An Integrated Analysis of Aberrantly Expressed miRNA and mRNA Profiles Unveils a Robust Regulatory Network in HepG2 Cell

doi:10.4236/eng.2013.510B011

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Engineering, 2013, 5, 53-56

http://dx.doi.org/10.4236/eng.2013.510B011 Published Online October 2013 (http://www.scirp.org/journal/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

Email: kyny2011@hotmail.com, zhanghui31[email protected], [email protected],

[email protected], #[email protected]

Received October 2012

ABSTRACT

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

Education

Institutions (PAPD).

Corresponding author.

S. YANG ET AL.

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

(a)

(b)

(c)

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

study.

S. YANG ET AL.

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

mRNAs.

No. Pathway Count P-value

1 MAPK signaling pathway 59 2.15E−36

2 Regulation of actin cytoskeleton 54 7.13E−37

3 Focal adhesion 47 1.27E−30

4 Wnt signaling pathway 45 1.13E−34

5 Axon guidance 38 2.94E−29

6 Focal adhesion 42 4.35E−30

7 Regulation of actin cytoskeleton 31 1.02E−17

8 MAPK signaling pathway 30 4.98E−14

9 Insulin signaling pathway 26 3.49E−18

10 Axon guidance 24 1.11E−16

11 MAPK signaling pathway 27 3.63E−18

12 Focal adhesion 22 7.35E−16

13 Regulation of actin cytoskeleton 20 3.04E−13

14 Wnt signaling pathway 18 5.71E−14

15 Chronic myeloid leukemia 17 3.37E−18

*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 sequences” and 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-

S. YANG ET AL.

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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