Engineering, 2013, 5, 53-56 Published Online October 2013 (
Copyright © 2013 SciRes. ENG
An Integrated Analysis of Aberrantly Expressed miRNA
and mRNA Profiles Unveils a Robus t Regu latory
Network in HepG2 Cell*
Sheng Yang, Hui Zhang, Li Guo, Yang Zhao, Feng Chen#
Department of Epidemiology and Biostatistics, Nanjing Medical University (NJMU), Nanjing, China
Received October 2012
As crucial negative regu latory small non-coding molecules, microRNAs (miRNAs), have multiple biological roles. The
abnormal expression of specific miRNAs may contribute to the occurrence and development of tumor. Here, based on
HepG2 and L02 cells, we attempted to demonstrate the potential regulatory network of aberrantly expressed miRN A
profiles, interaction between miRNA and mRNA, and potential functional correlation between different miRNAs. De-
regulated miRNA and mRNA expression profiles were completely surveyed and identified by applying deep sequenc-
ing and microarray techniques, respectively. The genome-wide and integrative analysis of miRNA-mRNA was per-
formed based on their functional relationship according to experimentally validated and predicted targets. Nearly 50%
targets were negatively regulated by at least 2 aberrantly expressed miRNAs. Similar results were obtained based on
experimentally validated and predicted targets. Compared with abnormal miRNAs , their targets showed various expres-
sion patterns: stably expressed, down-regulated or up-regulated. Although the theoretical potential miRNA-mRNA in-
teraction could be predicted, they showed consistent or inconsistent expression patterns. Both functional enrichment
analysis of target mRNAs of dysregulated miRNAs and abnormal mRNA profiles suggested that corresponding path-
ways were involved in tumorigenesis. Moreover, to obtain potential fun ction al relation ships between different miRNAs,
we also performed expression analysis of homologous miRNAs in gene families. Generally, they could co-regulate bi-
ological processes with similar roles. The integr ative analysis of miRNA-mRNA ind icated a complex and flexible reg-
ulatory network. The robust network mainly derived from multiple targets for a specific miRNA (and vice ver sa), each
mRNA and co-regulation roles of different miRNAs.
Keywords: miRNA (mi c roRNA ); mRNA; Intergrated Analysis; Hepatoma Carcinoma Cell
1. Introduction
MicroRNAs (miRNA) are not only the most conserved
but also special non-coding RNAs which guide RNA
silencing. These different characters are given rise to the
disparate structure and biogenesis [1]. Mature miRNA
with a length of approximately 22 nucleotides (nt) is a
single strand and processes from a stem-loop precursor
miRNA (pre-miRNA) molecule (60 - 120 nt), with the
assistance of DISER. Before the precursor forming, pri-
mary miRNA (pri-miRNA) is cleaved by a ribonuclease
(RNase), such as DROSHA. After processing the miR-
NA duplex, one strand, called mature or active miRNA,
is loaded into AGO protein to participate the post-tr an s-
criptional procedure. Then the single strand miRNA is
incorporated with RNA-inducing silencing complex
(RISC) that interacts with 3’ untranslated region (UTR)
of messenger RNAs (mRNAs) through base pairing to
facilitate mRNAs repression or degradation [2-4].
Many experimental and bioinformatics analysis evi-
dences indicate that one single miRNA can regulate or
depress a great amount of mRNAs, because miRNAs
match the mRNAs only by the seed sequence which is
comprised by the nucleotides from 2 to 7 or 8 [5]. The
process of RNA silencing or RNA interference is com-
pleted of miRNAs, whose abnormal expression may give
rise to many tumors, such as breast cancer, non-s ma l l -
cell lung cancer and bladder cancer [1,6-8]. Recently, the
personalized treatment of cancer patients has been de-
veloping with the deep study of molecular characteriza-
*The work was supported by the project of 810723
89 from National
Natural Science Foundation of China and a Project Funded by the
Priority Academic Program Development of Jiangsu Higher
Institutions (PAPD).
Corresponding author.
Copyright © 2013 SciRes. ENG
tion of primary tumors [9]. Therefore, the integrated
analysis of mRNA-miRNA is one of the focuses of the
cancer studies.
In this study, we performed an integrative analysis of
miRNA-mRNA based on aberrantly expressed miRNA
and mRNA profiles in tumor cells by high-throughput
sequencing and microarray techniques, respectively. Ac-
cording to their functional relationships and expression
patterns, potential miRNA-mRNA interaction and regu-
latory network were comprehensively analyzed.
2. Material and Methods
HepG2 an d L02 cells were obtained from American Type
Tissue Collection and further sequenced. According to
miRNA and mRNA expression profiles, differentially
expressed miRNA and mRNA profiles were calculated
between the two samples through fold change filtering.
To acquire these mRNA/miRNA species, fold change
values were assessed from normalized datasets which
were the data from L02 cells. In this study, the cut-off
values were 2 and 0.5, which is utilized to identify
whether the up-regulated and down-regulated mRNA/
miRNA species. The study was based on the fold change
mRNA/miRNA to detect the aberrant expr ession between
two kinds of cell.
The relationship between targets of miRNA from the
miRTarBase database, the TargetScan software and the
experimental results of this study were studied. Firstly,
we utilized the miRNAs which had fold change in this
work to predict the regulated mRNAs through the miR-
Tar Ba se database which includes the validated miRNA
targets. And through the TargetScan software, the targets
of the same abnormal expression miRNAs were pre-
dicted. Then the accumulating frequency diagram and
venn diagram directly presented the relationship of the
data from the three groups. Finally, we compared the
accuracy rate of the two methods.
Further, the functions of the predicted mRNAs were
analyzed. Firstly, those mRNAs that were regulated by
more than 2 differentially expressed miRNAs were se-
lected, if their frequencies were more than 3. Functional
enrichment analysis was used the CapitalBio MAS 3.0
software based on those aberran tly expressed mRNAs
and predicted target mRNAs of abnormal miRNAs.
Those abundantly expressed miRNA gene families
were selected from the abnormal miRNA expression pro-
files. miRNA members in the gene family have similar
sequences, and may co-regulate biological processes.
Therefore, we also analyzed their potential targets, espe-
cially some common targets. Simultaneously, further
expression analysis was performed based on aberrant ly
expressed miRNA and mRNAs profiles.
3. Results and Discussion
3.1. Similar Distribution Patterns across
Different Target mRNA Datasets
Herein, three methods were used to analyze targets of
aberrantly expressed miRNAs (experimentally validated
targets, pred icted targets, and obtained abnormal mRNAs
in the study). The accumulation frequency diagrams
suggested diversity of targets based on dysregulated
miRNAs. Some mRNAs were regulated by specific
miRNAs, but others might be regulated by 2 or more
differen t miRNAs (Figure 1). However, the frequ ency of
down-regulated miRNAs is higher than up-r egu lated
species. The venn diagram based on the miRTarBase
database detected that the intersection of targets between
up-regulated and down-regulated was 11 4 (32.02% from
up-regulated miRNAs, 17.54% from down-regulated
species). The results from TargetScan program indicated
that the intersection of targets between up-regulated and
down-regulated is 3143 (82.17% from up-regulated
miRNAs, 54.44% from down-regulated species). Based
Figure 1. The dist ribution s of the number of targets an d the
venn diagram of the aberrantly expressed miRNA profile.
(a) is based on the data from the miRTarBase database; (b)
is based on the data from the TargetScan program; (c) is
based on the abnormal mRNA expression profile from the
Copyright © 2013 SciRes. ENG
on special mRNA expression profiles in tumor cells, the
study demonstrated that the intersection of up-regulated
and down-regulated is 325 (8.66% from up-regulated
mRNA, 6.09% from down-regulated mRNA).
These findings testified that each miRNA can regulate
a series of targets; simultaneously each mRNA can be
negatively regulated by several miRNAs (F ig ure 1). The
reason was mainly derived from the flexible interaction
between miRNA and mRNA through seed sequences
and UTR. Moreover, we compared the accuracy rates of
the two methods in predicting the target gene through
chi-square test. No significant difference was detected
between the two methods in predicting the targets, in -
cluding targets of up-regulated and down-regulated
miRNAs. Specially, in predicting the down-regulated
mRNAs, the accuracy rates of the two methods are 29.84%
and 28.0 3% (χ2 = 0.95, P = 0.33), wh ile in predicting the
up-regulated targets, the accuracy rates of the two me-
thods are 21.91% and 21.05% (χ2 = 0.14, P = 0.70).
3.2. Functional Enrichment Analysis Reveals
Potential Contributions in Tumorigenesis
More bioinformatics information about the different ex-
pression mRNAs and targets of abnormal miRNAs can
be a cqu ir ed from MAS 3.0, such as the pathways, the
regulation netw orks and proteins. Herein, in order to fur-
ther study their potential roles in occu rr en ce and devel-
opment of tumor, we also chose the pathway information
from the KEGG (Kyoto Encyclopedia of Genes and Ge-
nomes) database [10]. One pathway was involved in a
great amount of mRNAs. Simultaneously a specific
mRNA also participated many pathways (Table 1). Ac-
cording to targets of der eg ul ated miRNAs, and aberrantly
expressed mRNA profiles in the study, important and
essential biological processes could be enriched, includ-
ing some human diseases. The pathways included MAPK
signaling pathway, Wnt signaling pathway, Chronic
myeloid leukemia, and etc. (Table 1). Further, we de-
tected that the intersection of these three situations is
MAPK signaling pathway, regulation of actin cytoskele-
ton and focal adhesion. Therefore, we conjectured that
these pathways might trigger development or generation
of each tumor or special liver cancer. The functions of
key regulatory proteins of the actin cytoskeleton is regu-
lating cancer cell migration and invasion though forma-
tion of invasive protrusions used by tumor cells, such as
lamellipodia and invadopodia [11]. Wnt signaling path-
way makes the hepatocelluar carcinoma dysregulated by
two distinctive classes (CTNNB1 and Wnt -TGFβ) [12].
3.3. The Co-Rgulation of Homologous miRNAs
in Gene Family
We further selected a pair of miRNA family from the
Table 1. Pathways regulated by the different expression
No. Pathway Count P-value
1 MAPK signaling pathway 59 2.15E36
2 Regulation of actin cytoskeleton 54 7.13E37
3 Focal adhesion 47 1.27E30
4 Wnt signaling pathway 45 1.13E34
5 Axon guidance 38 2.94E29
6 Focal adhesion 42 4.35E30
7 Regulation of actin cytoskeleton 31 1.02E17
8 MAPK signaling pathway 30 4.98E14
9 Insulin signaling pathway 26 3.49E18
10 Axon guidance 24 1.11E16
11 MAPK signaling pathway 27 3.63E18
12 Focal adhesion 22 7.35E16
13 Regulation of actin cytoskeleton 20 3.04E13
14 Wnt signaling pathway 18 5.71E14
15 Chronic myeloid leukemia 17 3.37E18
*The 1 - 5 pathways of target mRNAs from up-regulated miRNAs; The 6 -
10 pathways of target mRNAs from down-regulated miRNAs; The 11 - 15
pathways are predicted by the intersection of them.
differentially expressed miRNA profiles. From the up-
regulation miRNAs, we chose the mir-8 gene family,
including hsa-miR-200b-3p and hsa-miR-200a-3p (fold
change value: 377.30 and 130.43) which had the maxi-
mum fold change value in from the mir-8 gene family
(mi R-200a, miR-200b, mi R-200c, mi R-141 and miR-
429). Secondly, we consulted the miRBase database to
find the target of the two miRNAs. Obviously, they pos-
sessed some common target genes, including BAP1, SIP1,
WASF3, ZEB1, ZEB2 and ZFPM2, but they also regu-
lated the different genes, respectively. Thirdly, we found
the down-regulated mRNAs from the experimental data
to check the predicted target genes. Finally, we utilized
exact probability method to compare the accuracy rates
between the co-regulated mRNAs and the other mRNAs.
The intersection of the two miRNAs was 6 (46.15% of
the mRNAs regulated by hsa-mir-200b-3p, 33.33% of the
mRNAs regulated by hsa-mir- 200 a-3p) (Figure 2). The
miRNA members in gene family shared the same or sim-
ilar “seed sequencesand always showed consistent ex-
pression patterns, and they co-regulated multiple and
essential biological processes. Therefore, we should pay
attention to the miRNA family when we study miRNAs
or the regulated mRNAs.
In conclusion, miRNA-mRNA interaction is much
more complex than we thought. A specific miRNA may
regulate multiple target mRNAs, and vice versa. The
miRNA members in miRNA gene family may regulate
the same targets and show cons istent expression pat-
Copyright © 2013 SciRes. ENG
Figure 2. The experimentally validated targte mRNAs of
hsa-miR-200a-3p and hsa-miR -200b-3p (members in mir-8
gene family). mRNAs or mi RNAs in red are up-regulated in
tumor cells; mRNAs in blue are down-regulated; and
mRNAs in black stably express between normal cells. Fur-
thermore, deregulated miRNAs or mRNAs are also high-
lighted fold change values.
terns in special time and space in vivo. The co-regulation
processes will contribute to the robust network with mul-
tiple different molecules. Systematic analysis across dif-
ferent regulatory molecules should be crucial to study
tumorigenesis at the multiple molecular levels, including
mRNAs, miRNAs, lncRNA s, and etc.
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