is associated with age, elderly people represent a consistent subgroup of patients. Elderly people with GBM show dismal prognosis (about 6 months) and limited response to treatments. Age is a negative prognostic factor, which correlates with clinical frailty, poorer tolerability to surgery or adjuvant radio-chemotherapy, and higher occurrence of comorbidities and/or secondary complications. The aim of this paper is to review the clinical and molecular characteristics, current therapeutic options, and prognostic factors of elderly patients with GBM.

EWAS Open Platform: integrated data, knowledge and toolkit for epigenome-wide association study

Epigenome-Wide Association Study (EWAS) has become a standard strategy to discover DNA methylation variation of different phenotypes. Since 2018, we have developed EWAS Atlas and EWAS Data Hub to integrate a growing volume of EWAS knowledge and data, respectively. Here, we present EWAS Open Platform (https://ngdc.cncb.ac.cn/ewas) that includes EWAS Atlas, EWAS Data Hub and the newly developed EWAS Toolkit. In the current implementation, EWAS Open Platform integrates 617 018 high-quality EWAS associations from 910 publications, covering 51 phenotypes, 275 diseases and 104 environmental factors. It also provides well-normalized DNA methylation array data and the corresponding metadata from 115 852 samples, which involve 707 tissues, 218 cell lines and 528 diseases. Taking advantage of integrated knowledge and data in EWAS Atlas and EWAS Data Hub, EWAS Open Platform equips with EWAS Toolkit, a powerful one-stop site for EWAS enrichment, annotation, and knowledge network construction and visualization. Collectively, EWAS Open Platform provides open access to EWAS knowledge, data and toolkit and thus bears great utility for a broader range of relevant research.

Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

INTRODUCTION

Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificial Intelligence (AI) and Machine Learning (ML) techniques and their applications in various fields have brought immense value in providing insights into advancement in support of cancer control.

METHODS

A systematic and thematic analysis was performed on the Scopus database to identify the top 100 cited articles in cancer research. Data were analyzed using RStudio and VOSviewer.Var1.6.6.

RESULTS

The top 100 articles in AI and ML in cancer received a 33 920 citation score with a range of 108 to 5758 times. Doi Kunio from the USA was the most cited author with total number of citations (TNC = 663). Out of 43 contributed countries, 30% of the top 100 cited articles originated from the USA, and 10% originated from China. Among the 57 peer-reviewed journals, the "Expert Systems with Application" published 8% of the total articles. The results were presented in highlight technological advancement through AI and ML via the widespread use of Artificial Neural Network (ANNs), Deep Learning or machine learning techniques, Mammography-based Model, Convolutional Neural Networks (SC-CNN), and text mining techniques in the prediction, diagnosis, and prevention of various types of cancers towards cancer control.

CONCLUSIONS

This bibliometric study provides detailed overview of the most cited empirical evidence in AI and ML adoption in cancer research that could efficiently help in designing future research. The innovations guarantee greater speed by using AI and ML in the detection and control of cancer to improve patient experience.

Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization

Cancer immune evasion is one of the hallmarks of carcinogenesis. Cancer cells employ multiple mechanisms to avoid immune recognition and suppress antitumor immune responses. Recently, accumulating evidence has indicated that immune-related pathways are epigenetically dysregulated in cancer. Most importantly, the epigenetic footprint of immune-related pathways is associated with the patient outcome, underscoring the crucial need to understand this process. In this review, we summarize the current evidence for epigenetic regulation of immune-related pathways in cancer and describe bioinformatics tools, informative visualization techniques, and resources to help decipher the cancer epigenome.

Abnormal patterns of genomic chemical modification help tumors elude immunological destruction, but sophisticated computational tools could help identify and overcome these survival mechanisms. Immunotherapy can be a potent weapon against cancer, but many tumors evolve the ability to protect themselves by subduing the immune response. Sungjune Kim and colleagues at the Moffitt Cancer Center, Tampa, USA, have reviewed efforts to study how chemical alterations to DNA that affect gene expression contribute to this process. Considerable evidence indicates a role for a modification called methylation in this immune evasion, and researchers now have access to vast repositories of tumor-specific gene methylation profiles. The authors describe these data resources, and highlight some of the software tools that are helping oncologists to identify patterns in the data that might lead to better therapies.

Bioinformatic tools for DNA methylation and histone modification: A survey

Epigenetic inheritance occurs due to different mechanisms such as chromatin and histone modifications, DNA methylation and processes mediated by non-coding RNAs. It leads to changes in gene expressions and the emergence of new traits in different organisms in many diseases such as cancer. Recent advances in experimental methods led to the identification of epigenetic target sites in various organisms. Computational approaches have enabled us to analyze mass data produced by these methods. Next-generation sequencing (NGS) methods have been broadly used to identify these target sites and their patterns. By using these patterns, the emergence of diseases could be prognosticated. In this study, target site prediction tools for two major epigenetic mechanisms comprising histone modification and DNA methylation are reviewed. Publicly accessible databases are reviewed as well. Some suggestions regarding the state-of-the-art methods and databases have been made, including examining patterns of epigenetic changes that are important in epigenotypes detection.

Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications

Epigenetics is mainly comprised of features that regulate genomic interactions thereby playing a crucial role in a vast array of biological processes. Epigenetic mechanisms such as DNA methylation and histone modifications influence gene expression by modulating the packaging of DNA in the nucleus. A plethora of studies have emphasized the importance of analyzing epigenetics data through genome-wide studies and high-throughput approaches, thereby providing key insights towards epigenetics-based diseases such as cancer. Recent advancements have been made towards translating epigenetics research into a high throughput approach such as genome-scale profiling. Amongst all, bioinformatics plays a pivotal role in achieving epigenetics-related computational studies. Despite significant advancements towards epigenomic profiling, it is challenging to understand how various epigenetic modifications such as chromatin modifications and DNA methylation regulate gene expression. Next-generation sequencing (NGS) provides accurate and parallel sequencing thereby allowing researchers to comprehend epigenomic profiling. In this review, we summarize different computational methods such as machine learning and other bioinformatics tools, publicly available databases and resources to identify key modifications associated with epigenetic machinery. Additionally, the review also focuses on understanding recent methodologies related to epigenome profiling using NGS methods ranging from library preparation, different sequencing platforms and analytical techniques to evaluate various epigenetic modifications such as DNA methylation and histone modifications. We also provide detailed information on bioinformatics tools and computational strategies responsible for analyzing large scale data in epigenetics.

MethHC 2.0: information repository of DNA methylation and gene expression in human cancer

DNA methylation is an important epigenetic regulator in gene expression and has several roles in cancer and disease progression. MethHC version 2.0 (MethHC 2.0) is an integrated and web-based resource focusing on the aberrant methylomes of human diseases, specifically cancer. This paper presents an updated implementation of MethHC 2.0 by incorporating additional DNA methylomes and transcriptomes from several public repositories, including 33 human cancers, over 50 118 microarray and RNA sequencing data from TCGA and GEO, and accumulating up to 3586 manually curated data from >7000 collected published literature with experimental evidence. MethHC 2.0 has also been equipped with enhanced data annotation functionality and a user-friendly web interface for data presentation, search, and visualization. Provided features include clinical-pathological data, mutation and copy number variation, multiplicity of information (gene regions, enhancer regions, and CGI regions), and circulating tumor DNA methylation profiles, available for research such as biomarker panel design, cancer comparison, diagnosis, prognosis, therapy study and identifying potential epigenetic biomarkers. MethHC 2.0 is now available at http://awi.cuhk.edu.cn/∼MethHC.

LncRNA regulation: New frontiers in epigenetic solutions to drug chemoresistance

Long-noncoding RNAs (lncRNAs) have been shown to participate in sensitizing or de-sensitizing cancer cells to chemical drugs during cancer therapeutics. Notably, a plethora of lncRNAs have been confirmed to be associated with epigenetic controllers and regulate histone protein modification or DNA methylation states in the process of gene transcription. This correlation between lncRNAs and epigenetic regulators can induce the expression of core genes to trigger drug resistance. In addition, epigenetic signatures are considered to be effective and attractive biomarkers for monitoring drug therapeutic effects because they are inheritable, dynamic, and reversible. Therefore, the regulatory mechanism between lncRNAs and epigenetic machinery can serve as a novel indicator and target to overcome or reverse drug resistance in cancer therapy. In this review, we also presented a curated selection of computational tools (including online databases and network analysis) in the area of epigenetics. A classic workflow for lncRNA expression network analysis is presented, providing guidance for non-bioinformaticians to identify significant correlation between lncRNAs and other biomolecules.

SCDb: an integrated database of stomach cancer

BACKGROUND

Stomach cancer (SC) is a type of cancer, which is derived from the stomach mucous membrane. As there are non-specific symptoms or no noticeable symptoms observed at the early stage, newly diagnosed SC cases usually reach an advanced stage and are thus difficult to cure. Therefore, in this study, we aimed to develop an integrated database of SC.

METHODS

SC-related genes were identified through literature mining and by analyzing the publicly available microarray datasets. Using the RNA-seq, miRNA-seq and clinical data downloaded from The Cancer Genome Atlas (TCGA), the Kaplan-Meier (KM) survival curves for all the SC-related genes were generated and analyzed. The miRNAs (miRanda, miRTarget2, PicTar, PITA and TargetScan databases), SC-related miRNAs (HMDD and miR2Disease databases), single nucleotide polymorphisms (SNPs, dbSNP database), and SC-related SNPs (ClinVar database) were also retrieved from the indicated databases. Moreover, gene_disease (OMIM and GAD databases), copy number variation (CNV, DGV database), methylation (PubMeth database), drug (WebGestalt database), and transcription factor (TF, TRANSFAC database) analyses were performed for the differentially expressed genes (DEGs).

RESULTS

In total, 9990 SC-related genes (including 8347 up-regulated genes and 1643 down-regulated genes) were identified, among which, 65 genes were further confirmed as SC-related genes by performing enrichment analysis. Besides this, 457 miRNAs, 20 SC-related miRNAs, 1570 SNPs, 108 SC-related SNPs, 419 TFs, 44,605 CNVs, 3404 drug-associated genes, 63 genes with methylation, and KM survival curves of 20,264 genes were obtained. By integrating these datasets, an integrated database of stomach cancer, designated as SCDb, (available at http://www.stomachcancerdb.org/) was established.

CONCLUSIONS

As a comprehensive resource for human SC, SCDb database will be very useful for performing SC-related research in future, and will thus promote the understanding of the pathogenesis of SC.

Construction of an integrated human osteosarcoma database, HOsDb, based on literature mining, microarray analysis, and database retrieval

Background

Osteosarcoma (OS) is the most frequent primary malignancy of bone with a high incidence in adolescence. This study aimed to construct a publicly available, integrated database of human OS, named HOsDb.

Methods

Microarray data, current databases, and a literature search of PubMed were used to extract information relevant to human OS-related genes and their transcription factors (TFs) and single nucleotide polymorphisms (SNPs), as well as methylation sites and microRNAs (miRNAs). This information was collated for constructing the HOsDb.

Results

In total, we identified 7191 OS tumor-related genes, 763 OS metastasis-related genes, and 1589 OS drug-related genes, corresponding to 190,362, 21,131, and 41,135 gene-TF pairs, respectively, 3,749,490, 358,361, and 767,674 gene-miRNA pairs, respectively; and 28,386, 2532, and 3943 SNPs, respectively. Additionally, 240 OS-related miRNAs, 1695 genes with copy number variations in OS, and 18 genes with methylation sites in OS were identified. These data were collated to construct the HOsDb, which is available at www.hosdatabase.com. Users can search OS-related molecules using this database.

Conclusion

The HOsDb provides a platform that is comprehensive, quick, and easily accessible, and it will enrich our current knowledge of OS.

A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer

BACKGROUND

We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations.

MATERIALS AND METHODS

We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated.

RESULTS

In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation.

CONCLUSION

We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.

Epigenetic Biomarkers in the Management of Ovarian Cancer: Current Prospectives

Ovarian cancer (OC) causes significant morbidity and mortality as neither detection nor screening of OC is currently feasible at an early stage. Difficulty to promptly diagnose OC in its early stage remains challenging due to non-specific symptoms in the early-stage of the disease, their presentation at an advanced stage and poor survival. Therefore, improved detection methods are urgently needed. In this article, we summarize the potential clinical utility of epigenetic signatures like DNA methylation, histone modifications, and microRNA dysregulation, which play important role in ovarian carcinogenesis and discuss its application in development of diagnostic, prognostic, and predictive biomarkers. Molecular characterization of epigenetic modification (methylation) in circulating cell free tumor DNA in body fluids offers novel, non-invasive approach for identification of potential promising cancer biomarkers, which can be performed at multiple time points and probably better reflects the prevailing molecular profile of cancer. Current status of epigenetic research in diagnosis of early OC and its management are discussed here with main focus on potential diagnostic biomarkers in tissue and body fluids. Rapid and point of care diagnostic applications of DNA methylation in liquid biopsy has been precluded as a result of cumbersome sample preparation with complicated conventional methods of isolation. New technologies which allow rapid identification of methylation signatures directly from blood will facilitate sample-to answer solutions thereby enabling next-generation point of care molecular diagnostics. To date, not a single epigenetic biomarker which could accurately detect ovarian cancer at an early stage in either tissue or body fluid has been reported. Taken together, the methodological drawbacks, heterogeneity associated with ovarian cancer and non-validation of the clinical utility of reported potential biomarkers in larger ovarian cancer populations has impeded the transition of epigenetic biomarkers from lab to clinical settings. Until addressed, clinical implementation as a diagnostic measure is a far way to go.

Lnc2Meth: a manually curated database of regulatory relationships between long non-coding RNAs and DNA methylation associated with human disease

Lnc2Meth (http://www.bio-bigdata.com/Lnc2Meth/), an interactive resource to identify regulatory relationships between human long non-coding RNAs (lncRNAs) and DNA methylation, is not only a manually curated collection and annotation of experimentally supported lncRNAs-DNA methylation associations but also a platform that effectively integrates tools for calculating and identifying the differentially methylated lncRNAs and protein-coding genes (PCGs) in diverse human diseases. The resource provides: (i) advanced search possibilities, e.g. retrieval of the database by searching the lncRNA symbol of interest, DNA methylation patterns, regulatory mechanisms and disease types; (ii) abundant computationally calculated DNA methylation array profiles for the lncRNAs and PCGs; (iii) the prognostic values for each hit transcript calculated from the patients clinical data; (iv) a genome browser to display the DNA methylation landscape of the lncRNA transcripts for a specific type of disease; (v) tools to re-annotate probes to lncRNA loci and identify the differential methylation patterns for lncRNAs and PCGs with user-supplied external datasets; (vi) an R package (LncDM) to complete the differentially methylated lncRNAs identification and visualization with local computers. Lnc2Meth provides a timely and valuable resource that can be applied to significantly expand our understanding of the regulatory relationships between lncRNAs and DNA methylation in various human diseases.

RMDB: An Integrated Database of Single-cytosine-resolution DNA Methylation in Oryza Sativa

Background:

Previous studies have revealed that DNA methylation plays a crucial role in eukaryotic growth and development via involvement in the regulation of gene expression and chromosomal instability. With the advancement of biotechnology, next-generation sequencing (NGS) is emerging as a popular method to explore the functions of DNA methylation, and an increasing number of genome-scale DNA methylation datasets have been published. Several DNA methylation databases, including MethDB, NGSmethDB and MENT have been developed for storing and analyzing the DNA methylation data. However, no public resource dedicated to DNA methylation of Oryza sativa is available to date.

Methods & Results:

We built a comprehensive database (RMDB) for integration and analysis of DNA methylation data of Oryza sativa. A couple of functional modules were developed to identify the connections between DNA methylation and phenotypes. Moreover, rich graphical visualization tools were employed to facilitate data presentation and interpretation.

Conclusion:

RMDB is an integrated database dedicated to rice DNA methylation. To the best of our knowledge, this is the first integrated rice DNA methylation database. We believe that RMDB will be helpful to understand the epigenetic mechanisms of Oryza sativa. RMDB is freely available at http://admis.fudan.edu.cn/rmdb.

Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach

Prostate cancer is the most common cancer type in men in Finland and second worldwide. In this paper, we analyze almost 150, 000 published papers about prostate cancer, authored by ten thousands of scientists worldwide, with an integrated text mining and computational network theory approach. We demonstrate how to integrate text mining with network analysis investigating research contributions of countries and collaborations within and between countries. Furthermore, we study the time evolution of individually and collectively studied genes. Finally, we investigate a collaboration network of Finland and compare studied genes with globally studied genes in prostate cancer genetics. Overall, our results provide a global overview of prostate cancer research in genetics. In addition, we present a specific discussion for Finland. Our results shed light on trends within the last 30 years and are useful for translational researchers within the full range from genetics to public health management and health policy.

ILDgenDB: integrated genetic knowledge resource for interstitial lung diseases (ILDs)

Interstitial lung diseases (ILDs) are a diverse group of ∼200 acute and chronic pulmonary disorders that are characterized by variable amounts of inflammation, fibrosis and architectural distortion with substantial morbidity and mortality. Inaccurate and delayed diagnoses increase the risk, especially in developing countries. Studies have indicated the significant roles of genetic elements in ILDs pathogenesis. Therefore, the first genetic knowledge resource, ILDgenDB, has been developed with an objective to provide ILDs genetic data and their integrated analyses for the better understanding of disease pathogenesis and identification of diagnostics-based biomarkers. This resource contains literature-curated disease candidate genes (DCGs) enriched with various regulatory elements that have been generated using an integrated bioinformatics workflow of databases searches, literature-mining and DCGs–microRNA (miRNAs)–single nucleotide polymorphisms (SNPs) association analyses. To provide statistical significance to disease-gene association, ILD-specificity index and hypergeomatric test scores were also incorporated. Association analyses of miRNAs, SNPs and pathways responsible for the pathogenesis of different sub-classes of ILDs were also incorporated. Manually verified 299 DCGs and their significant associations with 1932 SNPs, 2966 miRNAs and 9170 miR-polymorphisms were also provided. Furthermore, 216 literature-mined and proposed biomarkers were identified. The ILDgenDB resource provides user-friendly browsing and extensive query-based information retrieval systems. Additionally, this resource also facilitates graphical view of predicted DCGs–SNPs/miRNAs and literature associated DCGs–ILDs interactions for each ILD to facilitate efficient data interpretation. Outcomes of analyses suggested the significant involvement of immune system and defense mechanisms in ILDs pathogenesis. This resource may potentially facilitate genetic-based disease monitoring and diagnosis.

Database URL: http://14.139.240.55/ildgendb/index.php

A high-throughput 3' UTR reporter screening identifies microRNA interactomes of cancer genes

Introduction

Despite the established contribution of deregulated microRNA (miRNA) function to carcinogenesis, relatively few miRNA-cancer gene interactions have been validated, making it difficult to appreciate the true complexity of miRNA-cancer gene regulatory networks.

Results

In this effort, we identify miRNA interactomes of 17 well-established cancer genes, involved in various cancer types, through a miRNome-wide 3’ UTR reporter screening. Using a novel and performant strategy for high-throughput screening data analysis, we identify 390 interactions, quadrupling the size of the known miRNA interactome for the cancer genes under investigation. Clear enrichments of established and predicted interactions underscore the validity of the interactome data set. Interactomes appear to be primarily driven by canonical binding site interactions. Nonetheless, non-canonical binding sites, such as offset 6mer and seed-mismatched or G:U wobble sites, also have regulatory activity, albeit clearly less pronounced. Furthermore, we observe enhanced regulation in the presence of 3’ supplementary pairing for both canonical and non-canonical binding sites.

Conclusions

Altogether, the cancer gene-miRNA interactome data set represents a unique resource that will aid in the unraveling of regulatory miRNA networks and the dynamic regulation of key protein-coding cancer genes. In addition, it uncovers aspects of the functional miRNA binding site’s architecture and the relative contributions of different binding site types.

NGSmethDB 2017: enhanced methylomes and differential methylation

The 2017 update of NGSmethDB stores whole genome methylomes generated from short-read data sets obtained by bisulfite sequencing (WGBS) technology. To generate high-quality methylomes, stringent quality controls were integrated with third-part software, adding also a two-step mapping process to exploit the advantages of the new genome assembly models. The samples were all profiled under constant parameter settings, thus enabling comparative downstream analyses. Besides a significant increase in the number of samples, NGSmethDB now includes two additional data-types, which are a valuable resource for the discovery of methylation epigenetic biomarkers: (i) differentially methylated single-cytosines; and (ii) methylation segments (i.e. genome regions of homogeneous methylation). The NGSmethDB back-end is now based on MongoDB, a NoSQL hierarchical database using JSON-formatted documents and dynamic schemas, thus accelerating sample comparative analyses. Besides conventional database dumps, track hubs were implemented, which improved database access, visualization in genome browsers and comparative analyses to third-part annotations. In addition, the database can be also accessed through a RESTful API. Lastly, a Python client and a multiplatform virtual machine allow for program-driven access from user desktop. This way, private methylation data can be compared to NGSmethDB without the need to upload them to public servers. Database website: http://bioinfo2.ugr.es/NGSmethDB.

Integrating Epigenomics into the Understanding of Biomedical Insight

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

DiseaseMeth version 2.0: a major expansion and update of the human disease methylation database

The human disease methylation database (DiseaseMeth, http://bioinfo.hrbmu.edu.cn/diseasemeth/) is an interactive database that aims to present the most complete collection and annotation of aberrant DNA methylation in human diseases, especially various cancers. Recently, the high-throughput microarray and sequencing technologies have promoted the production of methylome data that contain comprehensive knowledge of human diseases. In this DiseaseMeth update, we have increased the number of samples from 3610 to 32 701, the number of diseases from 72 to 88 and the disease–gene associations from 216 201 to 679 602. DiseaseMeth version 2.0 provides an expanded comprehensive list of disease–gene associations based on manual curation from experimental studies and computational identification from high-throughput methylome data. Besides the data expansion, we also updated the search engine and visualization tools. In particular, we enhanced the differential analysis tools, which now enable online automated identification of DNA methylation abnormalities in human disease in a case-control or disease–disease manner. To facilitate further mining of the disease methylome, three new web tools were developed for cluster analysis, functional annotation and survival analysis. DiseaseMeth version 2.0 should be a useful resource platform for further understanding the molecular mechanisms of human diseases.

AAgAtlas 1.0: a human autoantigen database

Autoantibodies refer to antibodies that target self-antigens, which can play pivotal roles in maintaining homeostasis, distinguishing normal from tumor tissue and trigger autoimmune diseases. In the last three decades, tremendous efforts have been devoted to elucidate the generation, evolution and functions of autoantibodies, as well as their target autoantigens. However, reports of these countless previously identified autoantigens are randomly dispersed in the literature. Here, we constructed an AAgAtlas database 1.0 using text-mining and manual curation. We extracted 45 830 autoantigen-related abstracts and 94 313 sentences from PubMed using the keywords of either ‘autoantigen’ or ‘autoantibody’ or their lexical variants, which were further refined to 25 520 abstracts, 43 253 sentences and 3984 candidates by our bio-entity recognizer based on the Protein Ontology. Finally, we identified 1126 genes as human autoantigens and 1071 related human diseases, with which we constructed a human autoantigen database (AAgAtlas database 1.0). The database provides a user-friendly interface to conveniently browse, retrieve and download human autoantigens as well as their associated diseases. The database is freely accessible at http://biokb.ncpsb.org/aagatlas/. We believe this database will be a valuable resource to track and understand human autoantigens as well as to investigate their functions in basic and translational research.

MethSMRT: an integrative database for DNA N6-methyladenine and N4-methylcytosine generated by single-molecular real-time sequencing

DNA methylation is an important type of epigenetic modifications, where 5- methylcytosine (5mC), 6-methyadenine (6mA) and 4-methylcytosine (4mC) are the most common types. Previous efforts have been largely focused on 5mC, providing invaluable insights into epigenetic regulation through DNA methylation. Recently developed single-molecule real-time (SMRT) sequencing technology provides a unique opportunity to detect the less studied DNA 6mA and 4mC modifications at single-nucleotide resolution. With a rapidly increased amount of SMRT sequencing data generated, there is an emerging demand to systematically explore DNA 6mA and 4mC modifications from these data sets. MethSMRT is the first resource hosting DNA 6mA and 4mC methylomes. All the data sets were processed using the same analysis pipeline with the same quality control. The current version of the database provides a platform to store, browse, search and download epigenome-wide methylation profiles of 156 species, including seven eukaryotes such as Arabidopsis, C. elegans, Drosophila, mouse and yeast, as well as 149 prokaryotes. It also offers a genome browser to visualize the methylation sites and related information such as single nucleotide polymorphisms (SNP) and genomic annotation. Furthermore, the database provides a quick summary of statistics of methylome of 6mA and 4mC and predicted methylation motifs for each species. MethSMRT is publicly available at http://sysbio.sysu.edu.cn/methsmrt/ without use restriction.

SparkText: Biomedical Text Mining on Big Data Framework

Background

Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment.

Results

In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes.

Conclusions

This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

Methylation of a panel of genes in peripheral blood leukocytes is associated with colorectal cancer

The relationship between the DNA methylation status of the CpG islands of multiple genes in blood leukocytes in CRC susceptibility and prognosis, as well as possible interactions with dietary factors on CRC risk are unclear. We carried out a case-control study including 421 CRC patients and 506 controls to examine the associations between six genes (AOX-1, RARB2, RERG, ADAMTS9, IRF4, and FOXE-1), multiple CpG site methylation (MCSM) and susceptibility to CRC. High-level MCSM (MCSM-H) was defined as methylation of greater than or equal to 2 of 5 candidate genes (except for RARB2); low-level MCSM (MCSM-L) was when 1 candidate gene was methylated; non-MCSM was when none of the candidate genes were methylated. Blood cell-derived DNA methylation status was detected using methylation-sensitive high-resolution melting analysis. The hypermethylation status of each individual gene was statistically significantly associated with CRC. MCSM status was also associated with CRC (OR = 1.54, 95% CI: 1.15–2.05, P = 0.004). We observed interactions between a high level of dietary intake of cereals, pungent food, and stewed fish with brown sauce, age (older than 60 yrs), smoking and hypermethylation on risk of CRC. MCSM in peripheral blood DNA may be an important biomarker for susceptibility to CRC.

Identification of Cancer Related Genes Using a Comprehensive Map of Human Gene Expression

Rapid accumulation and availability of gene expression datasets in public repositories have enabled large-scale meta-analyses of combined data. The richness of cross-experiment data has provided new biological insights, including identification of new cancer genes. In this study, we compiled a human gene expression dataset from ∼40,000 publicly available Affymetrix HG-U133Plus2 arrays. After strict quality control and data normalisation the data was quantified in an expression matrix of ∼20,000 genes and ∼28,000 samples. To enable different ways of sample grouping, existing annotations where subjected to systematic ontology assisted categorisation and manual curation. Groups like normal tissues, neoplasmic tissues, cell lines, homoeotic cells and incompletely differentiated cells were created. Unsupervised analysis of the data confirmed global structure of expression consistent with earlier analysis but with more details revealed due to increased resolution. A suitable mixed-effects linear model was used to further investigate gene expression in solid tissue tumours, and to compare these with the respective healthy solid tissues. The analysis identified 1,285 genes with systematic expression change in cancer. The list is significantly enriched with known cancer genes from large, public, peer-reviewed databases, whereas the remaining ones are proposed as new cancer gene candidates. The compiled dataset is publicly available in the ArrayExpress Archive. It contains the most diverse collection of biological samples, making it the largest systematically annotated gene expression dataset of its kind in the public domain.

NEpiC: a network-assisted algorithm for epigenetic studies using mean and variance combined signals

DNA methylation plays an important role in many biological processes. Existing epigenome-wide association studies (EWAS) have successfully identified aberrantly methylated genes in many diseases and disorders with most studies focusing on analysing methylation sites one at a time. Incorporating prior biological information such as biological networks has been proven to be powerful in identifying disease-associated genes in both gene expression studies and genome-wide association studies (GWAS) but has been under studied in EWAS. Although recent studies have noticed that there are differences in methylation variation in different groups, only a few existing methods consider variance signals in DNA methylation studies. Here, we present a network-assisted algorithm, NEpiC, that combines both mean and variance signals in searching for differentially methylated sub-networks using the protein–protein interaction (PPI) network. In simulation studies, we demonstrate the power gain from using both the prior biological information and variance signals compared to using either of the two or neither information. Applications to several DNA methylation datasets from the Cancer Genome Atlas (TCGA) project and DNA methylation data on hepatocellular carcinoma (HCC) from the Columbia University Medical Center (CUMC) suggest that the proposed NEpiC algorithm identifies more cancer-related genes and generates better replication results.

DREMECELS: A Curated Database for Base Excision and Mismatch Repair Mechanisms Associated Human Malignancies

DNA repair mechanisms act as a warrior combating various damaging processes that ensue critical malignancies. DREMECELS was designed considering the malignancies with frequent alterations in DNA repair pathways, that is, colorectal and endometrial cancers, associated with Lynch syndrome (also known as HNPCC). Since lynch syndrome carries high risk (~40–60%) for both cancers, therefore we decided to cover all three diseases in this portal. Although a large population is presently affected by these malignancies, many resources are available for various cancer types but no database archives information on the genes specifically for only these cancers and disorders. The database contains 156 genes and two repair mechanisms, base excision repair (BER) and mismatch repair (MMR). Other parameters include some of the regulatory processes that have roles in these disease progressions due to incompetent repair mechanisms, specifically BER and MMR. However, our unique database mainly provides qualitative and quantitative information on these cancer types along with methylation, drug sensitivity, miRNAs, copy number variation (CNV) and somatic mutations data. This database would serve the scientific community by providing integrated information on these disease types, thus sustaining diagnostic and therapeutic processes. This repository would serve as an excellent accompaniment for researchers and biomedical professionals and facilitate in understanding such critical diseases. DREMECELS is publicly available at http://www.bioinfoindia.org/dremecels.

The identification of age-associated cancer markers by an integrative analysis of dynamic DNA methylation changes

As one of the most widely studied epigenetic modifications, DNA methylation has an important influence on human traits and cancers. Dynamic variations in DNA methylation have been reported in malignant neoplasm and aging; however, the mechanisms remain poorly understood. By constructing an age-associated and cancer-related weighted network (ACWN) based on the correlation of the methylation level and the protein-protein interaction, we found that DNA methylation changes associated with age were closely related to the occurrence of cancer. Additional analysis of 102 module genes mined from the ACWN revealed discrimination based on two main patterns. One pattern involved methylation levels that increased with aging and were higher in cancer patients compared with normal controls (HH pattern). The other pattern involved methylation levels that decreased with aging and were lower in cancer compared with normal (LL pattern). Upon incorporation with gene expression levels, 25 genes were filtered based on negative regulation by DNA methylation. These genes were regarded as potential cancer risk markers that were influenced by age in the process of carcinogenesis. Our results will facilitate further studies regarding the impact of the epigenetic effects of aging on diseases and will aid in the development of tailored cancer preventive strategies.

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

ViralEpi v1.0: a high-throughput spectrum of viral epigenomic methylation profiles from diverse diseases

AIMS

To develop a computational resource for viral epigenomic methylation profiles from diverse diseases.

MATERIALS & METHODS

Methylation patterns of Epstein-Barr virus and hepatitis B virus genomic regions are provided as web platform developed using open source Linux-Apache-MySQL-PHP (LAMP) bundle: programming and scripting languages, that is, HTML, JavaScript and PERL.

RESULTS

A comprehensive and integrated web resource ViralEpi v1.0 is developed providing well-organized compendium of methylation events and statistical analysis associated with several diseases. Additionally, it also facilitates 'Viral EpiGenome Browser' for user-affable browsing experience using JavaScript-based JBrowse.

CONCLUSION

This web resource would be helpful for research community engaged in studying epigenetic biomarkers for appropriate prognosis and diagnosis of diseases and its various stages.

Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers

Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource.

Genome-Wide Identification of a Methylation Gene Panel as a Prognostic Biomarker in Nasopharyngeal Carcinoma

DNA methylation, the best known epigenetic marker, can be used as a prognostic biomarker in many cancers. We examined DNA methylation status and survival in nasopharyngeal carcinoma (NPC) patients. Aberrant DNA-methylated genes in 24 NPC tissues and 24 noncancer nasopharyngitis biopsy tissues (NCNBT) were identified using Illumina 450K BeadChip. Correlations between DNA methylation and clinical outcomes were evaluated using bisulfite pyrosequencing in 454 NPC patients. Genome-wide methylation analysis demonstrated that NPC tissues had distinct DNA methylation patterns compared with NCNBT. Among all significant CpG sites, 2,173 CpG sites with β change ≥ 0.2 (1,880 hypermethylated, 293 hypomethylated) were identified (P < 0.05). A methylation gene panel comprising six hypermethylated genes was constructed with the average Z-score method. Patients in the training cohort with high methylation had poorer disease-free survival [DFS, HR, 2.26; 95% confidence interval (CI), 1.28-4.01; P, 0.005] and overall survival (OS, HR, 2.47; 95% CI, 1.30-4.71; P, 0.006) than those with low methylation. There were similar results in the validation (DFS, HR, 2.07; 95% CI, 1.17-3.67; P, 0.013; OS, HR, 1.83; 95% CI, 1.01-3.31; P, 0.046) and independent cohorts (DFS, HR, 1.94; 95% CI, 1.08-3.47; P, 0.026; OS, HR, 2.09; 95% CI, 1.10-3.98; P, 0.022). Analysis indicated that the methylation gene panel was an independent prognostic factor. Furthermore, patients with low methylation had a favorable response to concurrent chemotherapy with an improved DFS (P = 0.045) and OS (P = 0.031), whereas patients with high methylation did not benefit from concurrent chemotherapy. The six-hypermethylated gene panel was associated with poor survival in patients with NPC, demonstrating its potential usefulness as a prognostic biomarker to clinicians in NPC management.

Detection of novel germline mutations for breast cancer in non-BRCA1/2 families

The identification of the breast cancer susceptibility genes BRCA1 and BRCA2 enhanced clinicians' ability to select high-risk individuals for aggressive surveillance and prevention, and led to the development of targeted therapies. However, BRCA1/2 mutations account for only 25% of familial breast cancer cases. To systematically identify rare, probably pathogenic variants in familial cases of breast cancer without BRCA1/2 mutations, we developed a list of 312 genes, and performed targeted DNA enrichment coupled to multiplex next-generation sequencing on 104 'BRCAx' patients and 101 geographically matched controls in Ireland. As expected, this strategy allowed us to identify mutations in several well-known high-susceptibility and moderate-susceptibility genes, including ATM (~ 5%), RAD50 (~ 3%), CHEK2 (~ 2%), TP53 (~ 1%), PALB2 (~ 1%), and MRE11A (~ 1%). However, we also identified novel pathogenic variants in 30 other genes, which, when taken together, potentially explain the etiology of the missing heritability in up to 35% of BRCAx patients. These included novel potential pathogenic mutations in MAP3K1, CASP8, RAD51B, ZNF217, CDKN2B-AS1, and ERBB2, including a splice site mutation, which we predict would generate a constitutively active HER2 protein. Taken together, this work extends our understanding of the genetics of familial breast cancer, and supports the need to implement hereditary multigene panel testing to more appropriately orientate clinical management.

DDMGD: the database of text-mined associations between genes methylated in diseases from different species

Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases.

MethHC: a database of DNA methylation and gene expression in human cancer

We present MethHC (http://MethHC.mbc.nctu.edu.tw), a database comprising a systematic integration of a large collection of DNA methylation data and mRNA/microRNA expression profiles in human cancer. DNA methylation is an important epigenetic regulator of gene transcription, and genes with high levels of DNA methylation in their promoter regions are transcriptionally silent. Increasing numbers of DNA methylation and mRNA/microRNA expression profiles are being published in different public repositories. These data can help researchers to identify epigenetic patterns that are important for carcinogenesis. MethHC integrates data such as DNA methylation, mRNA expression, DNA methylation of microRNA gene and microRNA expression to identify correlations between DNA methylation and mRNA/microRNA expression from TCGA (The Cancer Genome Atlas), which includes 18 human cancers in more than 6000 samples, 6548 microarrays and 12 567 RNA sequencing data.

Human cancer databases (review)

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation

BACKGROUND

Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN).

RESULTS

Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia.

CONCLUSIONS

Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.

MethBank: a database integrating next-generation sequencing single-base-resolution DNA methylation programming data

DNA methylation plays crucial roles during embryonic development. Here we present MethBank (http://dnamethylome.org), a DNA methylome programming database that integrates the genome-wide single-base nucleotide methylomes of gametes and early embryos in different model organisms. Unlike extant relevant databases, MethBank incorporates the whole-genome single-base-resolution methylomes of gametes and early embryos at multiple different developmental stages in zebrafish and mouse. MethBank allows users to retrieve methylation levels, differentially methylated regions, CpG islands, gene expression profiles and genetic polymorphisms for a specific gene or genomic region. Moreover, it offers a methylome browser that is capable of visualizing high-resolution DNA methylation profiles as well as other related data in an interactive manner and thus is of great helpfulness for users to investigate methylation patterns and changes of gametes and early embryos at different developmental stages. Ongoing efforts are focused on incorporation of methylomes and related data from other organisms. Together, MethBank features integration and visualization of high-resolution DNA methylation data as well as other related data, enabling identification of potential DNA methylation signatures in different developmental stages and accordingly providing an important resource for the epigenetic and developmental studies.

miRBase Tracker: keeping track of microRNA annotation changes

Since 2002, information on individual microRNAs (miRNAs), such as reference names and sequences, has been stored in miRBase, the reference database for miRNA annotation. As a result of progressive insights into the miRNome and its complexity, miRBase underwent addition and deletion of miRNA records, changes in annotated miRNA sequences and adoption of more complex naming schemes over time. Unfortunately, miRBase does not allow straightforward assessment of these ongoing miRNA annotation changes, which has resulted in substantial ambiguity regarding miRNA identity and sequence in public literature, in target prediction databases and in content on various commercially available analytical platforms. As a result, correct interpretation, comparison and integration of miRNA study results are compromised, which we demonstrate here by assessing the impact of ignoring sequence annotation changes. To address this problem, we developed miRBase Tracker (www.mirbasetracker.org), an easy-to-use online database that keeps track of all historical and current miRNA annotation present in the miRBase database. Three basic functionalities allow researchers to keep their miRNA annotation up-to-date, reannotate analytical miRNA platforms and link published results with outdated annotation to the latest miRBase release. We expect miRBase Tracker to increase the transparency and annotation accuracy in the field of miRNA research.

Database URL:www.mirbasetracker.org

DNA Methylation at the Novel CpG Sites in the Promoter of MED15/PCQAP Gene as a Biomarker for Head and Neck Cancers

Head and neck cancers (HNCs) represent a significant and ever-growing burden to the modern society, mainly due to the lack of early diagnostic methods. A significant number of HNCs is often associated with drinking, smoking, chewing beetle nut, and human papilloma virus (HPV) infections. We have analyzed DNA methylation patterns in tumor and normal tissue samples collected from head and neck squamous cell carcinoma (HNSCC) patients who were smokers. We have identified novel methylation sites in the promoter of the mediator complex subunit 15 (MED15/PCQAP) gene (encoing a co-factor important for regulation of transcription initiation for promoters of many genes), hypermethylated specifically in tumor cells. Two clusters of CpG dinucleotides methylated in tumors, but not in normal tissue from the same patients, were identified. These CpG methylation events in saliva samples were further validated in a separate cohort of HNSCC patients (who developed cancer due to smoking or HPV infections) and healthy controls using methylation-specific PCR (MSP). We used saliva as a biological medium because of its non-invasive nature, close proximity to the tumors, easiness and it is an economically viable option for large-scale screening studies. The methylation levels for the two identified CpG clusters were significantly different between the saliva samples collected from healthy controls and HNSCC individuals (Welch’s t-test returning P < 0.05 and Mann–Whitney test P < 0.01 for both). The developed MSP assays also provided a good discriminative ability with AUC values of 0.70 (P < 0.01) and 0.63 (P < 0.05). The identified novel CpG methylation sites may serve as potential non-invasive biomarkers for detecting HNSCC.

OncoSearch: cancer gene search engine with literature evidence

In order to identify genes that are involved in oncogenesis and to understand how such genes affect cancers, abnormal gene expressions in cancers are actively studied. For an efficient access to the results of such studies that are reported in biomedical literature, the relevant information is accumulated via text-mining tools and made available through the Web. However, current Web tools are not yet tailored enough to allow queries that specify how a cancer changes along with the change in gene expression level, which is an important piece of information to understand an involved gene's role in cancer progression or regression. OncoSearch is a Web-based engine that searches Medline abstracts for sentences that mention gene expression changes in cancers, with queries that specify (i) whether a gene expression level is up-regulated or down-regulated, (ii) whether a certain type of cancer progresses or regresses along with such gene expression change and (iii) the expected role of the gene in the cancer. OncoSearch is available through http://oncosearch.biopathway.org.

DNA hypermethylation in prostate cancer is a consequence of aberrant epithelial differentiation and hyperproliferation

Prostate cancer (CaP) is mostly composed of luminal-like differentiated cells, but contains a small subpopulation of basal cells (including stem-like cells), which can proliferate and differentiate into luminal-like cells. In cancers, CpG island hypermethylation has been associated with gene downregulation, but the causal relationship between the two phenomena is still debated. Here we clarify the origin and function of CpG island hypermethylation in CaP, in the context of a cancer cell hierarchy and epithelial differentiation, by analysis of separated basal and luminal cells from cancers. For a set of genes (including GSTP1) that are hypermethylated in CaP, gene downregulation is the result of cell differentiation and is not cancer specific. Hypermethylation is however seen in more differentiated cancer cells and is promoted by hyperproliferation. These genes are maintained as actively expressed and methylation-free in undifferentiated CaP cells, and their hypermethylation is not essential for either tumour development or expansion. We present evidence for the causes and the dynamics of CpG island hypermethylation in CaP, showing that, for a specific set of genes, promoter methylation is downstream of gene downregulation and is not a driver of gene repression, while gene repression is a result of tissue-specific differentiation.

A computational model for genetic and epigenetic signals in colon cancer

Cancer, a class of diseases, characterized by abnormal cell growth, has one of the highest overall death rates world-wide. Its development has been linked to aberrant genetic and epigenetic events, affecting the regulation of key genes that control cellular mechanisms. However, a major issue in cancer research is the lack of precise information on tumour pathways; therefore, the delineation of these and of the processes underlying disease proliferation is an important area of investigation. A computational approach to modelling malignant system events can help to improve understanding likely "triggers", i.e. initiating abnormal micro-molecular signals that occur during cancer development. Here, we introduce a network-based model for genetic and epigenetic events observed at different stages of colon cancer, with a focus on the gene relationships and tumour pathways. Additionally, we describe a case study on tumour progression recorded for two gene networks on colon cancer, carcinoma in situ. Our results to date showed that tumour progression rate is higher for a small, closely-associated network of genes than for a larger, less-connected set; thus, disease development depends on assessment of network properties. The current work aims to provide improved insight on the way in which aberrant modifications characterize cancer initiation and progression. The framework dynamics are described in terms of interdependencies between three main layers: genetic and epigenetic events, gene relationships and cancer stage levels.

NGSmethDB: an updated genome resource for high quality, single-cytosine resolution methylomes

The updated release of ‘NGSmethDB’ (http://bioinfo2.ugr.es/NGSmethDB) is a repository for single-base whole-genome methylome maps for the best-assembled eukaryotic genomes. Short-read data sets from NGS bisulfite-sequencing projects of cell lines, fresh and pathological tissues are first pre-processed and aligned to the corresponding reference genome, and then the cytosine methylation levels are profiled. One major improvement is the application of a unique bioinformatics protocol to all data sets, thereby assuring the comparability of all values with each other. We implemented stringent quality controls to minimize important error sources, such as sequencing errors, bisulfite failures, clonal reads or single nucleotide variants (SNVs). This leads to reliable and high-quality methylomes, all obtained under uniform settings. Another significant improvement is the detection in parallel of SNVs, which might be crucial for many downstream analyses (e.g. SNVs and differential-methylation relationships). A next-generation methylation browser allows fast and smooth scrolling and zooming, thus speeding data download/upload, at the same time requiring fewer server resources. Several data mining tools allow the comparison/retrieval of methylation levels in different tissues or genome regions. NGSmethDB methylomes are also available as native tracks through a UCSC hub, which allows comparison with a wide range of third-party annotations, in particular phenotype or disease annotations.

EPITRANS: a database that integrates epigenome and transcriptome data

Epigenetic modifications affect gene expression and thereby govern a wide range of biological processes such as differentiation, development and tumorigenesis. Recent initiatives to define genome-wide DNA methylation and histone modification profiles by microarray and sequencing methods have led to the construction of databases. These databases are repositories for international epigenetic consortiums or provide mining results from PubMed, but do not integrate the epigenetic information with gene expression changes. In order to overcome this limitation, we constructed EPITRANS, a novel database that visualizes the relationships between gene expression and epigenetic modifications. EPITRANS uses combined analysis of epigenetic modification and gene expression to search for cell function-related epigenetic and transcriptomic alterations (Freely available on the web at http://epitrans.org ).