dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers
dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers
Zhen Yang 2
Liangcai Wu 1
Anqiang Wang 1
Wei Tang 0
Yi Zhao 4
Haitao Zhao 1
Andrew E. Teschendorff 2 3
0 School of Biotechnology Engineering, Tianjin University , 135 Yaguan Road, Jinnan District, Tianjin , China
1 Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC) , 1 Shuaifuyuan, Wangfujing, Beijing 100730 , China
2 Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology , 320 Yue Yang Road, Shanghai 200031 , China
3 Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London , 72 Huntley Street, London WC1E 6BT , UK
4 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190 , China
MicroRNAs (miRNAs) are often deregulated in cancer and are thought to play an important role in cancer development. Large amount of differentially expressed miRNAs have been identified in various cancers by using high-throughput methods. It is therefore quite important to make a comprehensive collection of these miRNAs and to decipher their roles in oncogenesis and tumor progression. In 2010, we presented the first release of dbDEMC, representing a database for collection of differentially expressed miRNAs in human cancers obtained from microarray data. Here we describe an update of the database. dbDEMC 2.0 documents 209 expression profiling data sets across 36 cancer types and 73 subtypes, and a total of 2224 differentially expressed miRNAs were identified. An easy-to-use web interface was constructed that allows users to make a quick search of the differentially expressed miRNAs in certain cancer types. In addition, a new function of 'meta-profiling' was added to view differential expression events according to user-defined miRNAs and cancer types. We expect this database to continue to serve as a valuable source for cancer investigation and potential clinical application related to miRNAs. dbDEMC 2.0 is freely available at http://www.picb.ac.cn/dbDEMC.
As the leading cause of human death, cancer continues to
represent a huge economic and social burden to society.
It currently accounts for >8 million deaths according to
World Cancer Report 2014 (1), and the incidence and
mortality rates of many cancer types have been increasing over
the last years. It is therefore urgent to understand the
molecular mechanisms underlying cancer development and to
develop reliable biomarkers for early detection, diagnosis and
treatment of cancer.
In recent years, large numbers of studies have indicated
that microRNAs (miRNAs), a class of small noncoding
RNAs (∼18–23 nt in length), are highly associated with
initiation and progression of cancer. MiRNAs mainly function
to regulate mRNA expression through sequence specific
interaction with 3’-untranslated regions (3’-UTRs) (2,3). To
date, more than 2500 miRNAs have been identified in the
human genome according to latest release of miRBase (4).
Studies have indicated that miRNAs play important roles
in a wide range of physiological and biological processes,
such as: cell division, proliferation, differentiation,
development, metabolism and apoptosis (5–8). Hence, many
miRNAs could function as oncogenes or tumor suppressors
by regulating different cancer associated genes. Over the
last decade, associations between alterations in miRNA
expression and the occurrence of cancer has been the
subject of intense investigation (9). MiRNAs have emerged as
an important kind of diagnostic, prognostic and
predictive biomarker for different types of cancer, and earned a
promising role for cancer biology (10,11). In addition,
miRNAs offer potential new avenues for cancer treatment, for
instance by using miRNA mimics or antogomirs (12,13).
In the past years, genome wide detection methods
including microarray and next generation sequencing have
been developed to identify miRNA profiles in a variety
of cancer types (14,15). Huge amount of miRNA
expression profiling data for cancers was released by public
resources, such as the Gene Expression Omnibus (GEO) and
other cancer projects including The Cancer Genome
Atlas (TCGA), and the International Cancer Genome
Consortium (ICGC). These valuable resources provide an
opportunity to investigate cancer associated miRNAs from
large amount of samples. However, these miRNA–cancer
relationships are buried in thousands of published studies.
It is still a challenging task to translate this huge amount
of miRNA expression data into a systematically annotated
and documented database for ease of interpretation.
Previously, we developed dbDEMC (16), a database of
miRNA expression changes in different cancer types. It
differs from other databases that used mainly manually
collection or text mining methods, such as miR2Disease
(17), HMDD (18), miR2cancer (19), TUMIR (20) or
OncomiRDB (21), to identify cancer associated miRNAs, in
that dbDEMC identifies these differentially expressed
miRNAs from de-novo analysis of high-throughput expression
data. The first version of dbDEMC provides researchers an
easy to use resource to retrieve cancer related miRNAs in
fourteen cancer types. Each miRNA entry provides
differential expression pattern seen in these cancer types, and also
the annotation from different experiments. Since its first
release in 2010, many more cancer miRNA expression
profiling studies have been published. It is therefore paramount
to update the database to keep a pace with the rate of data
accrual. Here we introduce dbDEMC 2.0, an updated and
significantly expanded version of this database. The
second version of dbDEMC documents a total of 2224
differentially expressed miRNAs from 36 cancer types through
the processing of >200 expression data sets. In addition to
the expanded data volume, the search and browser
functions are retained and enhanced, and new features have been
added for better usage of the database. We expect this
updated database could facilitate the identification of cancer
associated miRNAs and benefit the investigation of their
roles in physiological and pathological processes of cancer
development. All the information in dbDEMC 2.0 is freely
available to the public domain through http://www.picb.ac.
DATA COLLECTION AND PROCESSING
We conducted a systematic data search for cancer related
miRNA expression profiles by using cancer-related
keywords, such as: ‘cancer’, ‘tumor’, ‘carcinoma’ and
‘neoplasm’, in combination with ‘microRNA’ or ‘miRNA’ from
GEO. All the data sets used are limited to human studies
published before June 2016. In addition, we also collected
high quality miRNA expression profiles for 22 cancer types
generated by miRNA-seq from TCGA. More than 400 data
sets were collected initially. The data processing procedure
is as follows:
i. For data quality control, we made a rigorous manual
review for each data set to screen those meeting the aim of
this study initially. To ensure that only high quality data
sets were included in our database, only the data sets
have enough samples for both case and control (at least
three) groups were used. After this quality control step,
a total of 209 miRNA expression data sets remained.
ii. For each dataset, the expression values were
logarithmically transformed (base 2) and quantile normalized.
For miRNA expression data sets get from TCGA, we
used miRNA isoforms expression data as it provides the
mature miRNA expression information. The maximum
expression value was selected if there were multiple
isoforms for a given miRNA in each sample. The limma
(Linear Models for Microarray and RNA-seq Data)
package (22) embedded in R (http://www.r-project.org/)
was used to select miRNAs whose mean expression level
is significantly different between case and control
samples. The population level control and one factor
analysis were used. Those miRNAs with FDR adjusted
Pvalue <0.05 were extracted as candidates that have
significant different expression.
iii. The inconsistency of the miRNA name annotated in
different updates from miRBase could result in
ambiguities for the miRNA names from different
expression platforms. To overcome this problem, we used
miRBase-tracker (23) to unify all the miRNA names to
those annotated in the most recent release of the
miRBase. In addition, the miRNA IDs were also uniformly
mapped to the HUGO Gene Nomenclature
Committee (HGNC) (24), Entrez Gene ID (25) and Ensemble
Gene ID (26). The sequences for mature and
precursor miRNAs from miRBase were integrated.
Furthermore, the cancer names were also unified into 36
different cancer tissues: for example, ‘lung squamous cell
carcinoma’, ‘lung adenocarcinoma’, ‘small cell lung
cancer’ and ‘large cell lung cancer’ were grouped into the
‘lung cancer’, but decimated by the ‘cancer subtype’.
iv. In addition to the miRNA expression data generated
from microarray and miRNA-seq, low-throughput data
in the original articles such as real-time PCR and
northern blot, etc. were manually collected. These types of
information were also integrated into database as
validation of results obtained from high-throughput
methods and represented separately in the database. The
flowchart for data collection and database construction
is shown in Supplementary Figure S1.
For dbDEMC 2.0, all the data are managed by a relational
database implemented with MySQL. The dynamic web
processing and the tool modules implementation for the
database were implemented by in-house R scripts. Apache
was used for the http server.
In this current release, dbDEMC documents 209 miRNA
expression data sets from 143 peer-reviewed publications
and also those from TCGA (Supplementary Table S1). It
now contains 49 202 miRNA–cancer associations for 2224
differentially expressed miRNAs identified from 436
experiments, and a total of 36 cancer types and 73 cancer
subtypes were covered (Supplementary Table S2). The number
of differentially expressed miRNAs in this version accounts
for 86% of the miRNAs identified in the human genome
(miRBase Release 21). Figure 1A illustrates the number of
differentially expressed miRNAs for each cancer type. Take
the colon cancer, the one with highest number of miRNAs,
as an example, a total of 2116 miRNAs were identified to
be differentially expressed, among which 1116 are
upregulated and 1000 are downregulated. In addition, gastric
cancer and pancreatic cancer are also among the top ranked
cancer types. The number of miRNAs identified for breast
cancer, esophageal cancer, lung cancer and hepatocellular
carcinoma are also increased dramatically over the first
release. The number of differentially expressed miRNAs that
validated by low-throughput methods across major cancer
types is depicted as Figure 1B, brain cancer, colon cancer
and breast cancer are top ranked cancer types.
NEW FEATURES AND DATABASE UTILITY
Improved experimental description
For each of the microarray data sets presented, we reviewed
the samples profiled and classified the experiments for
differential expression as one of the following categories:
cancer versus respective normal tissue, high grade cancer
versus low grade cancer, metastasis versus primary cancer,
subtype1 versus subtype2 (include histological subtypes
comparison or molecular subtypes comparison), poor outcome
versus good outcome (includes recurrence versus
nonrecurrence, long-term versus short-term survival and
canerspecific death versus alive), blood samples from cancer
patient versus blood samples from normal person, and also
drug-treatment versus non-treatment sample. After the
assignment of samples to different classes, each miRNA was
assessed for differential expression with the limma package.
For each experiment, a detail information page was
constructed to delineate the related publication reference, GEO
expression profile description, experimental design, cancer
type and subtypes, sample information, miRNA
quantification procedure and the total number of miRNAs
identified. The percentage of top ranked cancer types for all the
experiments was depicted as Figure 1C, the breast cancer
constitute the largest part of 13% of the total experiments,
then followed by lung cancer and kidney cancer. Whereas
for the experimental design, cancer vs. normal comparison
and high grade versus low grade comparison account for
the most of the total experiments (Figure 1D).
Database query and searching tools
We provide several ways to allow database query. First,
users can perform a quick search in dbDEMC 2.0 by
using miRNA names from the ‘Search’ page (Figure 2A). By
imputing the interested miRNA name in the textbox as the
keyword, the search engine will search all the items that
contain the query miRNA in database. The search result page
briefly lists the associated GEO ID, cancer types, subtype,
experimental design, and log Fold Change between case and
control samples (Figure 2B). Multiple miRNAs can be
submitted at a time. Secondly, we also provide the sequence
similarity search tools implemented by using BLAST, which
allow user to determine whether an unknown miRNA is
overlapped with the existing miRNAs by using the miRNA
sequence. In addition, users can also select particular cancer
type or subtype and browse all related experiments from the
‘Browse’ page (Figure 2C). The experiment ID,
experimental design, case sample, control sample and the number of
upregulated and downregulated miRNAs will be listed
(Figure 2D). After the experiment list obtained, users can select
particular experiment and click the ‘view miRNAs’ button
to navigate the differentially expressed miRNA list.
Enhanced miRNA page
By clicking the hyperlink of a particular miRNA ID, users
can view the detailed expression information of a specific
miRNA (Figure 2E). In this updated version, the detailed
expression information page has also been enhanced. This
page mainly consists of four sections: miRNA Summary,
Expression Profile and Expression Detail and Validation.
In the ‘Summary’ section, miRNA ID, miRBase
accession number, sequences for both mature miRNA and the
precursor miRNA are listed. The hyperlinks to external
databases including HUGO, Entrez gene and Eensembl
are provided, In addition, this page also provides the
links to predicted miRNA target databases including
TargetScan (27), DIANA-microT-CDS (28) and RNA22v2
(29). The ‘Expression Profile’ section demonstrates six
different heatmaps of the differential expression profile across
six types of the experimental design. The heatmap indicates
the number of experiments to support the conclusion of
upregulation and downregulation in each cancer type. Here,
the expression profiling heatmap for drug treatment
samples analysis were not included due to the heterogeneity of
different studies, such as different small molecule or
various experimental conditions used. In the ‘Expression
Detail’ section, a list of experiment ID, cancer types and
subtypes, experimental design and the differential expression
results getting from limma, such as the log Fold Change,
t-statistics, P-value, FDR adjusted P-value, profiles of the
miRNA were displayed so that the degree of deregulated
information can be evaluated. In the ‘Validation’ section,
the expression information for this miRNA retrieved from
low-throughput experiments were presented if it is available.
dbDEMC 2.0 adds a new function of meta-profiling that
allow users to draw differential expression profile for user
defined miRNAs among a specific set of cancer types. Users
can input a list of miRNAs, pick one of the six types of
experimental designs and select the cancer types of interested
(Figure 3A). The meta-profiling tool will return a heatmap
depicting the expression change for queried miRNAs across
multiple cancer types (Figure 3B). The up- and
downregulated expression status is represented by red and green
colors according to a confidence score’, that is calculated as
the number of studies on certain types of cancer and
experimental design supporting the differential expression status.
This meta-profiling tool helps users to make a quick view of
differential expression events of miRNAs from user defined
COMPARISON TO RELATED DATABASES
Here we compared the content of dbDEMC 2.0 with
cancer related databases including miR2Disease, TUMIR,
HMDD, which are now available to download. We only
selected the cancer associated miRNAs, then we unified the
miRNA and cancer names for each database respectively.
The Venn diagram for the cancer related miRNAs indicated
that a great portion of the differentially expressed
miRNAs in the dbDEMC 2.0 are newly identified, with ∼27%
of miRNAs shared with those of other external databases
(Figure 3C). Whereas for the cancer types, 29 cancer types
overlap with external databases, and seven cancers are not
included by other databases (Figure 3D). This indicates
dbDEMC 2.0 will be an important complement to other
MiRNAs are widely involved in regulation of crucial
signaling pathways by controlling the expression of
important oncogenes in normal cells. Many studies have shown
that aberrant expression of miRNAs plays a critical role
in human cancers (30). The decreasing cost of the
highthroughput methods has led to large amount of miRNA
transcriptome data from cancer-related studies. This allows
researchers to perform miRNA quantification analysis in
cancer samples and identify cancer associated miRNAs.
Here, we provide the dbDEMC 2.0 to utilize these resources
and to provide a tool to facilitate the study of miRNA
expression levels in cancer.
dbDEPC 2.0 now has documented much more datasets
and differentially expressed miRNAs than the first version
(Supplementary Figure S2). In addition to a greater
number of miRNA–cancer associations included, dbDEMC 2.0
also has several advanced features that distinguish it from
other sources. For instance, by studying the samples profiled
in each of the collected data sets, we defined seven classes of
differential expression analyses relevant to the processes of
neoplastic transformation and progression. These included
cancer versus respective normal tissue, high grade versus
low grade samples, metastasis versus primary cancer,
subtype1 versus subtype2, poor outcome versus good outcome,
blood samples from cancer patient versus blood samples
from normal person, and also drug-treatment samples
versus non-treatment samples. This approach provides a
better way in application yet robust to the heterogeneous data
formats and experimental designs for miRNA expression.
For instance, circulating miRNAs have been one of the hot
topics in cancer research, it has been suggested as an
important class of potentially promising biomarkers in a variety
of different cancers (31,32). Users can query the database by
different cancer types and then filter the associated
experimental design easily to check the differentially expressed
By analysing the data from database, we could find
important miRNAs that may drive cancer development.
Previous studies have shown many miRNAs present consistent
differential expression pattern across cancer types. These
miRNAs can cooperatively regulate oncogenic pathways
and contribute to cancer hallmarks (33,34). We would like
to re-assess this using the result in the database. Here,
we only focus on the data from TCGA as it used
highquality miRNA-Seq method and avoid the heterogeneity
from different miRNA expression platforms. We selected
twelve cancer types with sufficient samples profiled,
miRNAs present consistent up- or down-regulation across all
or most cancer types are of particular interest, since these
may represent candidate list of oncogenes or tumor
suppressors. We identified a list of 42 miRNAs which were
consistently deregulated across at least nine of the twelve cancer
types, with 25 of these upregulated, and with the remaining
17 exhibiting downregulation (Supplementary Figure S3).
Among the deregulated miRNAs, many have been
previously demonstrated to be associated with cancers. For
instance, miR-23b could function as a tumor suppressor that
present downregulation in stomach cancer (35) and bladder
cancer (36). Whereas the miR-130 family have been
identified as upregulated in many cancer types that
representing putative oncogene (37,38). This pan-cancer wide
metaprofiling analysis indicated that the comprehensive
investigation of the database may help to illuminate the
complicated relationship between miRNAs and cancers and
develop more effective treatment strategies for cancers.
In summary, dbDEMC 2.0 provides a comprehensive
collection of cancer related miRNAs based on analysis of
large scale expression profiling data. As the cancer related
miRNA expression profiling data accumulate rapidly, this
database will be updated periodically to incorporate new
miRNA expression data. We hope to make dbDEMC 2.0
a useful resource that facilitate to cancer research, and
contribute to the biomarker discovery or even cancer treatment
related to miRNA.
Supplementary Data are available at NAR Online.
The authors wish to thank the Chinese Academy of
ences, Shanghai Institute for Biological Sciences and the Max-Planck Society for financial support. We also thank
TCGA Research Network: http://cancergenome.nih.gov/
for the availability of the data.
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