Original Article

Comparative transcriptome maps: a newapproach to the diagnosis of colorectalcarcinoma patients using cDNA microarrays

Jansova E, Koutna I, Krontorad P, Svoboda Z, Krivankova S,Zaloudık J, Kozubek M, Kozubek S. Comparative transcriptome maps:a new approach to the diagnosis of colorectal carcinoma patients usingcDNA microarrays.Clin Genet 2006: 69: 218–227. # Blackwell Munksgaard, 2006

The progression of colorectal cancer involves accumulation of variousgenetic and epigenetic events that dramatically change gene expression.The aim of this study was to investigate a possible new approach tothe diagnosis of colorectal carcinoma patients, based on their geneexpression profiles. Human 19K cDNA microarrays were used toanalyze the gene expression profiles of 18 colorectal carcinoma patients.Transcriptome maps (TMs) were analyzed to detect chromosomalregions that could serve as potential diagnostic markers for coloncancer. A comparison of TMs showed chromosome regions withconserved changes of gene expression typical of colorectal cancer ingeneral, and also patient-specific variable regions. We identified 195genes with significantly altered expression in colon cancer. Functionalanalysis of the regulated genes distinguished three main categories:biological processes, cellular components, and molecular functions. Wefound that different patients had chromosome regions characterized byvery similar changes of gene expression, probably linked to the mostfundamental events in carcinogenesis. On the other hand, variablechromosome regions can be patient-specific. The variable regions mayprovide further information on the individual pathogenesis andprognosis of the patient. Comparison of TMs is proposed as a tool tofacilitate diagnosis and treatment planning for individual patients.

E Jansovaa, I Koutnaa,

P Krontorada, Z Svobodaa,

S Krivankovaa, J Zaloudıkb,M Kozubeka and S Kozubekc

aFaculty of Informatics, bFaculty ofMedicine, Masaryk University, Brno,Czech Republic, and cInstitute ofBiophysics, Academy of Sciences of theCzech Republic, Brno, Czech Republic

Key words: cDNA microarrays – colorectalcarcinoma – expression profiles – func-tional clustering – transcriptome map

Corresponding author: AssociateProfessor Stanislav Kozubek, PhD,Institute of Biophysics, Academy ofSciences of the Czech Republic,Kralovopolska 135, 612 65 Brno, CzechRepublic.Tel.: þ420 5 4151 7139;fax: þ420 5 4124 0498;e-mail: kozubek@ibp.cz

Received 14 October 2005, revised andaccepted for publication 16 January 2006

Colorectal epithelial tumors are the major causeof cancer-related deaths for men and women (1).The majority of colorectal tumors (85%) aresporadic in origin, yet they have close similaritiesto tumors resulting from inherited colon cancersyndromes. About 85% of colorectal cancers arerelated to chromosomal instability (CIN) andexhibit gross chromosomal abnormalities suchas aneuploidy and loss of heterozygosity (LOH).The remaining 15% of colorectal cancers are dueto events that result in microsatellite instability(MIN) and have been firmly linked to a faultyDNA mismatch repair system (2). Adenomatouspolyposis coli (APC) or b-catenin mutations arethe most common initial molecular lesions in theCIN phenotype (3).

The progression of colon carcinoma involvesaccumulation of a series of genetic and epigeneticevents such as mutations of cancer suppressorgenes in normal colon (APC, mismatch repairgenes), methylation abnormalities (APC, b-catenin), or protooncogene mutations leading toadenoma (K-ras) and others (1, 4–8).cDNA microarray technology, developed over

the last decade, provides a tool for determinationof expression profiles in normal and tumortissues. This method is based on monitoring theexpression level of thousands of genes simulta-neously and allows identification of the diseaseat the genome level. Previous studies usingcDNA technology have been performed foracute leukemia, lymphoma, breast cancer, and

Clin Genet 2006: 69: 218–227 Copyright # 2006 Blackwell Munksgaard

Printed in Singapore. All rights reserved No claim to original US government works

CLINICAL GENETICS

doi: 10.1111/j.1399-0004.2006.00588.x

218

other malignancies (9–14). Oligonucleotide andcDNA microarrays have also been used formonitoring mRNA transcription levels in colonadenocarcinomas, adenomas, and normal tissue(15–17). Kitahara compared expression profilesof individual colorectal cancer cells obtained bylaser-capture microdissection with expressionprofiles of corresponding colonic epithelia usingDNA microarrays (18). These studies showeddifferences in gene expression between normaland cancerous colonic epithelium, or betweennormal tissue and carcinoma tissue from variousDukes’ stages. Moreover, genes associated withlymph node metastasis were detected and theirfunctions were analyzed (19).The aims of our study were to identify differ-

ences in gene expression profiles of colorectalcarcinoma and normal colon tissue, and to tryto diagnose patients according to their expressionprofiles. We found that 195 genes had signifi-cantly altered expression: 31 genes were upregu-lated and 164 were downregulated in coloncancer compared with normal colon epithelium.Our study presents the results on up- and down-regulated genes identified using transcriptomemaps (TMs). Comparison of TMs for individualpatients showed regions with very similarchanges in various colorectal cancer patients,and also regions of high variability among thepatients. Using functional clustering, severalgroups of genes were identified (e.g. genes encod-ing structure-related proteins). This approachprovides additional information that might beof interest for diagnosis and treatment planning.

Materials and methods

Tissue samples

Samples of colon adenocarcinoma were obtainedfrom patients at the Masaryk Memorial CancerInstitute, Brno, and the University Hospital, Brno.The tissue was snap-frozen in a liquid pro-pane–butane solution immediately after resectionand stored at �80 �C until needed. The samples ofcancer tissue included patients without metas-tases, and patients with metastases in regionallymph nodes. All presumptive diagnoses wereconfirmed by pathologists using biopsy and his-tological examination of tumor and surroundingtissue, including regional lymph nodes. Informedconsent was obtained from all patients, prior toenrollment in the study, under a protocolapproved by the Masaryk Memorial CancerInstitute and the University Hospital, Brno. Everytissue was homogenized (Polytron System PT

1200CL), and total RNA was extracted fromthe frozen tissue with TRI REAGENT (Sigma-Aldrich Inc., Germany). Each sample was then pur-ified with an RNeasy Mini Kit (Qiagen GmBH,Germany). The acquired RNA was dissolved inRNase-free water (Sigma). This method is a mod-ification of a protocol for isolation of high-qualityRNA from tumors (20, 21). Arabidopsis cDNA,used to check the hybridization process, wasobtained from pArab plasmid (theUHN Microarray Centre) by in vitro transcription(using a T7 Transcription Kit, Fermentas; SacI,Fermentas; T4 DNA Polymerase, FermentasInternational Inc., Canada). Total human colonRNA (Clontech, USA) was used as a commonreference for all patients.

Microarrays

The microarray slides used in this study wereHuman 19K single-spotted microarrays [ClinicalGenomic Centre (CGC), Toronto, Ontario,Canada] containing 19,008 characterized andunknown human ESTs. cDNA fragments wereplaced into 12 grid rows, four grid columns, 20subrows, and 20 subcolumns.

Probe preparation, hybridization, and washing

For each microarray experiment, 10–20 mg of sam-ples of purified total RNA prepared from patientswith colorectal carcinoma was labeled by incorpor-ating Cy3- and Cy5-dCTP (Amersham PharmaciaUK) using Oligo(dT) Primers (Anchored,Gaithersburg, MD, USA) and SuperScript II(Invitrogen Invitrogen Corporation, CA, USA)during RT-PCR. Acquired labeled cDNA was pre-cipitated by isopropanol (protocols from the OCIMicroarray Centre, 2000, University HealthNetwork/Ontario Centre Institute, Toronto,Ontario, Canada). Cy5 dye labeled the colorectalcarcinoma tissue, and Cy3 dye labeled the normalcolon tissue. The microarray slides were incubatedin 30 ml of hybridization solution (DIG Easy HybGranules, Roche,Mannheim,Germany) containingCy3- and Cy5-labeled target, 5 ml of yeast tRNA(Invitrogen), and 5 ml of calf thymus DNA (LifeTechnologies, Gaithersburg, MD, USA).Hybridization was performed by incubation ofslides in a hybridization chamber overnight at37 �C. The following day, the slides were washedthree times in 1� SSC containing 0.1% SDS for10 min at 50 �C. After washing, the samples wererinsed in 1� SSC (according to protocols from theOCI Microarray Centre, 2000). Expression wasassayed by measuring fluorescence intensities using

Transcriptome maps as diagnostic tools

219

the ScanArray Express V2.0 (PerkinElmer, USA)confocal laser scanner that produced paired 16-bitTIFF images. To confirm the results, reciprocallabeling was applied. Analysis of reciprocallylabeled samples showed levels of gene expressionvery similar to the standard labeling procedure.Average values were calculated for furtherprocessing.

Reverse transcriptase-polymerase chain reaction

To confirm the cDNA microarray results, reversetranscriptase-polymerase chain reaction (RT-PCR) was performed on six genes. Five genesshowed different expression in normal colon tis-sue and in colorectal carcinoma tissue; one genewas used as a control. Total RNA samples werereverse-transcribed to single-stranded cDNAusing oligo(dT) primer (Anchored) withSuperScript II Reverse Transcriptase(Invitrogen). Each diluted cDNA was used as atemplate for subsequent PCR amplification.Each PCR was carried out in a 50 ml volume ofmaster mix with 1 mM MgCl2 and Tag DNApolymerase, for 5 min at 94 �C for initialdenaturing, followed by 35 cycles of 30 s at94 �C, 30 s at 58 �C, and 1.5 min at 72 �C, inthe PCR system (PTC-100, MJ Research, Inc.,USA). Results of RT-PCR were confirmed bymobility gel electrophoresis and evaluated byGeneTools V3.00.22 (SynGene, Cambridge,England). 18S rRNA served as an internal con-trol for comparison of expression of the testedgenes (22).

Image analysis and data processing

To analyze images produced by the scanner, weused software developed in our laboratory. A gridwas automatically set up for each image, and thealignment to the array of spots was checked.Noise in the images was reduced using medianfiltering. Each spot was segmented individuallyby the gradient-weighted segmentation method(23). We implemented a procedure that decidedthe class of the area and performed automaticquality analysis. The procedure also removedsome defective spots according to the shape(circularity) and position of the spot. The spotswere quantified by taking the mean of the inten-sities of their pixels. Background intensity for eachspot was determined from the background sur-rounding the spot by taking the mean of the back-ground area. This background value wassubtracted from the intensity of the spot.

We used single-channel analysis to identifygenes whose expression had changed. Single-channel normalization of two-color cDNAmicroarray experiments can be considered as atwo-stage process: within-array normalizationfollowed by between-array normalization (24).In addressing the within-array problem, weapplied the local scatter plot smoother functionLOWESS (locally weighted scatter plot smooth-ing) for local normalization (25, 26). LOWESSallowed us to account for intensity and spatiallydependent bias. The smoothing parameter wasset to 0.33. In the second stage of single-channelnormalization, the between-array normalizationis performed. We used quantile normalization asproposed by Bolstad (27).To identify genes or ESTs that were differen-

tially expressed in colorectal cancer, a variationfilter was selected. For the selection, a relativechange of 1.5-fold and an absolute change of500 U were set up. Genes with increased ordecreased expression in more than 67% patients(12 of 18 cases) were defined as up- or down-regulated, respectively.Analysis of TMs was performed as follows. A

chromosome visualization utility was implemen-ted that gathered data from online databases andmapped genes on chromosomes at a user-definedsampling frequency. TMs of individual patientswere constructed using average values for geneexpression obtained from replicated experiments(with direct and reciprocal labeling) in both col-orectal cancer and normal epithelial tissue.Median values of TMs of 18 patients were finallyconstructed. Differential TMs (DTMs) were con-structed as median normal tissue values sub-tracted from median cancer values, as wellas median metastatic patient values subtractedfrom median non-metastatic patient values.DTMs were calculated also for each patient asthe TM of the individual patient minus the med-ian TM. Larger regions with conserved changes ofgene expression in tumors consist of at least fivegenes within a molecular distance 10 Mbp and withcumulative change in gene expression higher than10,000 U (absolute values were taken).For functional classification of genes, the

GENESPRING software (28) was used (trial ver-sion http://www.silicongenetics.com). This is awidely used package designed for the analysis ofgene expression data, especially for the functionalclustering of genes according to their biologicalrole. The resulting gene ontology tree containedthree main lists: ‘the biological process’, ‘the cel-lular component’, and ‘the molecular function’.Hierarchical clustering (HCL) was performedaccording to Eisen et al. (29).

Jansova et al.

220

Results

Human 19K cDNAmicroarrays containing 19,000genes and ESTs were used for determination ofgene expression in 18 samples of tumors obtainedfrom patients with colorectal carcinomas. The setof patients included eight men and 10 women; 50%were metastatic patients and 50% non-metastaticpatients. The age of the patients was around 65years. Using software developed in our laboratory,TMs were constructed for all patients. The geneexpression levels for both colorectal cancer andnormal epithelial tissue were compared. Basic ana-lysis revealed 195 genes with significantly alteredexpression levels: 164 genes were downregulatedand 31 genes were upregulated. We focused onfurther analysis of TMs that allowed detailed ana-lysis of expression profiles along all chromosomes(see Materials and methods).The median values of the expression levels of

19,008 genes calculated using the data from 18tumor samples are shown in Fig. 1, together withdifferential maps showing the medians of differ-ences between tumor and normal tissue expres-sion. We can easily recognize highly expressedgenes (Fig. 1, column 1), as well as genes withsignificantly altered levels of expression in thewhole group of tumors (Fig. 1, column 2). Inaddition, chromosome regions with very similarchanges of expression for colorectal carcinomapatients (conserved component of TMs) can easilybe distinguished. Examples of larger conservedregions (shown by the rectangles) are located onchromosomes 1, 3, 6, 9, 12, 17, 19, and 20 (Fig. 1).The TMs for individual patients are subtracted

from the median values. This subtraction givesthe variable component of the patient’s TM; inother words, patient-specific events are seen. Onthe basis of variable components of individualpatients, variable chromosome regions can beidentified. Examples of variable regions are loca-lized on chromosome 1 (180–240 Mbp) and onchromosome 11 (110–120 Mbp). The variableregion detected on chromosome 1 included theCHML gene for the RAB escort protein 2 thatsupports geranylgeranylation of most Rab pro-teins (a group of GTP-binding proteins asso-ciated with distinct cellular traffickingpathways), the RPS6KC1 gene encoding riboso-mal protein S6 kinase that can phosphorylate 40Sribosomal protein S6 and the IER5 gene forimmediate early response protein 5 that playsan important role in mediating the cellularresponse to mitogenic signals, and other genes.The variable region on chromosome 11

included, among other genes, the FXYD6 geneencoding one of the small membrane proteins

(gamma subunit of Na, K-ATPase) involved inthe control of ion transport or inducing channelactivity, the SC5DL gene encoding sterol C5-desaturase-like, which is involved in cholesterolbiosynthesis (it is thought to be an integralmembrane protein), and the SIK2 gene encodingsalt-inducible serine/threonine kinase 2 phos-phorylating IRS-1 and modulating the efficiencyof insulin signal transduction. The genes detectedin variable regions are mainly connected withmetabolism or signaling pathways. The variablegenome regions may provide further informationabout the state of individual genes and might beused for the prognosis of carcinogenesis in aparticular patient.Comparison of TMs provides another approach

for more detailed processing of microarray datathat is more instructive for the analysis of indivi-dual patients. Median TMs were calculated fornon-metastatic and metastatic patients (Fig. 2, col-umns 1 and 2) as well as the differential TMs(Fig. 2, column 3). These maps show genes orshort regions that are different in metastatic andnon-metastatic patients, which can be of potentialinterest for diagnosis. Examples were found onchromosomes 2, 6, 12, and 22 (see rectangles inFig. 2). In these regions, genes were found thatwere connected with decreased immune response ordephosphorylation of targeting proteins of signalingpathways in metastatic patients. Genes withincreased expression in metastatic patients were con-nected with multidrug resistance, regulation of mito-sis, or transport between nucleus and cytoplasm(Table 1). Using these maps for patient diagnosis, itwas possible to recognize the occurrence of lymphnode metastasis, and to divide patients into meta-static and non-metastatic patients.The GENESPRING computer analysis allowed

us to cluster up- and downregulated genes accord-ing to their function and their involvement in aspecific biological pathway. We obtained fourmain categories: biological processes, cellular com-ponents, molecular functions, and genes withunknown functions. These categories were dividedinto various subcategories for both up- and down-regulated genes (Table 2). Both up- and downregu-lated genes were mostly related to the ‘biologicalprocess’ category. The second largest category was‘molecular function’ for downregulated genes and‘cellular component’ for upregulated genes.More detailed analysis of the functional groups

allowed us to identify groups of genes encodingstructure-related proteins. Some of these groupsof genes were found in specific chromosomalregions that represent part of a conserved com-ponent of the TM (arrows in Fig. 1; Table 3).Ribosomal proteins (RPL31, RPL30, RPS24,

Transcriptome maps as diagnostic tools

221

Chromosome 1

Chromosome 7 Chromosome 8 Chromosome 9 Chromosome 10 Chromosome 11 Chromosome 12

Chromosome 2 Chromosome 3 Chromosome 4 Chromosome 5 Chromosome 6

1 2

1 1 1 1 1 12

Chromosome 13

Chromosome 19 Chromosome 20 Chromosome 21 Chromosome 22 Chromosome X Chromosome Y

Chromosome 14 Chromosome 15 Chromosome 16 Chromosome 17 Chromosome 18

1 12

1 2 1 2 1 2 1 2 1 2 1 2

1 1 1 12 2 2 2 2

2 2 2 2 2

1 1 1 1 12 2 2 2 2

Fig. 1. Conserved chromosome components. A transcriptome map (TM) based on Human 19K cDNA microarray data isshown for colorectal cancer (red) and normal colon epithelium (green). Median expression levels for 18 patients are presentedin column 1. Differential TM (DTM) showing the differences in expression between median tumor tissue and median normalepithelium (upregulated in cancer on the right and downregulated on the left) is shown in column 2 (for better visualization,DTM was multiplied by a factor of 5). Larger regions of conserved changes of gene expression (see Materials and methods)are enclosed in blue rectangles. Blue arrows mark positional clusters of genes downregulated in tumor tissue that encodestructure-related proteins (e.g. metallothioneins and immunoglobulins) which also belong to the conserved component ofthe TM.

Jansova et al.

222

and RPS3A) and collagens (COL4A1, COL1A2,COL3A1, and WBSCR24) were found amongthe upregulated genes. Among the downregu-lated genes, two major groups of genes encodingstructure-related proteins could be detected, andfour smaller groups. The first large gene groupencoded immunoglobulins (Igs) (CLUD2, IGJ,IGHM, MRGX4, BML-IGVL gene, IGHV32,IGLJ3, LOC 91316, IGKC, and SNTG2),members of the Ig supergene family localized onchromosomes 14q32.3, 2p12, and 22q11. This setincluded genes of heavy chain particles, lightchain kappa particles, and light chain lambdaparticles. The second major group containedgenes for metallothioneins (MT2A, MT1B,MT1F, MT1G, and MT1H) localized on

chromosome 16q13. The other groups of downre-gulated structure-related genes were actins(ACTG2, ACTA2, and ACTR1A), keratins (KRT19 andKRT 8), integrins (ITGA3 and ITGB4), andmyosins (MYL4, TPM2, and MYH11).To confirm the reliability of the microarray

data, RT-PCR was performed using six genes(three downregulated, two upregulated, and onecontrol gene). The group of downregulated genesincluded the CA2, MT1B, and IGKC genes, ofwhich CA2 is considered to be the basal color-ectal marker; the upregulated genes were repre-sented by RPL31 and COL3A1; FTL was used asa control gene. RT-PCR confirmed the samepatterns of changes of gene expression as cDNAmicroarrays for all of the tested genes (Fig. 3).

Chromosome 2

1 1 1 12 2 2 23 3 3 3

Chromosome 6 Chromosome 12 Chromosome 22

Fig. 2. Transcriptome maps (TMs) as a diagnostic tool for metastatic patients. Median gene intensities from non-metastaticpatients are shown in column 1; median gene intensities from metastatic patients are shown in column 2. The TMs forcolorectal cancer (red) and normal colon epithelium (green) are based on Human 19K cDNA microarray data. DifferentialTM (column 3) showing the variability of tumor tissue between non-metastatic and metastatic patients is calculated as thedifference: [median of non-metastatic TM] � [median of metastatic TM] (upregulated in metastatic patients on the left, anddownregulated on the right). For better visualization, DTM was multiplied by a factor of 5.

Table 1. Examples of genes included in short regions that are different in metastatic and non-metastatic patients

Accession number Unigene ID Symbol Function Chromosome Location (Mbp)

H44273 Hs.512139 HypotheticalLOC400969

Unknown 2 88.9

BG684218 Hs.449621 IGKC Defense immunity protein activity 2 88.9

BM559619 Hs. 196437 MOBK1B Regulation of mitosis 2 74.3

BM980091 Hs.171802 RNF149 Ubiquitin ligase activity 2 101

H60498 Hs.121088 NUP153 Transport 6 17.7

R89198 Hs.486520 ALDH8A1 Retinal dehydrogenase activity 6 135

H16193 Hs.446050 ABCC9 Multidrug resistance 12 21.8

T80874 Hs.192570 FLJ22028 Unknown 12 21.5

W92881 Hs.112553 HypotheticalLOC387873

Unknown 12 91.9

H50621 Hs.252543 IKIP Apoptosis 12 97.5N24477 Hs.407693 LOC91316 Defense immunity protein activity 22 22.38R95770 Hs.474536 MTMR3 Phosphatase activity 22 28.65

Transcriptome maps as diagnostic tools

223

Discussion

Colon cancer is a major cause of cancer-relateddeaths, and thus it remains in the focus of attentionof many laboratories. Most recent studies on color-ectal cancer are concerned with monitoring differ-ences in gene expression between normal epitheliumand tumor tissue. Hierarchical clustering showedthat it was possible to distinguish samples withand without metastases, and from different sites(16, 17). In other studies, normal colon epitheliumand primary colon cancer cell lines were used toidentify several potential tumor markers (30). Inthe detailed analyses of Birkenkamp-Dermtroderet al. (17) and Frederiksen et al. (31), differentialgene expression at various tumor stages (Dukes’stages A–D) was investigated. Candidate tumorsuppressor genes and oncogenes were identifiedand mapped to individual chromosomes. In addi-tion, chromosomal clusters of regulated genes werefound (17). Several studies also focused on geneexpression in various metastatic cell lines (32–34),in which potential metastatic markers have beenidentified.

In this study, we performed a detailed analysisof the gene expression profiles of 18 patients withcolorectal carcinoma. Comparison of TMs, cre-ated on the basis of cDNA microarray resultsand the NCBI database, represents a powerfuland convenient tool for analyzing the expressionprofiles of patients. This analysis provides infor-mation on genome areas in which changes ofgene expression are very similar for all patients,and which may thus allow more reliable primarydiagnosis of the patient. The regions detected inour study could be related to gene expressionspecific in colorectal cancer, a finding that meritsfurther investigation.On the other hand, the remaining genes with

altered expression are either dispersed randomlyor may form variable regions on the genome, andrepresent a variable component of the comparativeexpression profile. The variability between indivi-dual patients is found mainly in genes connectedwith modification of proteins included in signalingpathways (phosphorylation and geranylgeranyla-tion), transport, and mitogenic response. Thegenes included in the variable regions might berelated either to biological variability of individualpatients (at the level of basal metabolism, geneticpredisposition, nutrition, etc.) or to progression ofthe illness, or even to differences in response to thetreatment. The comparison of TMs can be used formore detailed diagnosis of patients. For example,the appearance of metastases in patients correlateswith particular changes in their TMs.In addition to conserved and variable regions

that distinguish cancer tissue from normal epi-thelium, specific chromosomal regions werefound that were potentially diagnostic for theoccurrence of metastases in patients. Theseregions included genes connected with, for exam-ple, metabolism, transport, cell-cycle regulation,and the immune response. For example, theMOBK1 gene encodes the protein involved inregulation of the late phase of mitosis, while theNUP153 gene encodes nucleoporin that mediatesregulated movement between the nucleus andcytoplasm. Another such gene is IKIP, encodingthe IKK-interacting protein, which is suggested

Table 2. Functional clustering of up- and downregulatedgenes

Upregulated genes %Downregulatedgenes %

Biological process 50 Biological process 57.4Cell communication 25.8 Cell communication 29Cell growth and ormaintenance

37.9 Cell cycle, growth,proliferation

35.5

Development 36.3 Death 0.5Development 35

Cellular component 28 Cellular component 12.3Cytoplasm 31.3 Lysozyme, cytosol,

mitochondrionPlasma membrane 50Ribosome 18.7Molecular function 16 Molecular function 25.3Metabolism Transport 16.4

Catalytic activity 19.5Defense immunityprotein activity

32.8

Binding andsignalization

31.3

Unknown function 6 Unknown function 5

Table 3. Functions of chromosomal clusters

Chromosome CytoBand Localization (Mbp) Function Size of the region (Mbp) na Ratio of expression levelsb

2 2p11 88.26 Igs 0.73 3/5 1.6–3.36 6p21.3 30.9 MHC 0.50 3/6 1.99–2.214 14q32 103.05 Igs 2.95 5/20 1.8–4.016 16q13 55.2 MT 0.06 5/5 1.5–2.022 22q11 20.7 Igs 0.80 8/8 1.9–3.2

Igs, immunoglobulins; MHC, members of the major histocompatibility complex; MT, metallothioneins.aNumber of downregulated genes/number of genes in the region (silenced in tumor tissue).bRelative changes of gene expression (normal tissue/tumor tissue).

Jansova et al.

224

as a novel p53 target gene with proapoptoticfunction. This approach allowed us to classifythe patients into two groups that coincided withthe presence or absence of metastases. Ourgrouping of patients obtained by comparingtheir TMs agrees with that obtained by otherauthors who have studied colon cancer (15, 16,31, 35).Significant differences in expression between

tumor tissue and normal epithelium weredetected for 195 genes. Functional clustering of164 downregulated genes and 31 upregulatedgenes was performed by GENESPRING soft-ware. For the prominent genes, three fundamen-tal categories related to gene function weredistinguished: biological processes, cellular com-ponents, and molecular functions. Within thesecategories, genes were divided into subcategoriesaccording to their connection with special biolo-gical pathways or processes such as cell commu-nication, death, transport, metabolism, etc. Theseresults are supported by findings by otherauthors who have classified genes into similarsubcategories. For example, regulated genes incolon cancer were attributed to the category ‘bio-logical process and pathways’, such as cell cycle,tumorigenesis, oncogenes, transcription, etc. (31,32, 36). In the study of gene expression in lymphnode-positive and lymph node-negative colorec-tal cancers published by Kwon et al. (19), geneswhich are involved in transcription, signal trans-duction, and metabolism, and some oncogenes/tumor suppressor genes were detected. The mon-itoring of age-associated changes in gene expres-sion in colonic cancer allowed us to classify theregulated genes into several groups connectedwith the cell cycle and nuclear proteins, nutrientdigestion and absorption, intercellular signaling,metabolic enzymes, etc. (37). Genes regulated inour experiments were found in similar categoriesand subcategories (e.g. gene/protein expression,

immunology, cell structure and motility, metabo-lism, cell cycle and nuclear proteins, signal trans-duction, etc.).In addition, we detected genes encoding struc-

ture-related proteins. Among the downregulatedgenes, we found two major groups (genes encod-ing metallothioneins and Igs) and several smallergroups (genes encoding actins, myosins, keratins,and integrins).The metallothioneins (MTs) belong to the

superfamily of ubiquitous low-molecular-weightmetal-binding proteins with a high content ofcysteinyl residues and a strong affinity for diva-lent heavy metal ions such as zinc, copper,cadmium, mercury, silver, and platinum (38,39). The immunohistochemical detection of MToverexpression was associated with poor clinicaloutcome, for example, in invasive ductal adeno-carcinoma of the breast and in malignant mela-noma (39). Immunohistochemical study of MTexpression in colorectal carcinoma showed statis-tically significant downregulation associated withtumor stage and lymph node involvement (38, 40,41). These results have been interpreted by theauthors as early events in colonic carcinogenesis,suggesting a favorable clinical outcome ofMT-positive colonic carcinoma. Our experimen-tal data showing downregulation of MT genesagree with these findings. The comparison ofmetastatic and non-metastatic patients showedsimilar downregulation of MT genes in bothgroups.The Ig genes belong to the Ig supergene family

encoding structure-related glycoproteins. In thehuman genome, there are three complex genesuperfamilies: IGH for heavy chains on chromo-some 14, IGK for light chain kappa on chromo-some 2, and IGL for light chain lambda onchromosome 22. Carcinogenesis of tumors is clo-sely related to immunological surveillance by thehost. Previous studies proved the significance ofimmune responses for cancer surveillance (42).In addition, the gene was identified that encodesone Ig heavy chain molecule (SNC73) and parti-cipates in local anti-tumor activity; this gene isdownregulated in colorectal cancer (43). In ourstudy, many Ig genes were downregulated intumor tissue. These findings could be related tolower immune response in tumor tissue. The rela-tionship between Ig gene expression and carcino-genesis of colorectal cancer requires furtherstudy.Actins are protein constituents of the microfila-

ments that together with the myosins andthe tropomyosins form cell stress fibers andother cellular structures. They are found in boththe cytoplasm and nucleus of the cell, and

MT1BT

C5

C9

C13

N T N T N T N T N T NIGKC CAII RPL31 COL3A1 FTL

Fig. 3. Validation of the results of cDNA microarrays usingreverse transcriptase-polymerase chain reaction (RT-PCR).RT-PCR was used to determine changes of expression of sixgenes in colorectal cancer compared with non-canceroustissue: MT1B, IGKC, and CA2 (downregulated); RPL31and COL3A1 (upregulated), and FTL (without significantchanges in gene expression). N, normal tissue; T, tumortissue; C5, C9, and C13 indicate individual patients used inthis study.

Transcriptome maps as diagnostic tools

225

respond to both epigenetic signals andaltered gene expression occurring during tumor-igenesis. The stress fibers play an importantstructural role in the maintenance of adhesionplaques on the cell surface. The keratins belongto the group of intermediary microfilamentsoccurring in epithelial cells and mediate attach-ment of cells to the substratum. These threegene groups (actins, myosins, and keratins)encode cytoskeletal proteins. The last, smallergroup of genes encodes integrins, the transmem-brane proteins mediating cell adhesion andcell survival and proliferation signalization. Allthese groups of genes are involved in metastaticspread.The upregulated genes included only two

groups encoding structure-related proteins (ribo-somal proteins and collagens). The main functionof ribosomal proteins is in translation, which isconsistent with cell proliferation during tumori-genesis. Our study results concerning expressionof ribosomal proteins are supported by otherauthors (35).Genes encoding structure-related proteins were

detected by other authors as well; for example,several ribosomal proteins (RPL23a, RPS19,RPS28, and RPL30), collagens (COL1A2 andCOL3A1), MTs (MT3 and MT1B), etc. (5, 18,33, 44). These genes have been detected as indi-vidual genes, not as part of groups of genes as inour experiments. Some of the genes detected inour study have been reported by other authors(e.g. RPL30, COL1A2, COL3A1, and MT1B).Some genes from our groups could serve aspotential candidates for tumor markers, becausethey may contribute to transformation of normalepithelium into tumor tissue, with the acquisitionof malignant potential.Among the genes with significantly altered

expression, carbonic anhydrase II (CA2) is animportant candidate for a colorectalcarcinoma marker which is downregulated intumor tissue. Most authors have found that theexpression of this gene is changed during tumortransformation in the colon (15, 18, 30, 44);changes in its expression are related to poorprognosis of colon cancer. The expression ofCA2 correlates with biological aggressiveness ofcolorectal cancer and with the appearance ofsynchronous distant metastases. The presence ofCA2 is often associated with a more favorableoutcome in colorectal cancer (45, 46). We founddownregulation of the CA2 gene in colorectalcarcinoma compared with normal epitheliumbut did not detect any significant difference inits expression between metastatic and non-meta-static patients.

In this study, we have distinguished chromo-some regions with consistently conserved changesof gene expression among patients, and variableregions with patient-specific down- or upregula-tion of genes. We propose that TMs are potentialtools that should allow more sophisticated diag-nosis of various patients, based on analysis oftheir gene expression profiles, and could facilitatetreatment planning. It should be pointed out thatwe used only a fraction (19,000) of the totalnumber of genes, and consequently, our studycannot give a complete picture of the conservedregions. In addition, we used only 18 patients inthis study (nine patients in each group, with andwithout metastases) and we consider it necessaryto test TMs as a potential diagnostic tool on alarger group of patients in a clinical study.

Acknowledgements

This work was supported by the Grant Agency of the Ministry ofHealth of the Czech Republic (grant 1A/8241-3), the Ministry ofEducation, Youth and Sports (grants MSM0021622419), theAcademy of Sciences (grants Z50040507, IAA5004306, andA1065203) and the Grant Agency of the Czech Republic (grants202/04/0907). We thank the Masaryk Memorial Cancer Institute,Brno, and the University Hospital, Brno, for providing colorectalcarcinoma and colon epithelium samples, and for allowing us toscan our microarrays on a PerkinElmer scanner.

References

1. Cotran RS, Kumar V, Collins T. Robbins Pathologic Basisof Disease. Philadelphia: W.B. Saunders Company, 1999.

2. Dunican DS, McWilliam P, Tighe O, Parle-McDermott A,Croke DT. Gene expression differences between the micro-satellite instability (MIN) and chromosomal instability(CIN) phenotypes in colorectal cancer revealed by high-density cDNA array hybridization. Oncogene 2002: 21:3253–3257.

3. Kinzler KW, Vogelstein B. Lessons from hereditary color-ectal cancer. Cell 1996: 87: 159–170.

4. Fearon ER, Vogelstein B. A genetic model for colorectaltumorigenesis. Cell 1990: 61: 759–767.

5. Otsuka M, Kato M, Yoshikawa T et al. Differential expres-sion of the L-Plasmin gene in human colorectal cancerprogression and metastasis. Biochem Biophys ResCommun 2001: 289: 876–881.

6. Suzuki H, Gabrielson E, Chen W et al. A genomic screenfor genes upregulated by demethylation and histone deace-tylase inhibition in human colorectal cancer. Nat Genet2002: 31: 141–149.

7. Platzer P, Upender MB, Wilson K et al. Silence of chromo-somal amplifications in colon cancer. Cancer Res 2002: 62:1134–1138.

8. Liefers GJ, Tollenaar RAEM. Cancer genetics and theirapplication to individual medicine. Eur J Cancer 2002: 38:872–879.

9. Chu S, DeRisi J, Eisen M et al. The transcriptional pro-gram of sporulation in budding yeast. Science 1998: 282:699–705.

Jansova et al.

226

10. Spellman PT, Sherlock G, Zhang MQ et al. Comprehensiveidentification of cell cycle-regulated genes of the yeastSaccharomyces cerevisiae by microarray hybridization.Mol Biol Cell 1998: 9: 3273–3297.

11. Eisen MB, Brown PO. DNA arrays for analysis of geneexpression. Methods Enzymol 1999: 303: 179–205.

12. Perou CM, Jeffrey SS, van de Rijn M et al. Distinctive geneexpression patterns in human mammary epithelial cells andbreast cancers. Proc Natl Acad Sci USA 1999: 96: 9212–9217.

13. Perou CM, Sorlie T, Eisen MB et al. Molecular portraits ofhuman breast tumours. Nature 2000: 406: 747–752.

14. Alizadeh AA, Eisen MB, Davis RE et al. Distinct types ofdiffuse large B-cell lymphoma identified by gene expressionprofiling. Nature 2000: 403: 503–511.

15. Notterman DA, Alon U, Sierk AJ, Levine AJ.Transcriptional gene expression profiles of colorectal ade-noma, adenocarcinoma, and normal tissue examined byoligonucleotide arrays. Cancer Res 2001: 61: 3124–3130.

16. Zou TT, Selaru FM, Xu Y et al. Application of cDNAmicroarrays to generate a molecular taxonomy capable ofdistinguishing between colon cancer and normal colon.Oncogene 2002: 21: 4855–4862.

17. Birkenkamp-Demtroder K, Christensen LL, Olesen SHet al. Gene expression in colorectal cancer. Cancer Res2002: 62: 4352–4363.

18. Kitahara O, Furukawa Y, Tanaka T et al. Alterations ofgene expression during colorectal carcinogenesis revealedby cDNA microarrays after laser-capture microdissectionof tumor tissues and normal epithelia. Cancer Res 2001: 61:3544–3549.

19. Kwon HC, Kim SH, Roh MS et al. Gene expression profil-ing in lymph node-positive and lymph node-negative color-ectal cancer. Dis Colon Rectum 2004: 47: 141–152.

20. Baelde HJ. High quality RNA isolation from tumours withlow cellularity and high extracelular matrix component forcDNA microarrays: application to chondrosarcoma. J ClinPathol 2001: 54: 778–782.

21. Bowtell D, Sambrook J. DNA Microarrays A MolecularCloning Manual. New York: Cold Spring HarborLaboratory Press, 2003.

22. Selvey S, Thompson EW, Matthaei K, Lea RA, Irving MG,Griffiths LR. b-actin – an unsuitable internal control forRT-PCR. Mol Cell Probes 2001: 15: 307–311.

23. MacAulay C, Palcic B. A comparison of some quick andsimple threshold selection methods for stained cell. AnalQuant Cytol Histol 1988: 10: 134–138.

24. Cleveland WS. Robust locally-weighted regression andsmoothing scatterplots. J Am Stat Assoc 1979: 74: 829–836.

25. Yang IV, Chen E, Hasseman JP et al. Within the fold:assessing differential expression measures and reproducibil-ity in microarray assays. Genome Biol 2002: 3:0062.1–0062.12. (research)

26. Yang YH, Dudoit S, Luu P et al. Normalization for cDNAmicroarray data: a robust composite method addressingsingle and multiple slide systematic variation. NucleicAcids Res 2002: 30: e15.

27. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A com-parison of normalization methods for high density oligonu-cleotide array data based on variance and bias.Bioinformatics 2003: 19: 185–193.

28. Abiko Y, Hiratsuka K, Kiyama-Kishikawa M, TsushimaK, Ohta M, Sasahara H. Profiling of differentiallyexpressed genes in human gingival epithelial cells and fibro-blasts by DNA microarray. J Oral Sci 2004: 46: 19–24.

29. Eisen MB, Spellman PT, Brown OP, Botstein D. Clusteranalysis and display of genome-wide expression pattern.Proc Natl Acad Sci USA 1998: 95: 14863–14868.

30. Zhang L, Zhou W, Velculescu VE et al. Gene expressionprofiles in normal and cancer cells. Science 1997: 276:1268–1272.

31. Frederiksen CM, Kundsen S, Laurberg S, Orntoft TF.Classification of Dukes’ B and C colorectal cancers usingexpression arrays. J Cancer Res Clin Oncol 2003: 129:263–271.

32. Hegde P, Qi R, Gaspard R et al. Identification of tumormarkers in models of human colorectal cancer using a19,200-element complementary DNA microarray. CancerRes 2001: 61: 7792–7797.

33. Ramaswamy S, Ross KN, Lander ES, Golub TR. A mole-cular signature of metastasis in primary solid tumors. NatGenet 2003: 33: 49–54.

34. Buckhaults P, Zhang Z, Chen YC et al. Identifying tumororigin using a gene expression-based classification map.Cancer Res 2003: 63: 4144–4149.

35. Wang J, Bo TH, Jonassen I, Myklebost O, Hovig E. Tumorclassification and marker gene prediction by feature selec-tion and fuzzy c-means clustering using microarray data.BMC Bioinformatics 2003: 4: 60.

36. Alon U, Barkai N, Notterman DA et al. Broad patterns ofgene expression revealed by clustering analysis of tumorand normal colon tissue probed by oligonucleotide arrays.Proc Natl Acad Sci USA 1999: 96: 6745–6750.

37. Lee HM, Greeley GH Jr, Englander EW. Age-associatedchanges in gene expression patterns in the duodenum andcolon of rats. Mech Ageing Dev 2001: 122: 355–371.

38. Giuffre G, Barresi G, Sturniolo GC, Sarnelli R, Inca RD,Tuccari G. Immunohistochemical expression of metal-lothionein in normal human colorectal mucosa, in adeno-mas and in adenocarcinomas and their associatedmetastases. Histopapathology 1996: 29: 347–354.

39. Zelena J, Potesil D, Vacek J et al. Metallothionein as aprognostic marker of tumor disease. Klinicka Onkologie2004: 6: 190–195.

40. Ofner D, Maier H, Riedmann B et al. Immunohistochemicalmetallothionein expression in colorectal adenocarcinoma:correlation with tumor stage and patient survival. VirchowsArch 1994: 425: 491–497.

41. Tuccari G, Giuffre G, Barresi G. Immunohistochemicalexpression of metallothioneins in colorectal carcinoma.Virchows Arch 1996: 427: 547–548.

42. Keren DF, Silbart LK, Lincoln PM, Annesely TM.Significance of immune responses to mucosal carcinogens:a hypothesis and a workable model system. PatholImmunopathol Res 1986: 5: 265–277.

43. Hu JB, Zheng S, Deng YC. Expression of a novel immuno-globulin gene SNC73 in human cancer and non-canceroustissues. World J Gastroenterol 2003: 9 (5): 1054–1057.

44. Takemasa I, Higuchi H, Yamamoto H et al. Constructionof preferential cDNA microarray specialized for humancolorectal carcinoma: molecular sketch of colorectal cancer.Biochem Biophys Res Commun 2001: 285: 1244–1249.

45. Bekku S, Mochizuki H, Yamamoto T, Ueno H, TakayamaE, Tadakuma T. Expression of carbonic anhydrase I and IIand correlation to clinical aspects of colorectal cancer.Hepatogastroenterology 2000: 47: 998–1001.

46. Mori M, Staniunas RJ, Batnard GF, Jessup JM, Steele GD Jr,Chen LB. The significance of carbonic anhydrase in humancolorectal cancer. Gastroenterology 1993: 105: 820–826.

Transcriptome maps as diagnostic tools

227