Gene Expression Profiling of Malignant Mesotheliomaphysiology, and treatment of malignant mesothelioma remain unsolved. Making a diagnosis of mesothelioma from pleural fluid is notoriously

Gene Expression Profiling of Malignant Mesothelioma1

Sunil Singhal,2 Rainer Wiewrodt,2

Liliana D. Malden, Kunjlata M. Amin,Kimberly Matzie, Joseph Friedberg,John C. Kucharczuk, Leslie A. Litzky,Steven W. Johnson, Larry R. Kaiser, andSteven M. Albelda3

Section of Thoracic Surgery, Department of Surgery [S. S., K. M. A.,K. M., J. F., J. C. K., L. R. K.], and Departments of PulmonaryMedicine [R. W., L. D. M., S. M. A.], Pharmacology [S. W. J.], andPathology and Laboratory Medicine [L. A. L.], University ofPennsylvania School of Medicine, Philadelphia, Pennsylvania 19104

ABSTRACTPurpose: Malignant mesothelioma is a uniformly fatal

cancer of the pleural and peritoneal spaces. Several chal-lenging clinical problems include poor understanding of thepathophysiology, inaccurate diagnosis from tissue samples,and unsuccessful treatment strategies. The purpose of thisstudy was to use microarray analysis to identify specific geneexpression changes in mesothelioma compared with normalmesothelium.

Experimental Design: We performed gene expressionanalysis on mesothelioma tissue specimens from 16 patientsand compared these to 4 control pleural tissue samples usingcDNA microarray filters with 4132 clones. Multiple normal-ization and analysis approaches were used. Quantitativereverse transcription-PCR and immunohistochemistry wereused to validate results.

Results: Genes (166) were significantly up-regulated,and 26 were down-regulated. Validation of 18 genes usingreal-time PCR confirmed array predictions in every case.Analysis revealed activation of several key pathways includ-ing genes involved in glucose metabolism, mRNA trans-lation, and cytoskeletal remodeling. Expression profilingidentified processes likely responsible for 18-fluoro-2-deoxy-glucose uptake and tumor localization by positron emissiontomography, and a role for hypoxia-inducible factor-1 wassuggested. Potentially important up-regulated genes in-cluded gp96, lung resistance-related protein, galectin-3 bind-

ing protein, the Mr 67,000 laminin receptor (on tumorvessels), and voltage-dependent anion channels. Prospectivetesting using reverse transcription-PCR confirmed up-regulation of these novel markers.

Conclusions: Expression profiling revealed marked up-regulation of energy, protein translation, and cytoskeletalremodeling pathways in mesothelioma. Additional genesthat could be important in our understanding of the patho-genesis of mesothelioma, aiding in diagnosis, or improvingtargets for therapy were also identified.

INTRODUCTIONMalignant pleural mesothelioma is a uniformly fatal cancer

of the pleura, affecting 2,500 Americans annually. The inci-dence is rising sharply in Europe where 5,000–10,000 cases peryear are expected (1). No intervention has proven to be curative,despite aggressive chemotherapeutic regimens and prolongedradiotherapy. The median survival in most cases is only 12–18months after diagnosis.

Several clinical problems regarding the diagnosis, patho-physiology, and treatment of malignant mesothelioma remainunsolved. Making a diagnosis of mesothelioma from pleuralfluid is notoriously difficult and often requires a thoracoscopicor open pleural biopsy. Relatively few mesothelioma-specificmarkers have been identified often necessitating a “diagnosis byexclusion.” Although it is well established that asbestos expo-sure is a major risk factor in the development of mesothelioma(2), the molecular steps in carcinogenesis remain unknown. Arecent suggestion that SV40 virus may be a cocarcinogen raisesadditional pathophysiologic questions. Finally, given the poorresponse to current therapies, a better understanding of themolecular pathways active in this disease could potentiallyprovide new targets for therapy.

One potentially useful approach to solve these issues wouldbe to identify specific gene expression changes in cancerousmesothelial cells. Progress in this area has been limited and, todate, has been performed mostly in cell lines (3, 4). Recentreports from the Brigham and Women’s Hospital using actualtumor tissues identified 20 genes that were overexpressed inmesothelioma tissues (5, 6).

Expression profiling offers the opportunity to analyzegene expression changes in mesothelioma in an unbiasedmanner. Therefore, we performed microarray analysis ofmesothelioma tissues from 16 patients and compared theseresults with 4 control pleural tissue samples. To our knowl-edge, this approach has not yet been used to describe me-sothelioma tissues in a comprehensive manner. Our first goalwas to characterize major pathways altered in malignantmesothelioma. Our second goal was to identify genes thatcould be involved in the pathophysiology of mesothelioma orthat might serve as diagnostic markers to improve the accu-racy of tumor classification.

Received 11/5/02; revised 3/18/03; accepted 3/19/03.The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely toindicate this fact.1 Supported by National Cancer Institute Grant NCI PO1 66726,Mildred Sheel-Stiftung fur Krebsforschung (#98-02288), and a CancerMolecular Pathology Training Grant, NIH R25-CA87812.2 These authors contributed equally to this work.3 To whom requests for reprints should be addressed, at Department ofPulmonary Medicine, 8th Floor, BRB II/III 421 Curie Boulevard, Phil-adelphia, PA 19104. Phone: (215) 573-9933; Fax: (215) 573-4469;E-mail: [email protected].

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MATERIALS AND METHODSTissue Acquisition. Patients in this study presented to

the Hospital of the University of Pennsylvania between October1999 and December 2000, and underwent a standard thoracot-omy for diagnostic or therapeutic reasons. Mesothelioma tissuewas obtained from those patients with a confirmed pathologicaldiagnosis and who had not received prior therapy. Intraoperativemalignant mesothelial samples and nodules were dissected fromassociated fat and connective tissue, but no microdissection wasperformed. H&E staining was performed to verify the presenceof tumor cells. Samples were immediately frozen in liquidnitrogen before RNA analysis. Control pleural tissue was ob-tained from patients undergoing resection for lung cancer at anarea distant from the tumor site. Tissue acquisition was ap-proved by the Institutional Review Board at the University ofPennsylvania.

RNA Preparation. Three-hundred mg of each tumorspecimen was subjected to RNA extraction using standard tech-niques (7). Briefly, tissues were homogenized in guanidiniumisothiocyanate buffer at room temperature, extracted with phe-nol-chloroform-isoamyl alcohol, and precipitated with isopro-panol in the presence of sodium acetate. After initial recoveryand resuspension of the RNA pellet, a DNase step was per-formed for 3 h at 37°C using 80 �g of RNase inhibitor (Roche,Alameda, CA), 60 �g of RNAsin (Promega, Madison, WI), and10 units of RNase free DNase (Roche) in 1 M Tris (pH 7.4)buffer solution. Total RNA was then re-extracted, precipitated,and dissolved in water.

Microarray Hybridization. Hybridization was per-formed on GF211 GeneFilters Microarrays (Research Genetics,Inc., Carlsbad, CA) that contain 4132 named human genes basedon the protocol supplied by the manufacturer. Gene names arelisted according to the UniGene human-sequence collection(available at UniGene Web Site).4

Hybridizations, washes, and scanning were performed asdescribed previously (8). Briefly, the gene filter membraneswere prewetted in 0.5% SDS and prehybridized for 2 h at 42°Cin 5 ml of Microhyb solution (Research Genetics, Inc.) contain-ing 1.0 �g/ml Cot1 DNA (Life Technologies, Inc., Gaithers-burg, MD) and 1.0 �g/ml poly-deoxyadenylate (ResearchGenetics).

Ten �g of DNase-treated total RNA and 2 �g oligodeoxy-thymidylic acid (Promega) were incubated at 70°C for 10 min,and rapidly chilled on ice. Using SuperScript II RT (Life Tech-nologies, Inc.), RNA was next reverse transcribed according tothe manufacturer’s instructions but in the presence of radio-active [33P]dCTP. Labeled probes were purified using chroma-tography columns to remove any unincorporated nucleotides.

Labeled double-stranded cDNA was added to the prehy-bridization buffer and the filters hybridized for 18 h at 42°C.Posthybridization washes were performed twice at 50°C in 1�SSC (2� SSC, 15 mM sodium citrate, and 150 mM NaCl) and1% SDS for 20 min, and once at room temp in 0.5� SSC and1% SDS for 15 min. After drying, the membranes were placedin cassettes and scanned using the phosphorimager (Hewlett

Packard). After each hybridization the filters were stripped byboiling in 0.25% SDS solution and reused for up to three times.

Data Analysis. The images resulting from the phosphor-imager were imported directly into the image analysis “Path-ways” software (Research Genetics, Inc.). The background ra-diointensity for each array was simultaneously recorded. A fulldescription of the data analysis is beyond the scope of this paper,however, is available at our website.5 Software used for dataanalysis included Microsoft Access, Microsoft Excel, VisualPerl, and Visual Basic. Briefly, our initial step was to removethe background intensity from every hybridization experiment.Normalization was performed in three separate ways. Globalnormalization was used to calculate gene expression levelsbased on the average total intensity on each filter. A secondmethod normalized using the average intensity of the 250 geneswith the least amount of variability across hybridization exper-iments. The third method used genes that had the most similarexpression levels in each array. In this approach, a Gaussiancurve was fitted to a plot of number of genes versus intensitylevel for each array. The genes that fell within one SD of themean in all of the arrays were chosen. In our data set, there were200 genes that fit these criteria.

Three gene prediction techniques to identify signifi-cantly changed gene expression levels were used from thedata from each normalization process: Student’ s t test (9),significant analysis of microarrays6 (10), and patterns of geneexpression7 (11).

Using combinations of three normalization tools and threegene prediction techniques, this process generated nine separatelists of genes with differential expression. To be considered a“significantly changed gene,” a gene had to satisfy the followingcriteria: (a) the gene must appear on at least four separate listsof significant genes; (b) the P using the Student’s t test after atleast one normalization method must be �0.001; and (c) a genemust have at least a 2-fold change in gene expression levelbetween sample groups.

Genes were categorized using the vocabulary defined bythe GO8 Consortium.9 The complete vocabulary is structuredinto three broad categories, reflecting the biological roles ofgenes: (a) molecular function: tasks performed by individualgene products; (b) biological process: broad biological goalsaccomplished by ordered assemblies of molecular functions;and (c) cellular component: subcellular structures, locations, andmacromolecular complexes.

Significant genes were grouped under the appropriate GO

4 Internet address: http://www.ncbi.nlm.nih.gov/Unigene.

5 Internet address: http://www.uphs.upenn.edu/lungctr/academic_programs/pulmonary/research/labs/albelda.6 Internet address: http://www-stat.stanford.edu/�tibs/SAM.7 Internet address: http://www.cbil.upenn.edu/PaGE.8 The abbreviations used are: GO, Gene Ontology; RTQ-PCR, real-timequantitative PCR; GAPDH, glyceraldehyde-3-phosphate dehydrogen-ase; LDH, lactate dehydrogenase; ChoRE, carbohydrate response ele-ment; HIF, hypoxia-inducible factor; VEGF, vascular endothelialgrowth factor; PET, positron emission tomography; 18FDG, fluorine-18fluoro-2-deoxy-D-glucose; eIF, eukaryotic translation initiation factor;hsp, heat shock protein; LRP, lung resistance-related protein; VDAC,voltage-dependent ion channel; IAP, inhibitor of apoptosis.9 Internet address: http://www.geneontology.org.

3081Clinical Cancer Research



function and then ranked in groups by the most up-regulatedfunctions (see Table 2). In addition, we examined the list ofsignificant genes and pursued verification studies on a subsetthat might be useful diagnostically or those that had potentiallyinteresting functions.

Several genes were noted to be consistent with infiltrationwith WBCs in our tumor samples. Immunostaining with ananti-CD45 antibody (see below) confirmed the presence ofinfiltrating leukocytes in all of the tumors and pleural samplesthat were examined. Accordingly, a “virtual microdissection”was performed to remove genes that we suspected to be presentbecause of contaminating WBCs. Genes with the following GOvocabulary words were removed: class II MHC antigen, anti-microbial humoral response, antipathogen response, defenseresponse, response to bacteria, immune response, and responseto pathogenic bacteria.

Real-Time Semiquantitative PCR Confirmation ofSelected Genes. To validate a subset of genes with significantchanges, real-time, reverse transcription-PCR was performed.Four pools of RNA (300 �g each) were created. The first poolconsisted of RNA (60 �g each) from 5 normal pleural tissues.The second and third pools consisted of RNA from 5 patients(60 �g each) each, randomly selected, who had been analyzedpreviously on microarrays. The fourth pool consisted of RNAfrom 5 patients (60 �g each) who had not been studied on themicroarrays and was designated our “prospective” pool.

Three-hundred �g of RNA from each pool of total RNAwas reverse-transcribed using 0.5 �g oligodeoxythymidylic acid(Promega), 10 mM deoxynucleoside triphosphates (Clontech,Palo Alto, CA), 1 unit of Powerscript Reverse Transcriptase in5� First-Strand Buffer and 100 mM DTT (Clontech) for 80 minat 42°C. Gene sequences available at the National Center forBiotechnology Information GenBank and Unigene databaseswere selected to design primers. Optimum primer sequenceswere selected after verification for gene-specific complementa-tion using the National Center for Biotechnology InformationBlast program.10 Semiquantitative analysis of gene expressionwas performed using a Cepheid Smart Cycler using themanufacturer’s protocol for the Sybr-Green kit supplied byRoche (Cepheid, Sunnyvale, CA). cDNA concentrations fromeach pool were normalized using two control genes that showedno change in expression on the arrays: ubiquitin and cytochromep450 reductase. Standard curves were generated by preparingserial dilutions, and the relative level of expression of each ofthe verified genes was determined.

Immunoperoxidase Staining. Immunostaining was per-formed on a subset of genes. Five �m, frozen tissue sectionswere mounted on slides, permeabilized with acetone, and fixedin 5% blocking serum (PBS/BSA/Azide) for 20 min. The slideswere incubated with 5–10 �g/ml of the following antibodies:cytokeratin (5/18; NovoCastra, Newcastle upon Tyne, UnitedKingdom), anti-Grp94 (StressGen, Victoria, Canada), the Mr

67,000 laminin receptor (LabVision, Fremont, CA), and CD-45(Sigma, St. Louis, MO). Visualization was achieved by the use

of Vectastain kit or by Alkaline Phosphatase kit (Vector Labo-ratories, Burlingame, CA) using the manufacturer’s protocols.

RESULTS and DISCUSSIONClinical Characteristics of Patients with MalignantMesothelioma

Sixteen patients with malignant mesothelioma underwentresection and debulking for their pleural disease. Their clinicalcharacteristics are presented in Table 1. The average age at timeof operation was 63.0 years, with 1 female. The average smok-ing history was 33.3 pack-years. Fifteen of the patients hadsome history of asbestos exposure. Thirteen of the patientsunderwent a pleurectomy. Pathology was either epithelioid(75%), sarcomatous (6%), or biphasic (19%).

Summary of Hybridization Experiments and Selection ofSignificant Genes

Tumors from the 16 cancerous patients was compared with5 samples of normal pleura. No microdissection was performed.The normal pleura was a thin layer of tissue “stripped” from thechest wall that consisted of mesothelial cells and a small amountof adherent connective tissue.

RNA from each tissue was extracted, reverse transcribed,and labeled with [33P]dCTP, and arrayed on “unichannel” nylonmicroarrays containing 4132 genes. Our entire data set is avail-able on the web.5

In validation studies, the average radiointensity of the samegene varied by 8%. Because 1 of the 5 normal pleural arraysdemonstrated a variability of �15%, we removed this samplefrom additional data analysis. Another test of experimentalvalidity was performed by taking the tumor from a single patientand repeating hybridizations on 3 separate days on three newarrays from the same manufacturing lot. Regression analysisdemonstrated 12% variability of the same tumor run at threedifferent times.

Of the 4132 genes analyzed, 166 (4.0%) genes were clas-sified as “significantly” up-regulated and 26 (0.6%) genes10 Internet address: http://www.ncbi.nlm.nih.gov/blast.

Table 1 Clinical characteristics of patients undergoing thoracotomyfor debulking of malignant mesothelioma

Patient Age Sex Pathology from operative specimen

1 75 M Epithelioid subtype2 66 F Epithelioid subtype with pseudoglandular and

papillary features3 72 M Epithelioid subtype4 39 M Epithelioid subtype5 69 M Epithelioid subtype6 57 M Epithelioid subtype with spindle cell growth7 55 M Epithelioid subtype with papillary features8 68 M Sarcomatous/biphasic subtype9 62 M Epithelioid subtype

10 64 M Epithelioid subtype11 51 M Epithelioid subtype12 77 M Spindle cell subtype with significant

lymphocytic infiltrate13 61 M Biphasic subtype14 63 M Biphasic subtype15 75 M Epithelioid subtype16 54 M Biphasic subtype

3082 Expression Profiling of Mesothelioma



“significantly” down-regulated based on the criteria described inthe “Materials and Methods.” The degree of gene expressionup-regulation varied from 2-fold to 11-fold, and the degree ofdown-regulation ranged from 2-fold to 5-fold decrease in geneexpression. A complete list of these genes is available at ourwebsite.

We used the GO Consortium vocabulary to categorizesignificantly up-regulated genes by important molecular func-tions (Table 2). The categories with the most number of changedgenes included those involved with cytoskeletal reorganization(GO categories: extracellular matrix genes, epidermal develop-ment, cell shape and cell size control, and cell migration andmotility), protein synthesis (GO categories: protein synthesis,RNA processing and modification, and translation factors), andmetabolic pathways (GO categories: oxioreductase and energygeneration). We additionally analyzed a group of significantlyup-regulated genes that might have potential use as surfacereceptors for diagnostic markers, as well as other genes withpossible therapeutic and prognostic implications. Table 3 liststhe significantly down-regulated genes. We did not identify anypathways with consistent changes. Of interest was the down-regulation of the retinoblastoma gene, a finding consistent withcell cycle dysregulation.

Hierarchical cluster analysis was also used to interpretpatterns of gene expression (12). We used Cluster and Tree-View11 to create the clustering and dendograms to visualize allof the genes in selected gene categories (Fig. 1). The variousclusters examined involved in cellular metabolism were mRNAtranslation genes, oncogenes, cytoskeletal reorganization genes,and genes responsible for apoptosis. As shown in Fig. 1 (alsoavailable at our website),5 mesotheliomas demonstrated consist-ent up-regulation of gene expression in three major pathways:glucose metabolism (Fig. 1A), mRNA translation (Fig. 1B), andcytoskeletal reorganization (Fig. 1D). Interestingly, a number ofother pathways that might have been expected to be up-regu-lated in mesothelioma [genes classified in oncogenesis (Fig. 1C)and in apoptosis (Fig. 1E)] showed no consistent changes.

Validation of Selected Gene ExpressionsRTQ-PCR was used to confirm expression levels of 18

genes by comparing three pools of cDNA derived from RNA ofmesothelioma patients to one pool of RNA from normal pleuralsamples. (Fig. 2). Two of the mesothelioma pools (5 patients ineach pool), combined in Fig. 2, contained RNA obtained fromthe same patients that had been used on the original array. Thethird mesothelioma pool contained RNA from 5 new patientsnot used to generate the array data. Two genes (ubiquitin andcytochrome p450) that had equivalent expression on the arrayswere used to normalize the RTQ-PCR data across samples,because the expression of genes that have traditionally consid-ered to be housekeeping genes and used for normalization, i.e.,�-actin or GAPDH, were actually highly elevated in tumorpatients. As shown in Fig. 2, there was a very high level ofagreement between the array and the real-time PCR data sup-porting the validity of the array data.

Analysis of Specific Pathways and GenesGlucose Metabolism and the Warburg Effect. One of

the most striking changes we observed in the mesotheliomaswas up-regulation of many genes in the pathway involvingglycolysis and the Krebs cycle (Fig. 1A; Fig. 3): Seven of the 10enzymes present were up-regulated with an average fold in-crease of 2.8. Especially prominent increases were seen inGAPDH (6.4-fold increase; P � 0.00077), LDH (5.5-fold in-crease; P � 0.00001), and phosphoglycerate kinase 1 (3.9-foldincrease; P � 0.0011; Table 2; Fig. 3). These changes wereconfirmed by RTQ-PCR (Fig. 2).

These findings are consistent with observations that cancercells maintain high aerobic glycolytic rates, and produce highlevels of lactate and pyruvate despite the presence of oxygen, aphenomenon known historically as the Warburg effect (13, 14).Preferential reliance of glycolysis is correlated with diseaseprogression in several cancers such as breast cancer, non-smallcell lung cancer, uterine cancer, and hematological malignancies(14, 15), and the activities of several of the glycolytic enzymessuch as LDH, hemokinase, pyruvate kinase, and phosphofruc-tokinase have been reported to be significantly increased incancer cells.

It is interesting to consider the mechanisms of this meta-bolic activation. A ChoRE, a 5�-CACGTG-3� motif, controlstranscription of several of these metabolic enzymes includinghexokinase, GAPDH, pyruvate kinase, enolase, and LDH. Thebinding site for the ChoRE contains an E-box sequence,CACGGG; however, the transcription factors binding this sitestill remain poorly understood (16–18). Although glucose is amajor regulator of the ChoRE promoter, two other potentialparticipants include HIF-1 and c-myc (19–21). DNA sequenceand functional analyses have revealed that the ChoRE promoterhas an active HIF-1 and c-myc binding site, and that it stimu-lates expression of ChoRE-dependent glycolytic enzymes.

To study a possible relationship between glycolytic en-zyme expression with HIF-1 and c-myc expression, we usedRTQ-PCR to correlate expression of these two transcriptionfactors with two of the up-regulated glycolytic enzymes thathave the ChoRE promoter: GAPDH and LDH. We made cDNAfrom 4 patients who had relatively low glycolytic enzyme ex-pression levels and 4 patients who had high glycolytic enzymeexpression levels on the microarray hybridization experiments.We then used RTQ-PCR to measured their GAPDH and LDHlevels, as well as the gene expression levels of HIF-1� andc-myc. There was a very strong and significant (P � 0.05)correlation between HIF-1 and LDH (r2 0.98, Spearmancoefficient) and HIF-1 and GAPDH (r2 0.85, Spearmancoefficient). The correlation with c-myc was much lower pro-nounced (GAPDH r2 0.59 and LDH r2 0.41).

Although these data are only correlative, and do not provecause and effect, the significant association between HIF-1levels and up-regulation of glycolytic enzymes in mesotheliomais intriguing. Hypoxia is a usual feature of many solid cancers,and has been linked to malignant transformation, metastasis, andtreatment resistance (22, 23). Thus, the up-regulation of HIF-1in tumors is common where it functions as a key transcriptionfactor that potentially regulates 9 of the 11 glycolytic enzymes11 Internet address: http://rana.lbl.gov.




Table 2 Selected significant up-regulated genes with diagnostic therapeutic, and prognostic implicationsGenes are grouped by the vocabulary from the GO Consortium

GenBank accession no. Gene name Fold change P

Cytoskeletal reorganizationH15446 Annexin VII (synexin) 12.10 0.00349AA668178 Karyopherin �3 11.69 0.00092AA235002 Annexin VIII 11.68 0.00759AA485668 Integrin �4 10.58 0.00805AA598517 Keratin 8 9.35 0.06881AA664179 Keratin 18 8.90 0.05876N67487 Microfibrillar-associated protein 2 8.60 0.00667H90899 Desmoplakin I & II 7.95 0.00134T98612 Collagen III, �1 6.08 0.00410AA425450 Neuromedin B (Integrin �E) 5.84 0.00394AA451895 Annexin V (endonexin II) 5.75 0.00241AA219045 Microtubular associated protein 1b 5.75 0.00957AA419015 Annexin IV 5.72 0.00624AA490172 Collagen I, �2 5.55 0.00481AA160507 Keratin 5 5.32 0.02919AA487427 Rho GDP dissociation inhibitor 5.18 0.01831AA464982 Annexin XI 5.02 0.00537AA463257 Integrin �2 4.78 0.00194H99676 Collagen IV, �1 4.48 0.00311R40850 �-centractin 4.42 0.00047T60117 �-spectrin 4.25 0.00177AA888148 Tubulin �2 3.97 0.00155AA634103 Thymosin �4 3.69 0.00028AA677534 Laminin 3.62 0.00326AA634006 �2 actin 3.43 0.00304R76314 rho G 3.40 0.00316AA188179 Arp2/3 protein complex subunit p41-Arc (ARC41) 2.66 0.00013AA485959 Keratin 7 2.66 0.04224AA464748 Collagen VI, �2 2.58 0.01141H94892 ral-A 2.36 0.00015AA626787 rac 2.32 0.00012AA486321 Vimentin 2.31 0.01496R44290 �-actin 2.28 0.00058

Protein synthesisAA676471 Eukaryotic translation initiation factor (eIF3) 8.65 0.00175R43973 Elongation factor 1 6.02 0.00142H09590 Eukaryotic initiation factor (EIF) 4AI 5.82 0.00466R54097 Translational initiation factor 2� (eIF-2�) 4.40 0.00013AA669674 Eukaryotic Translation initiation factor (EIF) 3 3.30 0.00026R43766 Eukaryotic translation elongation factor 2 3.09 0.00087R37276 Eukaryotic initiation factor (eIF) 4G1 2.43 0.00005

Metabolic pathwaysAA676466 Argininosuccinate synthetase 9.06 0.03367R15814 Malate dehydrogenase 7.56 0.00152AA664284 Ubiquinol-cytochrome c reductase 6.45 0.00013H16958 Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) 6.41 0.00077H05914 Lactate dehydrogenase-A (LDH-A) 5.53 0.00001AA447774 Cytochrome c1 5.40 0.00282N93053 Cytochrome c oxidase 4.99 0.00089R12802 Ubiquinol-cytochrome c reductase core protein II 4.69 0.00255AA455235 Aldehyde dehydrogenase 6 3.98 0.00990AA599187 Phosphoglycerate kinase 1 3.92 0.00110AA775241 Aldolase A 2.61 0.00082

Genes with potential therapeutic and prognostic implicationsAA598676 Reticulocalbin 2 12.74 0.00041AA629897 Laminin receptor (67kD) 11.56 0.00181AA458861 Death associated protein 10.94 0.00000AA485353 Galectin-3 binding protein 10.38 0.00026AA156461 Pituitary tumor-transforming (PTT) interacting protein 9.46 0.00004H99170 Calreticulin (precursor) 6.69 0.00162T66814 Voltage-dependent anion channel (VDAC) 2 6.56 0.00046AA044059 Voltage-dependent anion channel (VDAC) 1 5.99 0.00025AA158991 Lung resistance related protein (major vault protein) 5.54 0.00001AA394136 PCTAIRE 3 5.42 0.00095W56266 Mitogen-activated protein kinase kinase kinase 8 (MAPKKK 8) 5.28 0.01120




(21, 24, 25) in addition to other genes such as VEGF anderythropoietin. It is thought that HIF-1 then orchestrates theadaptation of cancer cells to hypoxia by inducing glycolysis,angiogenesis, and erythropoiesis.

One interesting clinical implication of these observations isthat the marked up-regulation of glycolysis-related genes mayrepresent the molecular explanation for the increased metabolicactivity seen in mesotheliomas when imaged using PET scansusing radiolabeled 18FDG, a compound that correlates directlywith glucose metabolism (26). 18FDG is transported into cellsand phosphorylated; however, 18FDG-phosphate is an unsuit-able substrate for the next enzyme (phosphoglucose isomerase)in the glycolytic pathway. Increased hexokinase activity, to-gether with increased glucose transporter expression in tumorcells compared with that in surrounding tissue, results in selec-tive 18FDG accumulation in the tumor (27). Because 18FDG islabeled with the positron-emitting nuclide 18F, an image of thetumor can be seen using PET. Up-regulation of glycolytic en-zymes could potentially be useful therapeutically if, or when,new treatments are developed that target enhanced glucosemetabolism in tumors.

Initiation of mRNA Translation. A second pathway inwhich many genes were markedly up-regulated in mesotheliomawas the mRNA translation pathway (Fig. 1B; Fig. 4). Proteinsynthesis occurs on the ribosome; however, the ribosome doesnot bind to mRNA directly, but must be recruited to mRNA bythe concerted action of many eIFs (28, 29). Among our topup-regulated genes, 4 were ribosomal proteins and 6 were elon-gation factors (Table 2). As shown in Fig. 4, significant over-expression of genes was observed in almost all parts of thetranslation initiation pathway including eIF1, eIF4A1, eIF3,eIF2�, eIF3, and eIF4G1.

This up-regulation may be highly significant because thereis both experimental and observational evidence linking over-expression of eIFs to oncogenesis. Experimentally, overexpres-sion of eIF-4E (30) or eIF4G (31) in NIH 3T3 cells causestumorigenic transformation. Cells overexpressing eIF-4Eshowed a 130-fold increase in VEGF protein production (32).Conversely, down-regulation of eIF4E levels in transformedfibroblasts (33) or human head and neck squamous carcinoma

cell lines (34) using antisense technology leads to a loss oftumorigenicity. In observational studies, eIF4E was shown to beoverexpressed in a broad range of rat tumors and cells linescompared with normal tissues (35), and in human breast andprostate carcinomas, as well as non-Hodgkin’s lymphomas (36,37). Several additional translational initiation factors have beendiscovered subsequently to be up-regulated in human tumors,including: eIF2G in 30% squamous lung carcinomas (38), eIF-4AI in human melanoma cells, eIF-2B in human breast cancercell lines, and eIF-3 in breast cancer (39), squamous cell esoph-ageal carcinomas, and prostate cancer (40).

Cytoskeletal Reorganization. Many genes in the cy-toskeletal reorganization pathway are also up-regulated (Table 2;Fig. 1D). Given the epithelial differentiation of mesothelioma cellscompared with normal mesothelial cells, it was not surprising toobserve marked up-regulation of a number of cytokeratin genes:cytokeratin 8 (9.3-fold increase; P 0.06), cytokeratin 18 (8.9-foldincrease; P 0.058), cytokeratin 5 (5.32-fold increase; P 0.029),and cytokeratin 7 (2.66-fold increase; P 0.042). The relativelyhigh Ps suggest wide variation among tumors. Keratins constitutethe major intermediate filaments in several simple epithelial tissuessuch as liver, intestine, and pancreas, and their presence has beenused extensively in the diagnosis of tumors from epithelial andnonepithelial origin. For example, keratin 8 and keratin 18 arecommonly associated with both well- and poorly differentiatedcarcinoma cells (41). Staining of the mesotheliomas with an anti-cytokeratin 18 antibody showed very strong staining of tumor cellswith no staining of normal mesothelium (Fig. 5). The expression ofthe intermediate filament vimentin was also increased 2.3-fold(P 0.01).

Given the known tendency for malignant mesotheliomacells to generate abundant stromal tissue (42), it was also notsurprising to see laminin (3.62-fold increase; P 0.003) andseveral collagen genes among the most strongly up-regulated:collagen III, �1 (6-fold increase; P 0.004), collagen I, �2(5.55-fold increase; P 0.0048), collagen IV, �1 (4.48-foldincrease; P 0.003), and collagen VI, �2 (2.58-fold increase;P 0.011). A number of these increases were confirmed withsemiquantitative PCR (Fig. 2).

Actin filaments are key molecules in the regulation of cell

Table 2 Continued

GenBank accession no. Gene name Fold change P

W73874 Cathepsin L 5.27 0.00605AA599127 Superoxide dismutase I (Cu/Zn) 5.21 0.00060AA293571 Apoptosis (APO-1) antigen 1 4.74 0.02522AA598758 Adenotin (Tumor rejection antigen gp96) 4.67 0.00040H12044 p53 inducible protein 4.45 0.00218H01340 Mitogen-activated protein kinase kinase kinase 10 (MAPKKK 10) 4.08 0.00506AA039231 Calmodulin-related protein (NB-1) 3.78 0.00260H95960 SPARC/osteonectin 3.65 0.03699AA442991 Prothymosin � 3.59 0.00031AA444051 S100 calcium-binding protein A10 (Calpactin 1, p11) 3.51 0.01050AA460291 Bcl-2 binding component 6 (bbc6) 3.31 0.00144AA464731 S100 calcium-binding protein A11 (Calgizzarin) 2.74 0.01052R11526 Parathymosin 2.34 0.00915

a In order to be considered “significantly changed,” a gene had to satisfy the following criteria: (a) the gene must appear on at least four separatelists of significant genes; (b) the P using the Student’s t-test after at least one normalization method must be �0.001; and (c) a gene must have atleast a 2-fold increase in gene expression level between sample groups.




adhesion, cell spreading, and cell configuration, and are criticalfor the regulation of various cell functions, including prolifera-tion (43). Many studies have demonstrated genes that regulatethe actin-based cytoskeleton are important in the oncogenicprocess and have been reported to correlate with prognosis in

patients with various cancers (44). For example, small GTPasesof the rho family (such as rho, rac, cdc42, ralA, ral-GDS, andral-BP1) induce particular surface protrusions generated by ac-tin-remodeling reactions that change cell shapes, and influencecell adhesion and locomotion (43, 45). In addition, the rho

Table 3 Significantly down-regulated genes in malignant mesotheliomaThere were no distinct patterns of changes in gene expression.

GenBankaccession no. Gene name

Down-regulated

fold change P Protein function

R43753 Sialyltransferase 8E (�-2,8-polysialytransferase)

6.03 �0.0001 A member of glycosyltransferase family 29, which maybe involved in the synthesis of gangliosides GD1c,GT1a, GQ1b, and GT3 from GD1a, GT1b, GM1b,and GD3, respectively.

AA644211 Prostaglandin-endoperoxide synthase 2 5.07 0.0009 Prostaglandin-endoperoxide synthase (PTGS), also knownas cyclooxygenase, is the key enzyme in prostaglandinbiosynthesis that it is responsible for the prostanoidbiosynthesis involved in inflammation and mitogenesis.The expression of this gene is deregulated in epithelialtumors.

AA128328 Retinoblastoma binding protein 1 4.83 0.0004 It binds directly to retinoblastoma protein (pRB) whichregulates cell proliferation.

N25141 Cullin 3 4.77 0.0003 Involved in cell cycle transition from S phase to G2.AA156571 Alanyl-tRNA synthetase 4.29 0.0001 Catalyzes the attachment of alanyl to tRNA.AA187349 Ferredoxin 1 4.28 0.0004 A small iron-sulfur protein that transfers electrons from

NADPH through ferredoxin reductase to a terminalcytochrome P450.

T71272 Decoy receptor 1 (DcR1) 4.09 0.0002 Tumor suppressor gene that is a potent inhibitor of e2F-mediated transactivation.

R52085 Growth differentiation factor 10 4.01 0.0004 Regulator of cell growth and differentiation in bothembryonic and adult tissues.

H17398 Brain-specific angiogenesis inhibitor 3 4.00 0.0006 May play a role in angiogenesis.T68892 Secreted frizzled-related protein 1 3.96 0.0005 A member of the SFRP family that act as soluble

modulators of Wnt signaling.AA676797 Cyclin F 3.94 0.0002 Important regulators of cell cycle transitions through their

ability to bind and activate cyclin-dependent proteinkinases. Cyclin F is the largest of the cyclins yetwhose role is least understood.

AA010352 EST 3.90 0.0010 Unknown.AA283693 Osteoclast stimulating factor 1 3.62 0.0001 Induces bone resorption, acting probably through a

signaling cascade which results in the secretion offactor(s) enhancing osteoclast formation and activity.

N26665 EST 3.58 0.0008 Unknown.H15112 Uracil-DNA glycosylase 3.54 0.0009 Prevent mutagenesis by eliminating uracil from DNA

molecules by cleaving the N-glycosylic bond andinitiating the base-excision repair (BER) pathway.

H62029 Dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 3

3.47 0.0008 Belongs to the DYRK family of dual-specificity proteinkinases that catalyze autophosphorylation on serine/threonine and tyrosine residues.

AA448866 EST 3.37 0.0007 Unknown.AA621019 Par-6 partitioning defective 6 homolog �

(C. elegans)3.34 0.0003 Involved in the establishment of cell polarity in epithelial

cells.H11603 Adaptor-related protein complex 3, beta 2

subunit3.14 0.0007 Facilitates the budding of vesicles from the golgi

membrane and may be directly involved in traffickingto lysosomes.

AA521083 Protein phosphatase 6 2.58 0.0007 May function in cell cycle regulation.AA448184 Ubiquinol-cytochrome c reductase, Rieske

iron-sulfur polypeptide 12.52 0.0010 Component of the ubiquinol-cytochrome c reductase

complex.AA704492 Transducin-like enhancer of split 4 2.49 0.0004 Nuclear effector molecule.AA284528 Serine protease 2 (trypsin 2) 2.44 0.0005 A trypsinogen, which is a member of the trypsin family of

serine proteases.AA461304 Beta glucosidase 2 2.37 0.0006 Unknown.AA460830 Polymerase (RNA) II (DNA directed)

polypeptide J2.18 0.0005 A subunit of RNA polymerase II.

AA045192 Retinoblastoma 1 2.01 0.0003 Tumor suppressor gene whose loss results in deregulatedcell proliferation and apoptosis.




Fig. 1 Two-dimensional hierarchical clustering analysis of selected gene ontologies in 4 normal pleural samples and 16 malignant mesotheliomas.Expression levels are relative to normal pleural tissue, and color coded with red and black corresponding to an increase and no change in geneexpression, respectively. Full visualization of hierarchical clustering is available.5




Fig. 1 Continued.




Fig. 1 Continued.




Fig. 1 Continued.




family of genes have roles in cytoskeletal transformation, reg-ulation of expression of growth-promoting genes, and progres-sion of cell cycles through the G1 phase of the cell cycle (43,46). Rho family proteins induce tumorigenic transformation ofrodent fibroblasts (47). Our experiments demonstrate increasesin expression of rho family genes including rho G (3.4-foldincrease; P 0.003), ral-A (2.4-fold increase; P 0.00015),and rac (2.3-fold increase; P 0.00012).

We also observed up-regulation of thymosin �-4 (3.7-foldchange; P 0.00028), a protein that binds monomeric actin, acomponent of the cytoskeleton, and may act as an actin buffer,preventing spontaneous polymerization of actin monomers intofilaments supplying a pool of actin monomer when the cellneeds filament (48). It is not clear how increased expression ofthymosin �-4 might promote metastasis, but it is likely relatedto the need for cells to migrate (49). Thymosin �-4 has alreadybeen demonstrated to be highly up-regulated in several tumorsincluding renal, bladder, prostate, colorectal, and thyroid neo-plasms (50, 51).

Identification of Other Potentially Important GenesIn addition to those genes that were part of defined acti-

vated pathways, our analysis identified a number of other sig-nificantly up-regulated genes that might be useful in understand-ing the pathogenesis of mesothelioma and/or assist in diagnosis(Table 2). A subset of these was chosen for additional analysis.Up-regulation of these genes was verified by real-time PCR(Fig. 2) and/or immunohistochemistry (Fig. 5).

gp96 (Adenotin). Gp96 (also known as adenotin, endo-plasmin, tumor rejection antigen 1, gp100, grp 94, or stress-inducible tumor rejection antigen gp96) is a cytoplasmic andcell surface-expressed member of the cellular hsp family, mostclosely related to hsp90 (52). On the array, gp96 was 4.7 foldup-regulated in mesothelioma tissue compared with normal

pleura (P 0.0004). RTQ-PCR demonstrated a 4–10-folddifference in mesothelioma tissues compared with normal pleu-ral samples (Fig. 2). Immunostaining showed variable gp96up-regulation in 10 of 19 tumors (Fig. 5). None of the normalpleura demonstrated any significant gp96 staining. Gp96 hasbeen implicated as an important cellular hsp that has beenknown for its ability to induce tumor-specific immunity inanimals that are immunized with it (53, 54).

Lung-related Resistance Protein. Our array data showmarked overexpression of a chemoresistance gene called LRP.LRP gene expression was up-regulated by 5.5-fold on the array(P 0.00001) and by 4–6-fold using RTQ-PCR (Fig. 2). LRPis a Mr 110,000 protein that is the major “vault” protein inhumans. Vaults are cytoplasmic organelles that are localized tothe nuclear membrane and act as a transporter, mediating nu-cleocytoplasmic exchange and have been shown to removecytostatic drugs such as doxorubicin, vincristine, VP-16, Taxol,and gramicidine-D (55). Overexpression of LRP has been dem-onstrated to select for doxorubicin resistance in colon and non-small cell lung carcinoma cells lines (56), and elevated expres-sion levels of LRP have been seen in colorectal and ovariancarcinomas and may serve as prognostic factors (57, 58).

Human malignant mesotheliomas are extremely resistant tochemotherapy with very low response rates to a wide variety ofchemotherapeutic agents such as doxorubicin. Our data suggestthat overexpression of LRP may be involved. This finding couldbe important therapeutically, because it has been shown thatribozymes capable of degrading LRP decrease the levels ofdoxorubicin that accumulate in the nucleus. If clinically useful(i.e., small molecule) inhibitors to LRP are developed, theycould potentially be used to great advantage in the treatment ofmesothelioma.

Galectin-3 Binding Protein. Galectin-3 binding protein(also known as Mac-2) is an endogenous �-galactoside-binding

Fig. 2 Validation of microarrayhybridization results by RTQ-PCR to compare gene expressionin pools of patients. The firstpool consisted of RNA (60 �geach) from pleural tissue from 5normal patients. The second andthird pools consisted of RNAfrom 5 patients (60 �g each)each who had been studied on themicroarrays. The average expres-sion for these two pools is la-beled “Confirmation of PriorMesothelioma Samples” on thefigure. The third pool consistedof RNA from 5 patients (60 �geach) who had not been studiedon the microarrays and was des-ignated “Prospective Evaluationof New Patients with Mesotheli-oma” on the figure. In general,there was good correlation be-tween the degree of up-regula-tion of the microarray results. Inall of the cases, direction ofchange in gene expression inboth techniques was the same.




protein that has been implicated in cell growth, differentiation,adhesion, and malignant transformation (59). The protein hasbeen shown to bind collagen and fibronectin, to be located in theextracellular matrix, and to promote cell adhesion and spreadingby binding to �1-integrins. Galectin-3 binding protein has beendemonstrated to have prognostic significance in several tumors.In head and neck squamous cell carcinomas, levels of galectin-3binding protein contributed additional prognostic value to con-ventional clinical staging of patients (44). Investigators in Mich-igan have demonstrated expression has been correlated withadvanced tumor stage in colon cancer, although direct evidencefor a role in metastasis is lacking (60). Similarly in breast cancer

and non-small cell lung cancer, increased expression levels havebeen proven to be an indicator of poor survival (61). In me-sothelioma, galectin-3 binding protein demonstrated a 10-foldup-regulation (P 0.00026) from hybridization experiments.Its role in mesothelioma tumor progression awaits additionalexperimentation.

Mr 67,000 Laminin Receptor. One of the most highlyup-regulated genes on the array was the Mr 67,000 lamininreceptor, which showed an 11.6-fold increase (P 0.0018).However, unlike the close correlation we observed between thereal-time PCR data and the array data for most of our otherup-regulated genes (Fig. 2), we observed only a very small

Fig. 3 Gene expression changesin glycolysis and the Krebscycle. Genes that qualified asup-regulated (�2-fold changewith a P � 0.001) are markedin dark gray. Light gray boxesindicate genes that were on thearray but did not reach cutoffs.Uncolored boxes indicate genesthat were not present on the mi-croarray so were not evaluated.Actual mesothelioma gene ex-pression levels compared withcontrols are indicated adjacentto the corresponding enzyme.Increased glycolysis and oxida-tive phosphorylation are criticalto malignant mesothelioma on-cogenesis. Pathway reflects theWarburg effect for mesothelialtumors.




(1.3–1.6-fold) increase using PCR. Interestingly, when we per-formed immunohistochemistry, we observed very little expres-sion of the Mr 67,000 laminin receptor on the tumor cells but didnote strong staining of blood vessels within the tumor (Fig. 5C,arrow). In contrast, blood vessels in normal lung tissue did notshow expression.

The Mr 67,000 laminin receptor is a nonintegrin protein ofMr 67,000 that was isolated on a laminin affinity column in thelate 1980s (62). It binds to a cysteine-rich domain in the shortarm of the laminin �1 chain. This receptor plays a role in tumordevelopment, progression, and metastasis. For example, its up-regulation on tumor cells is associated with the malignant phe-notype and prognosis in breast, lung, and ovarian cancer (63–

66). Given a report by Kallianpur et al. (67) that mesotheliomatissues expressed the Mr 67,000 laminin receptor, we weresomewhat surprised by the lack of tumor cells staining in ourtissue samples. Although we have no clear explanation, it ispossible that the protease and acid treatment that Kallianpur etal. (67) used on their formalin-fixed, paraffin-embedded mate-rial changed the type of staining that we observed using acetone-fixed frozen sections and a different antibody. Instead of tumorcell staining, we observed strong expression on the vesselswithin the tumor, those vessels presumably involved in angio-genesis.

This vessel-staining pattern is consistent with a secondfunction attributed recently to the Mr 67,000 laminin receptor,

Fig. 4 Gene expression changesin genes involved in initiationof mRNA translation. Color cod-ing is described in Fig. 3. Thetranslation initiation pathway ispromoted by a specific set ofinitiation factors. Several keycomponents of this pathway areup-regulated. In step 1, eIF6,eIF3 (2–3-fold up-regulated),and eIF1A induce dissociationof the 80S ribosomal complexinto 40S and 60S components.In step 2, the specific tRNAderivative used to initiate proteinsynthesis, methionyl-tRNA (met-tRNA) binds to eIF-2 (2-foldup-regulated) to form a complex.In step 3, binding of the 40Sribosomal subunit to met-tRNAto form the 43S initiation com-plex is regulated by eIF-2� (2.5-fold up-regulated). In step 4, theeIF-4 complex (composed of 3proteins: eIF-4E, eIF-4G, andeIF-4A), which is 2-fold up-regulated, binds to the m7G-capstructure of the mRNA.




specifically that this molecule is involved in angiogenesis inretinal tissues (68). Little data has yet accumulated on the roleof the Mr 67,000 laminin receptor in tumor angiogenesis, al-though a synthetic laminin peptide that binds to this receptor hasbeen shown to inhibit experimental tumor angiogenesis (69). Onthe basis of our data, we would propose that up-regulation of theMr 67,000 laminin receptor may play a role in the developmentof tumor vessels in mesothelioma.

Voltage-dependent Anion Channels. Two geneshighly up-regulated in mesothelioma were the VDAC 1,which was increased 6-fold (P 0.00025), and VDAC 2,which was increased 6.5-fold (P 0.00046). VDAC 1 wasup-regulated 2–3-fold using real-time PCR. VDAC is theprimary pathway for metabolite diffusion across the outermitochondrial membrane, that in its open configuration ispermeable to molecules as large as Mr 5,000 (70). VDAC has

been linked recently to cellular apoptosis through its inter-action with the Bcl-2 family of proteins, although the effectsof these proteins on VDAC are controversial. VDACs appearto interact with Bcl-2 family members and regulate levels ofapoptosis. The importance of up-regulation of VDAC inmesothelioma is currently unclear. It has been shown thatmesotheliomas express relatively high levels of both bax andBcl-xL, and that transfection of mesothelioma cells withadditional Bcl-xL protein enhances resistance to apoptosis(71, 72). Having higher levels of VDAC may amplify anyimbalance between pro- and antiapoptotic Bcl-2 family mem-bers that may be created by proapoptotic therapies.

Comparisons with Other StudiesAlthough differential display is not quantitative, it is interest-

ing to compare our results with the study conducted by Gordon

Fig. 5 Immunostaining wasperformed on select genes.Black arrow indicates normalpleural tissue in patients withnormal lung histology on theleft panel (�100 except H&E�40). Staining of mesotheli-oma samples demonstrated sig-nificant increase in cytokeratinand gp96 (adenotin) expressionon the right panel (�40 exceptgp96 �100). Although the Mr

67,000 laminin receptor was in-creased on the microarray, itappears to be a result of up-regulation on the vessels withinthe mesothelioma tumors.




et al. (5). In their comparison, 14 known genes (and about an equalnumber of expressed sequence tags) were identified to be morehighly expressed in mesothelioma than in normal pleura, includingthe �-folate receptor and IAP-1. Of the 5 genes that were on ourarray (histone acetyltransferase 1, ribosomal proteins S15a andL27a, IAP-1, and palmitoylated erythrocyte membrane protein), allshowed up-regulation with a range of 2.7–4.7-fold, although theexpression levels of some were quite low. The correlations in geneup-regulation between genes up-regulated in mesothelial and me-sothelioma cells in culture was much lower (3, 4), likely as a resultof the major changes occurring in cells during the explanation andculture process.

Gordon et al. (6) have reported recently results using tran-script profiling to discover 5 genes (MRC OX-2, KIAA097, VAC-�,calretinin, and PTGIS) that were preferentially expressed in ma-lignant mesothelioma versus lung adenocarcinomas. Our arrayexperiments contained VAC-� (also called annexin VIII), and wesimilarly found it to be one of our most highly up-regulated genes(11.8-fold; P 0.008; see Table 2). Annexin VIII is a specializedcalcium and phospholipid-binding protein normally present on lungendothelium, skin, and liver (73). Annexin VIII is also expressed inacute promyelocytic leukemia cells, although not in any otherlymphoid malignancies (74, 75). In acute promyelocytic leukemia,annexin VIII plays a role in signal transduction of cellular prolif-eration (75, 76). This gene can potentially serve as a diagnosticmarker in biopsy samples.

CaveatsAlthough microarray technology can provide vast amounts

of data, there are a number of limitations and caveats that mustbe considered in the interpretation of this and any other suchexperiment. First, selection of genes that have “real” and bio-logically significant differences between normal and tumor sam-ples is still an imperfect science. There are large amounts ofvariability at every step of the array process. Clearly using largesample numbers is an advantage to obtaining reliable results.Data analysis is also important. Rather than depending on anyone normalization or gene selection technique, we used a mul-tiplicity of approaches and selected genes that were commonlyidentified in multiple combinations of analysis algorithms. Thegoal of our gene identification strategy was not necessarilysensitivity (there are likely many gene differences that we didnot detect), but specificity. On the basis of our real-time PCRvalidation, this goal was achieved. Of the 16 genes validatedusing real-time PCR (those shown in Fig. 2 plus data showingno change in ubiquitin and cytochrome p450 reductase), therewas a striking similarity between the array data and the PCRdata in all but 1 of the genes (up-regulation of the Mr 67,000laminin receptor being the one exception). The validity of ourresults was additionally supported by showing very similarresults with PCR when analyzing a pool of RNA from fivemesothelioma tumors that had not been included in the originalarray. Thus, we feel that the normalization and gene selectionprotocols used accurately identified significantly changed genesand feel confident in the validity of the “non-PCR confirmed”data points.

Another limitation of our microarray hybridization experi-ments is they failed to predict genes that have been proven to bedifferentially expressed in malignant mesothelioma by reverse tran-

scription-PCR or other molecular biology techniques. Several po-tential reasons explain this finding in our experiments. Microarraysensitivity for selection of differentially expressed genes is un-known (77). Many well known genes (i.e., thrombospondin, p21,NCAM, p16, and NF2) were not on our nylon microarrays (4132genes). Furthermore, to minimize our false-positive rate of predic-tion of differentially expressed genes, we filtered those genes withlow intensities and heterogeneous expression. As a result, weeliminated genes (i.e., platelet-derived growth factor and p53) thatcould have important clinical implications to maintain a rigorousselection process. As bioinformatic tools improve, investigators canuse the data available publicly to reanalyze the data and extractmore differentially expressed genes.

A third caveat to be considered in our study is that micro-dissection of tumor tissue was not performed. Thus, a mixture ofcells including tumor, infiltrating WBCs, stromal cells, andtumor vessels were all analyzed. The limitations of this ap-proach were evident from our initial analysis where we notedthat a number of WBC-specific genes were up-regulated. Im-munostaining with an antibody against the common leukocyteantigen CD45 confirmed that tumors and normal pleural tissueswere infiltrated to various degrees by leukocytes. This con-founding factor was reduced to some degree by a “virtualmicrodissection” (i.e., using gene ontology data to “subtractout” leukocyte-specific genes), but this process is by natureincomplete and cannot remove genes common to all cells.

Another example of potentially confusing data are illus-trated by our findings with the Mr 67,000 laminin receptor.Although this receptor was up-regulated in the tumor samples, itwas not expressed on the tumor cells, but on the infiltratingvessels. On the other hand, using nonmicrodissected tissues hasthe advantage of revealing what genes will be expressed in astandard clinical biopsy specimen where only macrodissectionwill be feasible. If one is looking for potentially diagnostic orprognostic genes, it may be more important to sample the entiretissue milieu including white cell, stromal, and endothelial cellgenes. Clearly a number of validation steps including quantita-tive RNA analysis, protein measurements, and immunostainingare important when trying to assign significance to a specificgene identified in microarray data.

ConclusionMicroarray analysis of mesothelioma tumors revealed ac-

tivation of a number of key pathways including genes involvedin glucose metabolism and mRNA translation. These findingsare consistent with the need of the tumor to enhance energy andprotein production, and provide a molecular basis for the clinicalobservation that mesotheliomas show strong uptake of 18FDGon PET scanning. HIF-1 may have an important role in regula-tion of energy metabolism and will be the subject of additionalstudy. Other up-regulated genes included gp96, LRP, galectin-3binding protein, the Mr 67,000 laminin receptor (on tumorvessels), and voltage-dependent anion channels. Additionalstudy of these proteins may be important to improving ourunderstanding of the pathogenesis of mesothelioma, and willhopefully improve diagnostic methods and treatments.




ACKNOWLEDGMENTSWe thank Adam Henry for valuable assistance in preparation of

this article.

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2003;9:3080-3097. Clin Cancer Res Sunil Singhal, Rainer Wiewrodt, Liliana D. Malden, et al. Gene Expression Profiling of Malignant Mesothelioma

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