Gene Ontology Analysis of Colorectal Cancer Biomarkers Probed with

Affymetrix and Illumina Microarrays

Monika Simjanoska

, Ana Madevska Bogdanova

and Sasho Panov

Ss. Cyril and Methodius University, Faculty of Information Sciences and Computer Engineering, Skopje, Macedonia

Ss. Cyril and Methodius University, Faculty of Natural Sciences and Mathematics, Institute of Biology, Skopje, Macedonia

Keywords:

Colorectal Cancer, Affymetrix, Illumina, Machine Learning, Gene Ontology, Bayesian Classiﬁcation.

Abstract:

Colorectal cancer is the fourth most common cause of death worldwide. Recently, many microarray exper-

iments have been done to investigate the expression of the genes in the colorectal tissues and thus, to ﬁnd

the answers for its occurrence. Previously, we used experiments obtained from both Illumina and Affymetrix

microarray platforms to analyze the gene expression in healthy and carcinogenic tissues. As a result we got

speciﬁc sets of biomarkers that we used to build an accurate Bayesian diagnostic system. The high degree of

classiﬁer’s sensitivity and speciﬁcity intrigued us to proceed with the research of the signiﬁcant genes we dis-

covered, the biomarkers. Therefore, in this paper we aim towards biomarkers identiﬁcation and the functional

groups they are associated with, i.e., we performed gene ontology analysis. Investigating the genes that control

the colorectal carcinogenic tissue development is of central importance to the veriﬁcation of the biomarkers’

revealing method’s validity. Moreover, we showed the importance of their participation in the prior distribu-

tions modeling, which is the key part for achieving an accurate Bayesian classiﬁcation, regardless their strict

disease and disorder association.

1 INTRODUCTION

The cancer incidence, mortality and prevalence were

a target of the research which the World Health Orga-

nization (WHO) provided in 2008. The results of the

GLOBOCAN project showed that the colorectal can-

cer is the third most common cancer in men, and the

second in women with a total incidence of 1,234,000

cases, of which 60% occur in developed regions. The

mortality results of 8% of total cancer deaths make

this type of cancer to be the fourth most common

cause of death from cancer (GLOBOCAN, 2008).

Recently, the scientists provide intensive gene ex-

pression proﬁling experiments in order to compare the

malignant to the healthy cells in a particular tissue.

The advantage of the microarray technologies enables

simultaneous observation of thousands of genes and

allows the researchers to derive conclusions whether

the disorder is a result of the abnormal expression of

a subset of genes.

In our previous researches we assumed that the

colorectal cancer occurs as a result of increased and

decreases expression levels of a set of signiﬁcant

genes, which we refer to as biomarker genes. There-

fore, in (Simjanoska et al., 2013b) and (Simjanoska

et al., 2013a) we used experiments of colorectal car-

cinogenic and healthy adjacent tissues probed with

two widely used microarray technologies, Illumina

and Affymetrix. For each platform we developed

original methodologies for unveiling the genes that

show signiﬁcant changes in their expression levels

in presence of colorectal cancer, both adenomas and

adenocarcinomas, and we used the genes’ expres-

sions to model binary diagnostic system based on the

Bayes’ theorem. The outcomes of the classiﬁcation

showed a high precision when diagnosing both car-

cinogenic and healthy tissues.

The ability of the selected biomarkers to discrim-

inate between colorectal cancer and normal health

condition intrigued us to go deeper in the problem

and to investigate the biomarkers functions on molec-

ular level, i.e, to perform gene ontology analysis.

Analysing the molecular function and the biologi-

cal processes of the biomarkers will provide answers

whether all the signiﬁcant genes are tightly related to

the colorectal cancer phenomena, and whether all of

them are necessary for the developed classiﬁer to pro-

duce accurate decisions. Conducting this kind of re-

search is of great importance to the Bayesian machine

learning classiﬁcation approach, which we conﬁrmed

396

Simjanoska M., Madevska Bogdanova A. and Panov S..

Gene Ontology Analysis of Colorectal Cancer Biomarkers Probed with Affymetrix and Illumina Microarrays.

DOI: 10.5220/0004554803960406

In Proceedings of the 5th International Joint Conference on Computational Intelligence (NCTA-2013), pages 396-406

ISBN: 978-989-8565-77-8

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

to be very accurate, and also very important to ver-

ify if the methods used for biomarkers selection are

reliable. Furthermore, the results obtained from a re-

search like this can advance the progress of the future

personalized cancer treatment (Jain, 2004).

The rest of the paper is organized as follows. In

Section 2 we brieﬂy present the work related to our

ﬁeld of interest. The methodology for biomarkers re-

vealing, tissues classiﬁcation and ontology analysis is

explained in Section 3. In Section 4 we present the ex-

periments and the results from the ontology analysis

and the additional classiﬁcation experiments. In the

ﬁnal Section 5 we derive conclusions from the results

and we present our plans for future work.

2 RELATED WORK

In this section we give a brief review of the recent sci-

entiﬁc work that relates to ontology analysis and its

importance to various health disorders and diseases.

Furthermore, we present some of the most important

researches related to the colorectal cancer. Eventu-

ally, we exhibit the literature related to the microarray

experiments we used in our paper.

In (Ahn et al., 2003) the authors present their re-

search where the gene ontology analysis was used

to systematically characterize the global expression

proﬁles at cellular process levels. They showed that

potentially signiﬁcant pathogenetic cellular processes

can be identiﬁed and showed that the functional pro-

ﬁling has a signiﬁcant impact on the discovery of

pathogenic pathway in leiomyoma.

Another research is presented in (Holmans et al.,

2009) where gene ontology analysis has been used to

provide insights into the biology of bipolar disorder.

Avoiding single marker analysis, the authors in

(Jia et al., 2010) also incorporated Gene Set Enrich-

ment Analysis (GSEA) and hypergeometric test, and

combined them using Fisher’s combined method to

perform pathway-based analysis in order to detect

genes’ combined effects on mediating schizophrenia.

Interestingly, they found a few pathways to be top

ranked and likely associated with schizophrenia, how-

ever non of the genes involved in these pathways had

been detected by single marker analysis, conclud-

ing that this approach may complement the original

analysis of genome-wide association studies (GWAS)

dataset.

The approach of gene set enrichment analysis for

interpreting gene expression data was also discussed

in (Subramanian et al., 2005) where the researchers

demonstrate how it yields insights into several cancer-

related data sets, including leukemia and lung cancer.

They also state that single-gene analysis may miss im-

portant effects on pathways. Sometimes an increase

of 20% in all genes encoding members of a metabolic

pathway may be more important than a 20-fold in-

crease in a single gene. Also another statement that

is important to our research is that often the different

studies of the same biological system present a list of

statistically signiﬁcant genes that show distressingly

little overlap.

Considering the colorectal cancer, we cannot dis-

miss the Vogelstein’s genetic model for colorectal car-

cinoma which has been proposed as a result of a long

term research. In (Kinzler and Vogelstein, 1996) they

present the genetic changes associated with colorec-

tal tumorigenesis and distinguish several genes that

showed high involvement in colorectal neoplasia. In

this paper we will rely on this model, which is a mile-

stone in cancer research (Nature, 2006), to compare

our results and to verify our methods for biomark-

ers selection. However, blindly relying only on Vo-

gelstein’s model and not assuming any exceptions

is completely wrong. This is conﬁrmed by the re-

search in (Smith et al., 2002) where the authors inves-

tigate the mutations in the speciﬁc genes introduced

by Vogelstein, including adenomatous polyposis coli

(APC), Kirsten-ras (K-ras), and p53. According to

the results from their research, they come up with the

conclusion that multiple alternative genetic pathways

to colorectal cancer exist and that the widely accepted

genetic model of cancer development is not represen-

tative of the majority of colorectal tumors.

Regarding ontology and classiﬁcation analysis re-

lated to colorectal cancer, authors in (Lascorz et al.,

2011a) sum up the biomarkers results from 23 dif-

ferent researches. Even though most of them show

diversity in the signiﬁcant genes revealed, the au-

thors in their research take into account the unique

biomarkers, which are nearly 1000, and perform on-

tology analysis using various tools. They mainly hold

on to the ontology results analysis of the enriched set

of genes, rather than verifying the biomarkers with

classiﬁcation methods so that we can compare our re-

sults. Similarly, in (Xu et al., 2013) the researchers

use Affymetrix microarray data from 20 patients and

a different procedure from the one we presented in 3.1

to reveal signiﬁcant gene expression, which resulted

in 1469 biomarkers. From the ontology analysis they

ranked top 10 most important pathways. Compar-

ing our results to theirs, we realized that there is no

overlap between ours and their biomarkers sets. Even

though they lack a classiﬁcation analysis, we may in-

clude their biomarkers in our future work and test the

ability of the Bayesian approach to make an appro-

priate modelling using different biomarkers reveal-

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397

ing procedure. Since the non overlapping between

the biomarkers sets discovered in different scientiﬁc

papers is very common, a new meta-analysis model

of colorectal cancer gene expression proﬁling studies

is proposed in (Chan et al., 2008). As the authors

ranked the biomarker genes according to various pa-

rameters, the gene CDH3 which we found to play

role in the colorectal cancer, is also found by their

meta-analysis model. Another interesting approach

maintained with classiﬁcation analysis is presented

in (Jiang et al., 2008), where the authors constructed

disease-speciﬁc gene networks and used them to iden-

tify signiﬁcantly expressed genes. A particular atten-

tion is given to ﬁve biomarkers, from which one of

them, IL8, was also detected by our methodology,

but it was not considered important in our research

since no speciﬁc connection to the colorectal cancer

was found in the literature. In order to test the power

of the colon cancer-speciﬁc gene network biomark-

ers revealing ability, they use ﬁve different classiﬁers:

diagonal linear discriminate analysis (DLDA), 3 near-

est neighbours (3NN), nearest centroid (NC), support

vector machine (SVM) and Bayesian compound co-

variate (BCC).

Considering the fact that in this paper we use mi-

croarray experiments from Affymetrix and Illumina

platforms performed for different purposes, we pro-

vide an overview of the work related to these sets of

data.

The experiments obtained from the Affymetrix

platform were used in several researches. In (Hong

et al., 2010) the authors aimed to ﬁnd a metastasis-

prone signature for early stage mismatch-repair pro-

ﬁcient sporadic colorectal cancer (CRC) patients

for better prognosis and informed use of adjuvant

chemotherapy. A transcriptome proﬁle of human

colorectal adenomas is given in (Sabates-Bellver

et al., 2007) where they characterize the molecular

processes underlying the transformation of normal

colonic epithelium. One of the data sets has been

used in (Watanabe et al., 2006) to clarify the differ-

ence between microsatellite instability (MSI) and mi-

crosatellite stability (MSS) cancers and, furthermore,

to determine distinct characteristics of proximal and

distal MSI cancers. A similar research is presented

in (Jorissen et al., 2008) where the scientists showed

cross-study consistency of MSI-associated gene ex-

pression changes in colorectal cancers. The microar-

ray data obtained from the Illumina chip was used

in (Hinoue et al., 2012) where the authors performed

comprehensive genome-scale DNA methylation pro-

ﬁling of normal and carcinogenic tissues and identi-

ﬁed four DNA methylation-based subgroups of CRC

using model-based cluster analyses.

3 METHODS AND

METHODOLOGY

In this section we describe the procedure used for sig-

niﬁcant genes detection from both widely used types

of DNA microarrays, Affymetrix and Illumina. Fur-

thermore, we exhibit the methodology used for build-

ing the Bayesian classiﬁcator, and ﬁnally, we present

the gene ontology method that we use in this paper to

reveal overrepresented functional groups of genes.

3.1 Biomarkers Detection Methodology

The process for revealing the biomarkers consists of

the following steps (Simjanoska et al., 2013b), (Sim-

janoska et al., 2013a):

• Quantile normalization. Since our aim is to unveil

the difference in gene expression levels between

the carcinogenic and healthy tissues, we proposed

the Quantile normalization (QN) as a suitable nor-

malization method (Wu and Aryee, 2010).

• Low entropy ﬁlter. We used low entropy ﬁlter to

remove the genes with almost ordered expression

levels (Needham et al., 2009), since they lead to

wrong conclusions about the genes behaviour.

• Paired-sample t-test. Knowing the facts that both

carcinogenic and healthy tissues are taken from

the same patients, and that the whole-genome

gene expression follows normal distribution (Hui

et al., 2010), we used a paired-sample t-test.

• FDR method. False Discovery Rate (FDR) is a

reduction method that usually follows the t-test.

FDR solves the problem of false positives, i.e.,

the genes which are considered statistically sig-

niﬁcant when in reality there is not any difference

in their expression levels.

• Volcano plot. Both the t-test and the FDR method

identify different expressions in accordance with

statistical signiﬁcance values, and do not consider

biological signiﬁcance. In order to display both

statistically and biologically signiﬁcant genes we

used volcano plot visual tool.

3.2 Bayesian Classiﬁcation

As we discovered the two sets of biomarkers from

both microarray chips, we used them in our pre-

vious work (Simjanoska et al., 2013b) and (Sim-

janoska et al., 2013a) to propose a generative ap-

proach for building a Bayesian classiﬁer that models

the prior distributions at carcinogenic and healthy tis-

sues. Once we modelled the prior distributions for

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both classes, carcinogenic and healthy, we were able

to use them in the Bayes’ theorem and to calculate the

a posteriori probability for a given tissue to belong to

one of the two classes, C

Therefore, we calculate the a posteriori probabil-

ity P(C

|~x), as:

p(C

|~x) =

p(~x|C

) ∗ P(C

)

∑

i=1

p(~x|C

) ∗ P(C

)

(1)

The class-conditional densities, or, the prior distri-

butions, p(~x|C

), are calculated as the product of the

continuous probability distributions of each gene dis-

tinctively:

p(~x|C

) =

∏

... f

(2)

For the prior probabilities P(C

), we deﬁned two

test cases:

• Test Case 1: Since we have equal number of tis-

sues into both of the classes, the prior probabilities

are also equal P(C

) = P(C

) = 0.5;

• Test Case 2: The prior probabilities are esti-

mated according to the statistics in (GLOBO-

CAN, 2008). Therefore, P(C

) = 0.0002 and

P(C

) = 0.9998, where C

denotes the carcino-

genic class, and C

denotes the healthy class.

The tissue ~x, which is an input to the Bayesian

classiﬁer, is classiﬁed according to the rule of maxi-

mizing the a posteriori probability (MAP):

= max p(C

|~x) (3)

3.3 Gene Ontology

The analyses of single markers have been in the fo-

cus of the genome-wide association studies. How-

ever, it often lacks the power to uncover the relatively

small effect sizes conferred by most genetic variants.

Therefore, using prior biological knowledge on gene

function, pathway-based approaches have been devel-

oped with the aim to examine whether a group of re-

lated genes in the same functional pathway are jointly

associated with a trait of interest (Wang et al., 2010).

The goal of the Gene Ontology Consortium is to

produce a dynamic, controlled vocabulary that can

be applied to all eukaryotes even as knowledge of

gene and protein roles in cells is accumulating and

changing (Ashburner et al., 2000). The Gene On-

tology (GO) project since 1998 is a collaborative ef-

fort to provide consistent descriptors for gene prod-

ucts in different databases and to standardize classi-

ﬁcations for sequences and sequence features. The

GO project provides ontologies to describe attributes

of gene products in three non-overlapping domains of

molecular biology (Harris et al., 2004):

• Molecular Function describes activities, such as

catalytic or binding activities, at the molecular

level. GO molecular function terms represent ac-

tivities rather than the entities that perform the ac-

tions, and do not specify where, when or in what

context the action takes place.

• Biological Process describes biological goals ac-

complished by one or more ordered assemblies of

molecular functions.

• Cellular Component describes locations, at the

levels of subcellular structures and macromolec-

ular complexes.

There are many tools based on Gene Ontology re-

source, however, many of them require local instal-

lation and speciﬁc platform. Therefore, in this re-

search we use the freely accessible Gene Ontology

Enrichment Analysis Software Toolkit, GOEAST. It

is a web based tool which applies appropriate statis-

tical methods to identify signiﬁcantly enriched GO

terms among a given list of genes. Beside the other

functions, GOEAST supports analysis of probe set

IDs from Affymetrix and Illumina microarrays. It

provides graphical outputs of enriched GO terms to

demonstrate their relationships in the three ontology

categories. In order to compare GO enrichment sta-

tus of multiple experiments, GOEAST supports cross

comparisons to identify the correlations and differ-

ences among them (Zheng and Wang, 2008).

4 EXPERIMENTS AND RESULTS

In this section we present the experiments and the re-

sults obtained from the previously deﬁned methodolo-

gies.

4.1 Microarray Data Analysis

In order to extract signiﬁcant genes that character-

ize the colorectal cancer, we used two sets of mi-

croarray data. The ﬁrst was gene expression proﬁl-

ing of 32 colorectal tumors, adenomas, and matched

adjacent 32 non-tumor colorectal tissues probed with

Affymetrix Human Genome U133 Plus 2.0 Array.

It contains 54,675 probes, but the unique genes ob-

served are 21,050. The second is gene expres-

sion analysis of 26 colorectal tumors, adenocarcino-

mas, and matched adjacent non-tumor colorectal tis-

sues, probed with Illumina Human Ref-8 v3.0 whole-

genome expression BeadChip. It allows 24,526 tran-

script probes, but unique genes are 17,853.

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399

Figure 1: Molecular function.

Both sets of data were preprocessed according to

the methodology described in Section 3.1. The re-

sults showed 138 signiﬁcant probes from the tissues

probed with Affymetrix microarray, and 213 signiﬁ-

cant probes from the tissues probed with Illumina mi-

croarray.

The signiﬁcant probes were used in the Bayes’

theorem as discussed in Section 3.2. Table 1 presents

the results from the tissues classiﬁcation. Sensitiv-

ity refers to the classiﬁer’s ability to correctly classify

carcinogenic tissues, whereas speciﬁcity refers to the

classiﬁer’s ability to correctly classify healthy sam-

ples. Additionally, we used 239 patients already diag-

nosed with colorectal cancer, and 12 healthy patients,

in order to present the classiﬁer’s reliability. We need

the results from the Table 1 for comparing the out-

comes from the experiments that follow.

4.2 Gene Ontology Analysis

Once we revealed biomarker genes that showed ex-

cellent ability in distinguishing between the colorec-

tal cancer and the healthy samples, we continued our

research in analysing biomarkers functions on molec-

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Table 1: Classiﬁer’s sensitivity and speciﬁcity.

Chip Performance Sensitivity Speciﬁcity Test Cases

Affymetrix

Tissues

1 0.84 Test case 1

0.94 1 Test case 2

Patients

0.98 0.92 Test case 1

0.90 1 Test case 2

Illumina Tissues

0.96 0.92 Test case 1

0.81 1 Test case 2

ular level. For this purpose, we used the online avail-

able tool GOEAST, previously discussed in Section

3.3. For obtaining reliable results, we chose the

Fisher’s exact test and a p-value of 0.01. In order to

compare both enrichment results, we used the Multi-

GOEAST tool and produced the ontologies depicted

in ﬁgures 1, 2 and 3.

The different colour saturation degrees in the

graphs present the enrichment signiﬁcance of each

GO term, deﬁned by the p-value. In the graphical

output of Multi-GOEAST results, each set is repre-

sented with different colour. Therefore, red and green

boxes represent enriched GO terms only found in

Affymetrix and Illumina biomarkers, whereas yellow

boxes represent commonly enriched GO terms in both

experiments.

Figure 1 depicts the molecular function of the dif-

ferentially expressed genes. The results show that

’hormone activity’ is a common molecular function

for a subset of the biomarker genes from both mi-

croarray platforms. Inspecting the biological pro-

cesses described in Figure 2, we conclude that there

are no processes in common, whereas considering the

cellular component analysis in Figure 3 we perceive

that some genes from both platforms are found in the

extracellular region.

As we examined the genes from the ontology anal-

ysis, we derived conclusion that there is a small over-

lap between the enriched sets from the two platforms.

However, as explained in Section 2 this is not an un-

expected phenomena.

In order to compare the results, we analyzed an-

other study where the researchers also examined path-

ways in colorectal cancer development (Lascorz et al.,

2011b). They used 242 genes and total of nine tools

to detect enrichment of Gene Ontology (GO) cate-

gories or Kyoto Encyclopedia of Genes and Genomes

(KEGG) pathways. Among identiﬁed the consistently

enriched gene categories, we realized that our exper-

iment and their research have the following enriched

molecular functions and cellular components in com-

mon: receptor binding, cytokine activity, chemokine

activity, hormone activity, oxidoreductase activity,

acting on CH-OH group of donors, and oxidoreduc-

tase activity, acting on the CH-OH group of donors,

NAD or NADP as acceptor.

Assuming the enriched entities we found are sta-

tistically overrepresented among all biomarkers, we

performed classiﬁcation experiments to realize if the

Bayesian classiﬁcator we developed is able to dis-

criminate between the carcinogenic and the healthy

tissues using only the overrepresented genes as a

training set. However, the results presented in Table 2

show that the Bayesian classiﬁer’s ability to recognize

colorectal cancer has decreased.

Table 2: Classiﬁer’s sensitivity and speciﬁcity for the en-

riched sets of genes.

Chip Performance Sensitivity Speciﬁcity Test Cases

Affymetrix

Tissues

0.91 0.69 Test case 1

0.47 1 Test case 2

Patients

0.96 1 Test case 1

0.78 1 Test case 2

Illumina Tissues

0.96 0.88 Test case 1

0.58 1 Test case 2

Considering the results in tables 1 and 2, we con-

ﬁrmed that the statistical pattern recognition process,

i.e. the Bayesian model requires larger amount of

data, therefore, it works well only if both enriched

and residual genes are taken into account.

Furthermore, the next step is to verify the reliabil-

ity of the procedure for distinguishing the biomarkers

considering their relation to the colorectal cancer.

Using publicly available microarray data proﬁled

on Affymetrix U133A chips, the authors in (Benita

et al., 2010) examined gene enrichment proﬁles from

a tissue perspective rather than gene perspective,

thereby identifying highly enriched genes within a

cell type, which are often key to cellular differen-

tiation and function. To identify genes that are tis-

sue speciﬁc, the authors used an enrichment score to

benchmark expression levels in one tissue compared

to all other tissues. When applying their online avail-

able tool Gene Enrichment Proﬁler on the genes that

the ontology tool found to be overrepresented, we no-

ticed that at Affymetrix platform a group of genes

are enriched in the Central Nervous System’s tissues,

whereas the others are enriched in various other tis-

sues. For the overrepresented genes among the Illu-

mina biomarkers, we noticed that the enrichment is

also not concentrated in any particular tissue.

This intrigued as to go deeper and investigate ev-

ery gene involved in the molecular functions and the

biological processes from the ontology analysis. Few

of them are conﬁrmed to play role in colorectal cancer

beside the other related diseases and functions:

• VIP - Vasoactive intestinal peptide (VIP) is found

in hormone activity and receptor binding molecu-

lar functions. Its expression is down regulated at

both chips, Affymetrix and Illumina. In (Zhang

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401

Figure 2: Biological processes.

et al., 1997), the researchers evaluate the expres-

sion of VIP receptor in colonic carcinoma and in-

vestigate the its role in colon cancer growth.

• SCG2 - This gene is related to cytokine activity

and receptor binding molecular functions. It is

found to be signiﬁcant from the Affymetrix plat-

form and showed decreased expression. The pro-

tein encoded by this gene is a member of the chro-

mogranin/secretogranin family of neuroendocrine

secretory proteins. Chromogranin genes have

been explored in (Pagani et al., 1995) and (Fer-

rero et al., 1995).

• CHGA - The protein encoded by this gene is also a

member of the chromogranin/secretogranin fam-

ily of neuroendocrine secretory proteins and is

also down expressed at the Affymetrix chip.

• GUCA2B - The expression of this gene is down

regulated at Affymetrix chip. It encodes a mem-

ber of the guanylin family, and is expressed in

the stomach and intestine. It may be involved

in salt and water secretion into the intestinal lu-

men as well as the renal tubules, and thus regulate

electrolyte homeostasis in these tissues. Its role

in the colorectal cancer is discussed in (Li et al.,

2009) where the colorectal cancer is observed as

a disease of hormone insufﬁciency. Guanylin cor-

relation is examined in (Camici, 2008) and also

new diagnostic and therapeutic approaches for

colorectal cancer are discussed.

• MMP7 - Proteins of the matrix metalloproteinase

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402

Figure 3: Cellular component.

(MMP) family are involved in the breakdown of

extracellular matrix in normal physiological pro-

cesses, such as embryonic development, repro-

duction, and tissue remodeling, as well as in dis-

ease processes, such as arthritis and metastasis.

This gene showed up expression at the Affymetrix

chip. Its association with tumor cell invasion and

metastasis is exhibited in (Masaki et al., 2001).

Authors in (Mori et al., 1995) ﬁnd that MMP-7

mRNA is overexpressed in human colorectal car-

cinomas and that MMP-7 may prove useful as a

marker of biologic aggressiveness.

• MMP3 - MMPs play a central role in cell pro-

liferation, migration, differentiation, angiogene-

sis, apoptosis and host defences. Dysregulatoin of

MMPs has been implicated in many diseases in-

cluding arthritis, chronic ulcers, encephalomyeli-

tis and cancer. One of the ﬁrst steps in metasta-

sis is the degradation of the basement membrane,

a process in which MMPs have been implicated.

Synthetic or natural inhibitors of MMPs result in

inhibition of metastasis, while up-regulation of

MMPs led to enhanced cancer cell invasion. This

gene has showed up regulation at the Affymetrix

chip. Its importance to the colorectal cancer is

proved in (Zinzindohou

e et al., 2005), (Baba et al.,

2004), and (Roeb et al., 2004).

• CDH3 - This gene is a classical cadherin from

the cadherin superfamily. The encoded protein is

a calcium-dependent cell-cell adhesion glycopro-

tein comprised of ﬁve extracellular cadherin re-

peats, a transmembrane region and a highly con-

served cytoplasmic tail. CDH3 is overexpressed

in the majority of pancreatic cancer and various

other malignancies, including gastric and colorec-

tal cancers (Imai et al., 2008).

• DHRS9 - This gene is found to be down regulated

at the Illumina chip. It is involved in alcohol dehy-

drogenase (NAD) activity, oxidoreductase activ-

ity, acting on CH-OH group of donors, oxidore-

ductase activity, acting on the CH-OH group of

donors, NAD or NADP as acceptor, steroid dehy-

drogenase activity, acting on the CH-OH group of

donors, NAD or NADP as acceptor, steroid dehy-

drogenase activity, and steroid metabolic process.

This gene may play a role in the biosynthesis of

retinoic acid. The importance of the retionic acid

to the colorectal cancer is explained in (Jette et al.,

2004).

• GUCA2A - This gene is endogenous activator of

intestinal guanylate cyclase. It is highly expressed

in ileum and colon. At the Illumina chip it showed

down expression. The ontology results showed it

is involved in a hormone activity. The possibility

GeneOntologyAnalysisofColorectalCancerBiomarkersProbedwithAffymetrixandIlluminaMicroarrays

403

that the loss of guanylin activity leads to, or, is a

result of colorectal adenocarcinoma formation is

presented in (Cohen et al., 1998).

• PYY - This gene shows down expression at Il-

lumina platform. The ontology results showed

it is also involved in a hormone activity. PYY

chemotherapy resistance in colon cancer is dis-

cussed in (Kling et al., 1999).

• HPGD - This gene encodes an enzyme that func-

tion in a variety of physiologic and cellular pro-

cesses such as inﬂammation. Inhibits in vivo pro-

liferation of colon cancer cells. It is detected in

colon epithelium. According to the ontology, this

gene is involved in oxidoreductase activity, acting

on CH-OH group of donors and oxidoreductase

activity, acting on the CH-OH group of donors,

NAD or NADP as acceptor. At the Illumina chip

it showed down expression. Its role in the colorec-

tal cancer is researched in (Holla et al., 2008).

However, we found that the set we revealed is able

to discriminate between colorectal cancer and healthy

tissue. In order to conﬁrm our hypothesis, we used

additional biomarkers set, revealed with the GEO2R

web tool available from the Gene Expression Om-

nibus database (GEO, 2013). GEO2R allows rank-

ing the most signiﬁcant biomarkers from particular

tissues. We used the same data sets as in this pa-

per, and we took into account the top 250 biomarkers.

The GEO2R biomarkers and the Illumina biomarkers

set showed overlap in 84 genes and the retrained clas-

siﬁer with this set of biomarkers showed very high

accuracy during the classiﬁcation, whereas biomark-

ers from the Affymetrix set showed overlap in only

32 genes and our model was unable to discriminate

between the two classes with this retrained classiﬁer.

Very important result is that among the small num-

ber of the overlapping genes, we found many of

the genes we conﬁrmed to be related to colorectal

cancer: CHGA, GUCA2B, MMP7, CDH3, DHRS9,

GUCA2A, PYY and HPGD.

Considering the Vogelstein’s model, none of the

genes he deﬁned as biomarkers were found in the

biomarkers we discovered.

5 CONCLUSIONS

The aim of this paper was to show whether the

biomarkers revealed by appropriately deﬁned statis-

tical methodology in Section 3.1 and that showed ex-

cellent classiﬁcation ability using the model in Sec-

tion 3.2, play important biological role in the colorec-

tal cancer development.

For that purpose, we provided gene ontology anal-

ysis, and inspected the molecular functions and the

biological processes of a particular set of genes that

were overrepresented among all biomarkers. Further-

more, using the overrepresented genes, we performed

tests over the Bayesian classiﬁer. However, the results

showed decreased precision when using only the en-

riched sets of genes as a training set. This implicated

a conclusion that for successful Bayesian modelling,

we need larger amount of data which implicates more

detailed description of the statistical distribution of

the data.

To test the methodology relevance for biomark-

ers discovery, we used another set of biomarkers, re-

trieved from the same data sets using the GEO2R on-

line tool. Comparing the sets, we perceived that at

Illumina microarray data, 84 genes overlap, whereas

at Affymetrix microarray data, the number of over-

lapped genes is 32. In addition, we retrained our

Bayesian classiﬁer with the new biomarkers. The re-

sults for the Illumina chip were promising, since the

overlapping set is larger, whereas the results for the

Affymetrix chip were a complete failure. This once

again conﬁrmed that our methodology for signiﬁcant

genes revealing produces more biologically signiﬁ-

cant biomarkers.

Considering the colorectal cancer signiﬁcance of

the biomarker genes, we exhibit few biomarkers that

are proved to be related to the disease. This was

again supported by the fact that the same signiﬁcant

biomarkers are also found in the intersection between

our biomarkers and the GEO2R biomarkers.

Therefore, in this paper we conﬁrmed that our

previously developed methodology for biomarkers re-

vealing provided successful generative model for tis-

sues and patients recognition, and for the biomarkers

involved in this model, we conﬁrmed that they are re-

lated to the colorectal cancer using the GEO2R online

tool.

Further investigations are needed to validate our

results and to identify the scientiﬁc and applicative

potential of the biomarkers for molecular diagnostics,

evaluation and prognostic purposes in patients with

colorectal cancer.

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