dbDEPC 2.0: updated database of differentially expressed proteins in human cancers
dbDEPC 2.0: updated database of differentially expressed proteins in human cancers
Ying He 1 2
Menghuan Zhang 0 1
Yuanhu Ju 1
Zhonghao Yu 4
Daqing Lv 1
Han Sun 2
Weilan Yuan 3
Fei He 1
Jianshe Zhang 1
Hong Li 1 2
Jing Li 0
Rui Wang-Sattler 4
Yixue Li 1 2
Guoqing Zhang 1
Lu Xie 1
0 Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University , Shanghai 200240 , P. R. China
1 Shanghai Center for Bioinformation Technology , Shanghai 200235
2 Key Laboratory of Systems Biology, Chinese Academy of Sciences , Shanghai 200031
3 Biomedical Engineering for School of Life Sciences and Technology, Tongji University , Shanghai 200092, P. R. of China
4 Research Unit of Molecular Epidemiology, Helmholtz Zentrum M u ̈nchen , Neuherberg 85764 , Germany
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
A large amount of differentially expressed proteins
(DEPs) have been identified in various cancer
proteomics experiments, curation and annotation of
these proteins are important in deciphering their
roles in oncogenesis and tumor progression, and
may further help to discover potential protein
biomarkers for clinical applications. In 2009, we
published the first database of DEPs in human cancers
(dbDEPCs). In this updated version of 2011, dbDEPC
2.0 has more than doubly expanded to over 4000
protein entries, curated from 331 experiments
across 20 types of human cancers. This resource
allows researchers to search whether their
interested proteins have been reported changing in
certain cancers, to compare their own proteomic
discovery with previous studies, to picture
selected protein expression heatmap across
multiple cancers and to relate protein expression
changes with aberrance in other genetic level. New
important developments include addition of
experiment design information, advanced filter tools for
customer-specified analysis and a network
analysis tool. We expect dbDEPC 2.0 to be a much
more powerful tool than it was in its first release and
can serve as reference to both proteomics and
cancer researchers. dbDEPC 2.0 is available at
Cancer is becoming one of the leading causes of death
worldwide (1). In the past decade, advancements in
high-throughput technologies, such as genome
sequencing, expression microarrays and mass spectrometry (MS)
have greatly improved our ability to understand the
landscape of cancers (2) and helped to detect biomarkers in
cancer diagnosis, prognosis and therapy which lead to
better patient care (3,4).
Several distinguished cancer-related recourses such as
the Cancer Genome Atlas (TCGA) (5), Cancer Genome
Anatomy Project (CGAP) (6) and Oncomine (7) have
been developed to explore human cancer genes at the
level of the genome and mRNA. Many studies (8,9) have
proved the efficiency and importance of investigating
changes in cancer protein level using the technology of
MS. A great amount of cancer proteomics data have
been accumulating and require management of easy
access and systematic analysis. However, protein landscape
is more heterogeneous and complex than DNA and RNA,
proteomics technology is still in pursuit of accuracy,
coverage and repeatability (10). Few databases provide
information on cancer proteome, let alone quantitative
MS-based proteomic data. In 2001, a database in lung
cancer (11) collected both genomic and proteomic data.
Last year came out the Genome Medicine Database of
Japan Proteomics (GeMDBJ) (12) collecting cancer
proteome expression data produced by two-dimensional
polyacrylamide gel electrophoresis (2D PAGE). Human
Protein Atlas (13) database concentrates on protein
expression images in normal and cancerous tissues and
cells generated by antibody-based immunofluorescence.
In 2009, we reported the first dbDEPC (14) that aimed
to provide an overview of protein-level expression changes
mainly detected by MS technology. This resource allows
researchers to search their interested proteins and cancers;
the retrieved protein entry provides the differential
expression pattern seen in cancers, along with detailed
annotations from various MS experiments. It also associated the
differential proteins with genomic aberrance, such as
single-nucleotide polymorphisms (SNPs), which might
provide possible explanations from the genetic point of
view. However, dbDEPC could have been more useful
had there been more related data. With fast increment
of cancer quantitative proteomic data in recent years, we
have updated this database. In the new version of
dbDEPC 2.0, the DEPs deposited have been doubled to
over 4000, the cancer types increased to 20, and for the
first time, 18 subtypes of some cancers are included.
Altogether, the curated experiments have grown from 65
to 331. Other than largely expanded data volume, the
search and browser functions are retained and enhanced,
and quite a few new features have been added. Detailed
experiment information is recorded, which allows for
experiment to experiment comparison and filtering of results
according to user-specific interests. This will give a more
specific protein expression heatmap with a clearer
biological indication, avoiding ambiguous comparison
across heterogeneous experiments. Proteins usually do
not function as independent events; rather they interact
with each other to fulfill biological process. In dbDEPC
2.0, all DEPs can be viewed as networks in certain cancer
types, showing the differential protein regulation network.
Queried proteins can also be linked to DEP networks to
see their possible involvement in cancer. We expect that
both proteomics and cancer researchers can be benefited
from the more resourceful updated version of dbDEPC.
dbDEPC 2.0 is freely available to public domain at http://
DATA COLLECTION AND DATABASE
Data collection goes through the following process:
(1) An automatically text mining was conducted on
PubMed abstracts using names of cancer types
(Table 1) in MeSH, MS-related words (MS,
quantitative proteomic) and keywords describing expression
changes (upregulated, downregulated and fold
change). The current version of dbDEPC indexed
papers published before March 2011.
(2) To control the data quality, each deposited data set
went through the same rigorous manual review
process as described in the previous version (14).
(3) Different identifiers (IDs) or names of DEPs were
extracted from papers and uniformly mapped to
UniprotKB (15) accession (3rd May 2011).
(4) To give additional annotation of each DEP, new
external resources were integrated including the
HUGO Gene Nomenclature Committee (HGNC)
Brain tumora Head and neck cancera
Renal cell carcinomaa
Gall bladder cancera
Non-small cell lung carcinoma
Small cell lung carcinoma
Hepatitis C virus
Hepatitis B virus
Breast ductal carcinoma
Pancreatic ductal adenocarcinoma
Chronic myeloid leukemia
Chronic lymphocytic leukemia
Acute myeloid leukemia
Acute lymphoblastic leukemia
Papillary thyroid carcinoma
Follicular thyroid carcinoma
Follicular thyroid adenoma
Oral premalignant lesions
aMarked the new human cancer types and subtypes in dbDEPC 2.0.
(16), Gene Ontology (17), Kyoto Encyclopedia of
Genes and Genomes (KEGG) (18), STRING (19)
and CanProVar (20).
In dbDEPC 2.0, data are managed by MySQL5 (http://
www.mysql.com/) and a dynamic web interface is
constructed with J2EE technology (http://www.oracle.com/
technetwork/java/javaee) with a Tomcat server (http://
tomcat.apache.org/). The tool modules including
heatmap profile, network tool and venn-diagram-based
experiment comparison tool are developed with R
In the current release, dbDEPC contains information on
4092DEPs in 20 different human cancer types and 18
subtypes (Table 1). The number of DEPs in breast
cancer, hepatocellular carcinoma (HCC) and testicular
cancer augmented dramatically during the past 2 years
(Figure 1A). However, in all of the DEPs identified by
the proteomics approach, only 5% were validated by the
low-throughput assays such as western blot,
immunohistochemistry, etc (Figure 1B). dbDEPC now has
documented 331 MS experiments from 241 peer-reviewed
publications. Data sets of proteomics studies on cervical
cancer, gastric cancer, HCC and breast cancer have sharp
increments by 14, 9.67, 8.5 and 5.69 times, respectively
Gall Bladder Cancer 1
Head and Neck Cancer
Hepatocellular Carcinoma 4 6 Leukemia
Lung Adenocarcinoma Lymphoma Ovarian Cancer
Renal Cell Carcinoma Sarcoma Skin cancer
Testicular Cancer 1
up regulation (2.0)
up regulation (1.0)
down regulation (2.0)
down regulation (1.0)
not validated 95%
Dot Blot validation
Northern blotting analysis
Real−time quantitative PCR
Western blotting analysis
Normal vs. Cancer
13% cell line
8% cell line
Better experimental descriptions
We have refined the experimental descriptions following
the framework of PRIDE database (21), a public
repository standard for proteomics research. The experiment
page now delineates publication reference, experimental
design, sample information, protocol for protein
identification and quantification procedure, the total number of
identified proteins (peptides and spectra) and the
definition of threshold for differential expression. According
to the experimental designs, all datasets could be
categorized into four types of studies, i.e. normal versus
cancer comparison, cancer versus cancer comparison,
cancer metastasis studies and treatment research. They
account for 56%, 9%, 11% and 24% of total experiments,
respectively (Figure 1D).
New search and filter tools
In addition to query by proteins or cancers in the last
version, the search tool now allows users to browse all
the experiments. Regardless of query from proteins,
cancers or experiments, dbDEPC 2.0 first provides the
related experiments briefly, showing the experiment ID,
cancer name, experimental design, control sample, case
sample and the total number of upregulated and
downregulated DEPs in the experiment (Figure 2A), which give
users a first sight of their interested experiments.
We now provide various optional filters in search
function to help users focus on their most interested
results. The filter tools cover experimental design, cancer
name, sample type (tissue or cell type), sample size (<10,
10–50, 50–100 and >100), organism (Homo sapiens, Mus
musculus and Rattus norvegicus), MS technology (LC-MS/
MS, MALDI–TOF MS, etc.), proteomics search engine
(Mascot, Sequest, etc.) and protein quantification
approach (two-dimensional gel electrophoresis (2DE),
2D Fluorescence Difference Gel Electrophoresis
(2DDIGE), Isotope-labeling and label-free). After filtering
the experiment results, users can select them and click
the button ‘View Proteins’ to navigate the DEPs list that
gives the UniprotKB accession of each DEP and a quick
overview of its differential expression ratio in each
experiment (Figure 2B). The protein list is visualized by three
tags. Under the ‘All’ tag it shows all the results; the
‘Validated’ tag displays only the DEPs validated by
low-throughput assays and the ‘Not validated’ list the
complementary candidates, which offer possible chance
for further verification.
New outlook of heatmap profiles
dbDEPC profile page creating a heatmap facilitates users
to investigate differential expression profiles among
different cancers. In the last version, we found 63 proteins
showing conflicting expression changes (up or down) in
six studies on HCC, which might be due to different
samples or heterogeneous experimental designs. To
avoid the imbalance among different types of experiments,
in this version we allow users to extract data sets from
identical experimental design to create the heatmaps.
Users input a list of proteins, select cancers of interest as
before, and pick one type of experimental design (Figure
3A). The page will return a heatmap visualizing the
expression change of query proteins across multiple cancers
and a table listing the DEPs briefly (Figure 3B). Up and
downregulations are represented by red and green color,
respectively, for normal versus cancer comparison and the
color grades correspond to the confidence scores. For each
DEP, the score is calculated as the number of
homogeneous experiments on certain cancer supporting its up or
DEPs association network
Another new feature we would like to highlight is the
DEPs association network, a tool in attempt to find the
association of query proteins with reported DEPs in
particular cancers. The association network background
came from the STRING 9.0 (19). Users input query
proteins, select one experimental design, one or several
interested cancers and set an association confidence
score threshold. The database will present a network of
the first-layer-associated DEPs with the query proteins,
and the associated protein list in particular cancers
(Figure 3C). We also provide a downloadable zip file
containing two files. One describes the links between the
DEPs and query proteins, and the other represents the
expression patterns (upregulated or downregulated) of
the DEPs in the network. These two files can be
imported by Cytoscape (http://www.cytoscape.org/) that
allows users to modify the networks by themselves. This
tool helps users find the associated candidate markers
reported in other studies, which provide further
information to the queried proteins and give possible hint for their
next step research.
This network tool also demonstrates a cancer-specific
protein differential regulation network, and helps detect
important hub proteins from a systematic view. We
calculated the degree of each node (namely, the number
of connections or edges the node has to other nodes) in
our database. As the basic topological network
measurement, degrees of nodes provide insights into the important
architecture of the nodes of interest in the whole network
(22). Figure 3D displays degree distribution and the
average degree of the nodes in each protein set. The
average degree of 2317 DEPs was 22.48 and was
significantly higher than that of random networks of
counterpart size (ranged from 8.82 to 12.49, Wilcoxon’s test,
P = 1.9 10 18). This revealed that cancer proteins
show an increase in the number of proteins they interact
with, and also appear to participate in central hubs (23).
Through sorting the degree of each DEP in the association
network, we listed the top 10 proteins with highest degree
in Figure 3D and found that most of them are cancer
related. MYC, coding for a transcription factor,
connected with 106 DEPs, is believed to regulate expression
of 15% of all genes (24). As one of the most important
oncogene, MYC is reported to drive cell proliferation,
regulate cell growth and also play a great role in
differentiation and stem cell self-renewal (25,26). Another hub
protein epidermal growth factor receptor (EGFR)
involved in cell proliferation and cancer progressing has
been also known to associate with a number of cancers
(27). Through the network tool, we provide a global view
of cancer proteins and reveal their roles as central hubs to
connect and regulate other proteins in cancer cells.
Enhanced protein page
In this version, the protein page of each DEP has been
thoroughly revised. The enhanced page guided by a
navigation bar exhibits in seven parts: Protein Summary,
Cancer Profile, MS Experiment, Validation Assays,
Sequence Variation, Association DEPs and Function
Annotation (Supplementary Figure S1). In the summary
section, IPI IDs were replaced by UniprotKB (15)
accessions due to the closure of IPI database (28). Besides
protein ID and name, function and subcellular
information are included to delineate the summary information
for each DEP. The ‘Cancer Profile’ demonstrates four
heatmaps of the differential expression profiles of the
protein across cancers corresponding to the four types of
experimental designs. The experiment details could be
found in the ‘MS Experiment’ part. ’Validation Assays’
provide confirmation by low scale assays such as
western blot, immunohistochemistry, etc. Protein sequence
variations from CanProVar (20) are highlighted with
yellow or green color indicating cancer related or just
from dbSNP, respectively, which may provide users
possible explanations to the protein differential expression
in cancers. ‘Association DEPs’ section facilitates users to
find interested associated DEPs for further information.
The last part ‘Function Annotation’ displays the
biological descriptions of the protein-involved KEGG pathways
(18) and related Gene Ontology functions (17).
OTHER NEW FEATURES
In this version of dbDEPC, we also provide a new
Venn-diagram-based experiment comparison tool in the
search result page through the ‘Intersection’ button,
which provides a straightforward comparison between
upregulated or downregulated DEPs lists from two or
In addition to license free download for all academic
users, we now open a user upload system to invite other
researchers to share their findings. The upload files or
publications will be manually reviewed, extracted and
deposited into database if the data meet our quality
Proteomic technology nowadays allows researchers to
view protein change quantitatively in cancer patients
versus their healthy counterparts, thus lead to deeper
understanding behind the protein function. With the
considerable increasing of cancer proteomic data, dbDEPC is
expected to provide a resource to facilitate cancer research
at protein marker level.
Here is a possible scenario of how dbDEPC could
benefit cancer studies. Suppose a researcher would like
to find the DEPs in metastatic breast cancer; as he/she
queries by breast cancer and filters experimental design
by ‘Metastasis’, he/she can see 24 experiments at current
dbDEPC. Further, he/she wants the samples to be human
tissues so he/she filters the sample type by ‘tissue’ and
organism by ‘Homo sapiens’, eight experiments would
meet such criteria. Clicking experiment ID, more
information about experimental design and biological background
would show up. Now the eight experiments can be viewed
by categories. For example, focusing on breast cancer
concerning lymph node metastases, he/she selects three
experiments (EXP00095, EXP00213 and EXP00215). The
results can be displayed by two choices: ‘View Proteins’
or ‘Intersection’. (i) Click the ‘View Proteins’ button, all
DEPs identified in three experiments are provided as a
downloadable tab separate text file, in this case, 94
proteins altogether. Each is marked with upregulated or
downregulated in metastasis of that experiment. Clicking
‘Validated’ tag, the user can see that only eight proteins
(O00299, O75083, P07339, P13796 downregulated and
P00918, P01011, P09211, P50454 upregulated) were
validated by traditional biochemical assays. For each
DEP, the detailed annotation can be found on the protein
information page. (ii) Back to ‘Intersection’ button. It is
an experiment comparison tool; in this case, the three
experiments come up with two venn-diagrams showing the
intersection of upregulated and downregulated proteins,
respectively, in these experiments. Protein numbers that
were identified by multiple experiments can be seen such
as, one upregulated protein (P20774) identified by two
studies (EXP00095 and EXP00213), etc.
Moreover, even if the user-queried proteins are not
among the DEPs in dbDEPC, the network tool can help
link them to the associated DEPs in cancers, thus to
provide clues for their involvement in cancer networks.
The network tool also provides a systematic view of
differential protein interaction network in cancers. From the
above analysis on degree distribution of DEPs network,
we confirmed that proteins in cancer-specific network
appear to participate in central hubs (e.g. the most
dbDEPC is committed to be an potential reference
database forDEPs in human cancers. Such layered
curation and annotation of cancer-related proteins could
be useful in better understanding of both the value of
proteomics study in cancer research and the biological
meaning of protein expression change in certain cancer
stages. Metaanalyses are also possible by comparing the
experiments of the like, and thus to ensure possible
biomarker selection and predictive model construction.
Future update planned for dbDEPC will include
refinement of data sets from treatment design experiments.
Currently, the data sets from treatment experiments are
heterogeneous. Samples were compared under various
conditions such as no treatment versus drug treatment,
small molecule sensitive versus small molecule resistant
and so forth. We plan to redescribe the data sets and
provide a more delicate description for each treatment
experiment. Another feature planned is a profile similarity
algorithm to compare the query differential expression
profiles with the known DEPs profiles to find the similar
profile results. We will keep on documenting the
validation assays on the roles of particular proteins in certain
cancers from external low-throughput biochemical
experiments and functional analyses. More available
high-throughput protein expression data sets on same
cancer may cross validate each other. Currently,
dbDEPC mainly focuses on human cancers. Collecting
protein expression data of other model species like
mouse and rat may be under future construction.
dbDEPC has been continuing to grow and as always we
encourage users’ feedbacks including error reports and
feature requests, we hope to make dbDEPC a
comprehensive resource to facilitate cancer proteomic research and
may be in the end contribute to cancer treatment.
Supplementary Data are available at NAR
Supplementary Figure S1. Online:
The authors acknowledge the Shanghai Guidance of
Science and Technology for the offer of abstracts and
full texts of publications collected in dbDEPC. We also
acknowledge Drs Jia Jia, Yuchen Shen and Yangfan Guo
from Shanghai Center for Bioinformation Technology for
help with the revision of the database.
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