IN SILICO STUDY OF EXPRESSION PROFILES CORRELATION

BETWEEN MICRORNAS AND CANCEROUS GENES

Ka-Lok Ng

and Chia-Wei Weng

Department of Bioinformatics, Asia University, 500 Lioufeng Road, Wufeng Shiang, Taichung, 41354, Taiwan

Keywords: MicroRNA, Oncogene, Tumor suppressor gene, Gene expression profile, Correlation coefficient.

Abstract: We investigate the possibility that microRNA can act as an oncogene or tumor suppressor gene.

Experimentally verified microRNA target genes information (TarBase) are integrated with microRNA and

mRNA expression data (NCI-60) to study this hypothesis, in which the Pearson correlation and Spearman

rank coefficients are used to quantify these relations for nine cancer types. Correlation coefficients with

negative values are used to filter out microRNA targets. Biological annotations of the targets are supplied by

using the TAG, GO and KEGG records. The above information are utilized to provide a platform in

identifying potential cancer related microRNAs. A web based interface is set up for information query and

data display.

1 INTRODUCTION

MicroRNAs (miRNAs) are a class of small non-

coding RNAs that bind to its target mRNA sequence

in the 3′-untranslated region (3′UTR), and induce

either translation repression or mRNA degradation.

Recent studies indicated that microRNA could

possibly play an important role in human cancer

where microRNA targets oncogene (OCG) or tumor

suppressor gene (TSG) to regulate the gene

expression (Zhang et al., 2007, He and Cao, 2007,

Wu and Hu, 2006, Garzon et al., 2006). When

microRNA plays an oncogenic role, it targets TSG

and leads to tumor formation. On the other hand, if

microRNA plays the tumor suppressor role, it would

target OCG and suppress tumor formation.

This work utilized the following databases; the

TarBase (Sethupathy et al., 2005), miRBase

(Griffiths-Jones et al., 2006) and NCI-60

(Shankavaram et al., 2007, Blower et al., 2007),

Online Mendelian Inheritance in Man (OMIM),

Tumor Associate Gene (TAG) (Chan, 2006) Gene

Ontology (Gene Ontology Consortiium, 2006), and

Kyoto Encyclopedia of Genes and Genomes (KEGG)

(Kanehisa et al., 2008) databases to set up a platform

for predicting human microRNA targeting cancerous

genes information. Table 1 states the general

information provided by the databases used in the

current study.

The platform mainly provides the following two

functionalities; (i) human microRNA target

information, and (ii) nine cancer types’ Pearson

correlation and Spearman rank coefficients of

microRNA and its target expression level for three

Affymetrix chips.

Table 1: General information provided by the databases

used in the current study.

Database General information provided

TarBase experimentally tested miRNA target genes

miRBase

information for precursor microRNAs,

mature microRNAs, FASTA sequences,

and their target genes.

NCI-60

human cancer cell lines mRNA and

miRNA expression data

OMIM human diseases genetic data

TAG OCG, TCG and cancer related genes

Three types of gene annotations; i.e.

molecular function, biological

rocess and

subcellular localization

KEGG Metabolic pathways, disease pathways

141

Ng K. and Weng C. (2010).

IN SILICO STUDY OF EXPRESSION PROFILES CORRELATION BETWEEN MICRORNAS AND CANCEROUS GENES.

In Proceedings of the First International Conference on Bioinformatics, pages 141-146

DOI: 10.5220/0002728701410146

 SciTePress

2 MATERIALS AND METHODS

It is known that microRNA binds with mRNA and

can induce mRNA cleavage or inhibit translation. In

order to investigate the regulatory role of microRNA

in cancer diseases, we study the expression profiles

correlation between microRNA and its target genes,

in particular the OCG and TSG targets.

Figure 1 depicted the process flowchart for

identifying potential cancer related microRNAs. The

microRNA-target pairs information is obtained from

TarBase, whereas the expression profiles for

microRNAs and mRNAs are retrieved from the

NCI-60 dataset. Then, the expression profiles

correlation between microRNA and its target genes

are quantified by computing the correlation

coefficients. If the microRNA and its target gene are

direct interact, the correlation coefficient results

should reveal significant negative values.

MicroRNA target pairs with the correlation

coefficients below a given threshold are filtered for

further investigation. These pairs suggest a

regulatory relationship between the microRNAs and

their targets. The TAG dataset is used in order to sort

out the microRNA-OCG and microRNA-TSG pairs.

These pairs are further annotated by using the

OMIM, GO and KEGG biomedical terms. With

multiple biological annotations, i.e. disease type,

relevant biological function and pathway, for the

negative correlated pairs, this platform should

provide helpful guidance for investigating the role of

micorRNAs in tumor formation.

Figure 1: Process flowchart to identify potential cancer

related microRNAs based on quantifying the expression

profile correlation between microRNA and its target genes.

2.1 TarBase and miRBase Datasets

TarBase is a manually curated collection of

experimentally tested microRNA target genes. Each

experimentally validated target site is extracted from

the literatures. TarBase includes several species,

such as human, mouse, fruit fly, and worm,

microRNA target gene records.

There are many tools available for microRNA

target genes prediction, such as miRanda (Enright et

al., 2003), RNAhybrid (Kruger and Rehmsmeier,

2006), and TargetScans

(http://genes.mit.edu/tscan/targetscanS2005.html). A

major problem of microRNA target genes prediction

is that the prediction accuracy remains rather high,

there were reports indicated that the false positive

rate could be as high as 50% for human (John et al.,

2004), 24-39% and 22-31% when using miRanda

(Bentwich, 2005), and TargetScan (Bentwich, 2005)

respectively.

The primary goal of this work is to develop a

bioinformatics tool to investigate the possibility that

microRNA can act as an OCG or TSG. The main

advantage of using TarBase in constructing the

microRNA targeting information is that all the target

genes recorded by TarBase are experiment verified,

and TarBase provides their PubMed ID. From a

biologist point of view, experimental verified targets

imply higher confidence. If the miRNA:mRNA

targeting part is uncertain, then any further results

derived are doubtful. The TarBase version 5 dataset

from DIANA Lab. website is employed in the

present study. The miRBase database collects

information for precursor microRNAs, mature

microRNAs, FASTA sequences, and their target

genes. Currently the latest version of the miRBase

sequence database is 13.0, which includes

microRNA information across 103 species. In

version 13, a total of 706 Homo Sapiens mature

micorRNA entries are recorded.

2.2 Expression Datasets

In this study, we made use of the NCI-60 cancer cell

line mutation data to investigate the possibility that

microRNA can act as an OCG or TSG. This can be

achieved by calculating the correlation coefficient

between the expression levels of microRNAs and

their experimentally validated target genes.

The NCI-60 is a set of 60 human cancer cell lines

derived from diverse tissues. These cell lines include

nine tissues’ microRNA and mRNA expression

information, that is, breast cancer, central neural

system (CNS) cancer, colon cancer, leukemia,

melanoma, non-small cell lung cancer, ovarian

TarBase

NCI-60

Expression profile PCC, SRC calculation

PCC or SRC less

than -0.5

Integration of

OMIM, TAG,

KEGG, GO

Inferring potential cancer-related microRNAs

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cancer, prostate cancer, and renal cancer,.

Four publicly available datasets of gene expression

profiles are selected in this study; including the

microRNA expression, and the Affymetrix U95(A-

E), U133A and U133B mRNA expression datasets.

Affymetrix mRNA expression datasets use three

types of normalization methods, that is, GCRMA,

MAS5 and RMA. Therefore, a total of ten datasets

are used, including one microRNA dataset and nine

Affymetrix RNA expression datasets. The NCI-60

website provides a tool, called CellMiner

(Shankavaram et al, 2009), to query those chip

datasets.

2.3 Tumor Associated Gene Database

The Tumor Associated Gene (TAG) database

presents information about cancer related genes. In

TAG, cancer related genes are classified into OCGs,

TSGs and tumor-associated genes. All genes in the

TAG are retrieved through text-mining approach

from the PubMed database. Currently, TAG

documented 519 genes, including 198 OCGs, 170

TSGs, and 151 genes related to oncogenesis. In

addition, more cancer related microRNA gene

information are obtained by using Pipeline Pilot

which is a commercial bioinformatics text mining

package to do keywords search against PubMed. At

present, a total of 111 microRNAs are retrieved that

are related to certain types of cancers. These

microRNAs and their target records are stored in our

platform for further analysis.

2.4 OMIM, GO and KEGG Databases

Online Mendelian Inheritance in Man (OMIM) is a

compendium of human genes and genetic

phenotypes. It contains information on all known

mendelian disorders.

The GO database includes three structured

controlled vocabularies (ontology) that describe

gene products in terms of their associated biological

processes, cellular components and molecular

functions in a species-independent manner.

KEGG is short for Kyoto Encyclopedia of Genes

and Genomes which is a collection of manually

drawn pathway maps. In our study, we focused on

cancer related pathways information.

Investigation of cancer related pathway in

KEGG can help us determine the biological

functions of the target genes. We investigate which

cancer related pathways consist of the microRNA

target genes. By going through biological function

keywords search, a list of microRNA target genes

which participated in certain KEGG pathways are

obtained. Table 2 shows the cancer related pathways

which are processed in this study.

Table 2: Cancer related pathways which are processed in

this study.

KEGG ID Types of Cancer

hsa05210 Colorectal cancer

hsa05221 Acute myeloid leukemia

hsa05220 Chronic myeloid leukemia

hsa05218 Melanoma

hsa05211 Renal cell carcinoma

hsa05215 Prostate cancer

hsa05223 Non-small cell lung cancer

2.5 Preprocessing

The TarBase and NCI-60 datasets used different ID

formats for microRNAs, therefore, both sets of ID

are standardized using the miRBase IDs. We also

converted the mRNA gene IDs in TarBase and NCI-

60 Affymetrix RNA expression datasets IDs to

NCBI official symbols by using Gene Name Service

(Lin et al., 2007). Gene Name Service website

provides query services for 26 types of gene

identifiers of Homo sapiens genes. We obtained

44855, 20169 and 16441 entries for the U95(A-E),

U133A and U133B mRNA datasets after pre-

processing.

3 METHODS

3.1 Expression Profiles Correlation

For a given cancer tissue type, we calculated both

the Pearson correlation coefficient (PCC) and

Spearman rank coefficient (SRC),

ρ,

between the

expression level of a microRNA and it's target genes.

PCC or SRC is given by

∑∑

∑

−−

yyxx

)()(

))((

(1)

where x

and y

denote the expression intensity of

microRNA and the microRNA's target gene

respectively;

and

denote the mean expression

intensity of microRNA and the target gene

respectively; and n is the total number of the

IN SILICO STUDY OF EXPRESSION PROFILES CORRELATION BETWEEN MICRORNAS AND CANCEROUS

GENES

143

expression data entries. In case of SRC, the

expression intensity values of x

, y

and

are

replaced by their ranks.

The PCC for each microRNA and Affymetrix

RNA expression profile are computed for nine tissue

types. PCC takes a value between -1 and +1. One of

the troubles with quantifying the strength of

correlation by PCC is that it is susceptible to being

skewed by outliers. Outliers that is a single data

point can result in two genes appearing to be

correlated, even when all the other data points not.

SRC is a non-parametric statistical method that is

robust to outliers. It can ignore the magnitude of the

changes. The idea of SRC is to transform the

original values into ranks, and then to compute the

correlation between the series of ranks.

Gene expression values of microRNA and

mRNA in the same tissue type are ordered in

ascending order, the lowest value is assigned to rank

one. In case of ties mid-rank is assigned, as for

example, when both values are ranked five, a rank of

5.5 is assigned. After ranking the expression profiles

of microRNA and mRNA for a particular tissue,

SRC can be calculated by Eq. (1), with the ranks and

the average value of ranks of microRNA and mRNA

are used instead of the expression intensity and

average expression intensity. The SRC also takes a

value between -1 and +1.

4 RESULT

Both the PCC and SRC of microRNA expression

levels and their targeting mRNA expression levels

for nine types of cancer tissues are computed.

For example, microRNA hsa-miR-16 targets the

breast cancer gene, BCL2, have PCC less than -0.7

for the three Affymetrix datasets with three different

normalization methods for each chip. The results are

reported in Table 3. We can understand negative

PCC (SRC) based on the following reasoning. It is

known that microRNA is able to repress and/or

cleavage mRNA by incomplete or complete

complementary binding with the mRNA. If a

microRNA and its target gene is directly interacting,

the result of PCC (SRC) of their expression profiles

should reveal negative correlation. Table 3 list the

U95 results which suggested that microRNA, hsa-

miR-16, can possibly play a role in regulating the

cancer gene BCL2.

Downstream cancer targets are easily obtained by

cross-referencing the target gene results with the

TAG dataset. The results of cancerous genes found

in both TarBase and TAG are listed in Table 4.

Table 3: PCC of the expression profile for hsa-miR-16 and

BCL2 in the breast tissue.

Dataset Normalization PCC

U95(A-E) GCRMA -0.918

MAS5 -0.949

RMA -0.718

Table 4: MicroRNA target genes which are corresponded

with TAG entries.

4.1 Evaluation of Results by OMIM

Disease disorder keywords provided by the OMIM,

is compared with the negative (under or equal to -0.5)

PCC results. It is found that 82 entries involved in

disease disorder. Among these 82 entries, 19 are

cancer genes. In these cancer genes, only 17 entries

belong to the nine cancer types. Table 5 presents

five of the 17 OMIM entries.

Table 5: The 17 OMIM entries with negative PCC ( -

0.5) and their corresponding cancerous types.

OMIM ID Gene Symbol Cancerous Types

605882 BACH1 Breast

151430 BCL21 Leukemia

168461 CCND1 Colon

123829 CDK41 Melanoma

600160 CDKN2A CNS||Melanoma

For a given Affymetrix chip, it is found that the

PCC score is independent of the normalization

methods. Similarly, for a given normalization

method, the PCC score is also rather independent of

the chip type. Furthermore, it is also found that

target results (microRNA:mRNA) obtained by PCC

versus PCC and SRC are rather consistent. Table 4

reports the results for the U95(A-E) chip with the

GCRMA normalization method.

According to Table 6, four pairs of negative

microRNA:mRNA are found belonging to leukemia,

and one pair belongs to lung cancer. Among these

five pairs, only hsa-mir-370 targets an OCG, i.e.

MAP3K8. Besides the OMIM recorded data in

Table 4, we also identified five (in Italic) microRNA

Cancer Gene Gene Symbols

OCG

AXL, BCL2, CCND1, CDK4, CTGF,

ESR1, FGF20, KIT, MAP3K8, MYB,

MYBL1, MYCN, RYK, TEAD1

TSG

CAV1, CDKN1A, CDKN1C, CDKN2A,

HTATIP2, MXI1, NF2, PTEN, PTPN12,

RB1, SERPINB5, TGFBR2

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Table 6: OMIM evaluation results of both PCC and SRC under or equal to -0.5 for GCRMA-U95(A-E) dataset.

Bold font denotes the OMIM matching data.

Italic and bold font denotes that half of the microRNA probes’

PCC and SRC are ≦-0.5. Inside the box, the numerator of the fraction denotes the number of times where both of PCC and

SRC are less than -0.5.

The denominator represents the total number of PCC values calculated (i.e. the number of microRNA probes times the

number of mRNA probes). Inside the square bracket, zero ([0]) implies no matching with the OMIM data and [*] denotes

matching with the OMIM data.

targets in which half of the probes’ PCC and SRC

are under or equal to -0.5 in prostate cancer. Among

these pairs, one of the target is a TSG, i.e.

CDKN2A, and two targets are OCGs, i.e. CCND1

and MAP3K8.

4.2 Evaluation of Results by KEGG

The KEGG pathways are compared with the PCC

results. A total of 26 pairs (with an asterisk) of

negative correlated microRNA and its target genes

matched with KEGG data for four cancer types,

which are colon, leukemia, melanoma and prostate.

Among these KEGG matched pairs, twelve pairs

(with an asterisk) have both the PCC and SRC under

or equal to -0.5 in more than half of the probes. In

these twelve pairs, two target genes are TSG, i.e.

CDKN1A and PTEN, and one gene is an OCG, i.e.

CCND1.

In OMIM and KEGG evaluation results, two

pairs of microRNA:mRNA, i.e. hsa-mir-24-

1:CDKN2A and hsa-mir-24-2:CDKN2A, both

present significant negative correlation in prostate

cancer, with the correlation coefficients under or

equal to -0.5 in more than half of the probes. At

present, it is still unclear whether the hsa-mir-24-2

and CDKN2A has any regulatory relationship in

prostate cancer. These predicted negative correlated

microRNA:mRNA pairs maybe subjected to further

investigation in order to identify the exact regulatory

situations in prostate cancer.

According to OMIM (Table 6) and KEGG

evaluation results, one pair presents negative

correlation in leukemia which is hsa-mir-

34a:CCND1, but the PCC and SRC scores don’t

present in more than half of the probes are under or

equal to -0.5. Although the evidence is not strong,

but it suggests that hsa-mir-34a may also be a

potential regulator of CCND1 in leukemia.

Final, we set up a web based service to provide

the computed results. The web site is availabled at

http://ppi.bioinfo.asia.edu.tw/mirna_target/index.html.

5 CONCLUSIONS

Recent studies indicate that microRNA could

possibly play an important role in human cancer,

where microRNA targets TSG or doesn’t target

OCG. Experimentally verified microRNA targeted

genes information (TarBase) are integrated with

microRNA and mRNA expression data (NCI-60) to

study this hypothesis, in which two correlation

coefficients, PCC and SRC, are used to quantify the

correlation between microRNA and its targets

expression profiles. The predicted results are

evaluated with reference to the OMIM and KEGG

data. It is found that the obtained results are rather

Precursor Target Gene TAG Breast CNS Colon Lung Leukemia Melanoma Ovarian Prostate Renal

hsa-let-7b CCND1 OCG 0/3[0] 0/3[0] 0/3[0] 0/3[0] 0/3[0] 0/3[0] 0/3[0] 0/3[0] 1/3[0]

hsa-mir-155 BACH1 - 0/1[0] 0/1[0] 0/1[0] 0/1[0] 0/1[0] 0/1[0] 0/1[0] 0/1[0] 0/1[0]

hsa-mir-23b NOTCH1 - 1/4[0] 0/4[0] 0/4[0] 0/4[0] 1/4[*] 0/4[0] 0/4[0] 1/4[0] 0/4[0]

hsa-mir-24-1 NOTCH1 - 2/8[0] 0/8[0] 1/8[0] 0/8[0] 2/8[*] 0/8[0] 1/8[0] 2/8[0] 0/8[0]

hsa-mir-24-2 NOTCH1 - 1/4[0] 0/4[0] 0/4[0] 0/4[0] 1/4[*] 0/4[0] 0/4[0] 1/4[0] 0/4[0]

hsa-mir-24-1 CDKN2A TSG 0/8[0] 0/8[0] 2/8[0] 0/8[0] 0/8[0] 0/8[0] 2/8[0] 6/8[0] 1/8[0]

hsa-mir-24-2 CDKN2A TSG 0/4[0] 0/4[0] 0/4[0] 0/4[0] 0/4[0] 0/4[0] 1/4[0] 3/4[0] 0/4[0]

hsa-mir-27b NOTCH1 - 1/4[0] 0/4[0] 0/4[0] 0/4[0] 1/4[*] 0/4[0] 1/4[0] 3/4[0] 0/4[0]

hsa-mir-34a CCND1 OCG 0/6[0] 0/6[0] 0/6[0] 0/6[0] 1/6[0] 0/6[0] 0/6[0] 3/6[0] 0/6[0]

hsa-mir-370 MAP3K8 OCG 0/14[0] 1/14[0] 0/14[0] 1/14[*] 0/14[0] 0/14[0] 2/14[0] 8/14[0] 0/14[0]

IN SILICO STUDY OF EXPRESSION PROFILES CORRELATION BETWEEN MICRORNAS AND CANCEROUS

GENES

145

independent of the chip types and the normalization

methods too.

In the OMIM evaluation with both PCC and SRC

less than or equals to -0.5, five pairs of negative

correlated microRNA and its target genes matched

with OMIM records, in which four of them belong

to leukemia and the rest one is lung cancer. In these

five pairs, only one of them is an OCG, i.e.

MAP3K8. Besides, we also got five pairs of

significantly negative correlated microRNA and its

target in prostate cancer in which both of PCC and

SRC are under or equals to -0.5. Among these five

pairs, only one gene is a TSG, i.e. CDKN2A, and

only two genes are OCGs, i.e. CCND1 and

MAP3K8. These five pairs can be browsed in Table

5 in which they are denoted with italic and bold font.

Similar conclusions are obtained for the KEGG

evaluation.

Given that more than half of the probes’

correlation coefficients are negative correlated, we

identified certain putative pairs of microRNA and its

cancer related targets in different cancer types, such

as, hsa-mir-24-1:CDKN2A and hsa-mir-24-

2:CDKN2A in prostate cancer and hsa-mir-

19a:PTEN in both leukemia and prostate cancer.

It is suggested that those negative correlated pairs of

microRNA and target can be subjected to further

investigation, such as performing in vivo

experiments to valid the hypothesis that microRNA

could possibly play an important role in human

cancer.

ACKNOWLEDGEMENTS

K-L Ng work is supported by the National Science

Council of R.O.C. under the grant of NSC 98-2221-

E-468-013.

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