Oncogene (2008) 27, 1650–1656 & 2008 Nature Publishing Group All rights reserved 0950-9232/08 $30.00 www.nature.com/onc ONCOGENOMICS AZGP1 mRNA levels in normal human tissue correlate with lung disease status

FS Falvella1,4, M Spinola1,4, C Pignatiello1, S Noci1, B Conti2, U Pastorino2, A Carbone3 and TA Dragani1

1Department of Experimental Oncology and Laboratories, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy; 2Department of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy and 3Department of Pathology, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy

Evidence in animal models has suggested an association et al., 1994; Hoover, 2000) and nonsmokers (Wu et al., between susceptibility to lung tumorigenesis and - 2004). Indeed, genetic linkage analysis of pedigrees with expression profiles in normal lung. Here, we compared multiple lung mapped a lung cancer suscepti- RNA pools from normal lung tissue of lung adenocarcinoma bility locus to a region of 6 (Bailey-Wilson patients (cases) or non-lung cancer patients (controls) by et al., 2004). A gene-expression profile that distinguishes hybridization of whole- expression arrays. individuals at higher genetic risk of lung cancer might Principal component analysis identified a gene-expression permit the use of non-invasive methods for the estima- signature of 85 that distinguishes cases from controls tion of individual risk, such as analysis in induced as well as smokers from nonsmokers. Elevated mRNA sputum of related to the specific gene-expres- levels of one of these genes, AZGP1, were significantly sion profile. It might also be possible to identify new associated with disease status. These results support the genetic targets associated with the risk of lung cancer hypothesis that differences in the gene-expression levels of and develop new strategies for the prevention and early the normal tissue may be predictive of genetic predisposition diagnosis of lung cancer. to lung cancer in humans. In the present study, we compared the gene-expres- Oncogene (2008) 27, 1650–1656; doi:10.1038/sj.onc.1210775; sion profiles of normal lung tissue from lung adeno- published online 27 August 2007 carcinoma (ADCA) patients and from patients with lung metastases from other primary tumor sites. We Keywords: genetic susceptibility; complex diseases; gene found that variations in mRNA levels of 85 genes are expression; inherited predisposition; lung neoplasms; associated with lung cancer status and smoking habit. microarray Among these genes, the AZGP1 gene showed the closest association with disease status and was characterized further. Introduction

We described previously a specific gene-expression Results profile associated with genetic susceptibility to lung tumorigenesis in the normal lung of mouse inbred RNA pools from normal lung tissue of nonsmoker or strains (Gariboldi et al., 2003). Strain-dependent differ- smoker lung ADCA patients and non-lung cancer ential expression profiles in the target normal tissues controls were hybridized to the Human U133 2.0 plus have also been reported in other rodent models of GeneChip. Genes that were not expressed in any of the genetic susceptibility to tumorigenesis (Falvella et al., four samples were removed from the analysis, thus ONCOGENOMICS 2002; Fujiwara et al., 2003; Riggs et al., 2005), reducing the number of transcripts from 54 676 to suggesting that alteration of gene-expression profile in 33 363. Among the 250 transcripts showing the highest normal organs might represent a general phenomenon coefficient of variation, we selected the first 88 probes associated with genetic predisposition to tumorigenesis. with >4-fold maximum difference among the samples. In humans, genetic predisposition to the risk of lung The 88 probes identified 85 independent transcripts, cancer also seems to play a role in both smokers (Sellers since AZGP1, RP13-36C9.1 and AGTR2 transcript probes were present twice. In principal component Correspondence: Dr TA Dragani, Department of Experimental analysis to determine the extent of relationship among Oncology and Laboratories, Fondazione IRCCS Istituto Nazionale the samples based on the 85-transcript expression Tumori, Via G Venezian 1, Milan 20133, Italy. pattern, the main component on the x axis distinguished E-mail: [email protected] 4These two authors contributed equally to this work. ADCA (left) from control (right) samples, whereas the Received 23 March 2007; revised 16 July 2007; accepted 27 July 2007; second main component separated the nonsmoker (top) published online 27 August 2007 from the smoker (bottom) samples (Figure 1). Profile of lung cancer predisposition FS Falvella et al 1651 Table 1 Characteristics of lung adenocarcinoma patients and control subjects Subject characteristics Controls Cases

No of subjects 53 55 Median age (years) 54.5 63.8

Gender Male 31 39 Female 22 16

Smoker status Never 24 6 Ever 27 49

Type of cancer Lung ADCA — 55 Figure 1 Principal component analysis of the correlation matrix Breast carcinoma 2 — of gene-expression levels in normal human . Analysis included Colon/intestinal carcinoma 17 — 85 genes whose expression levels showed the highest coefficient of Melanoma 5 — variation in four RNA pools from lung ADCA or non-lung cancer Renal carcinoma 4 — patients (smokers or nonsmokers). The first factorial axis, which Sarcoma 23 — accounts for 38% of the total gene-expression phenotype variance, Othersa 2— contrasts samples with correlated to lung cancer risk (left, lung ADCA patients; right, control subjects). The second Clinical stage factorial axis (36% of the variance) contrasts samples whose I29 different gene expression is correlated with smoker status (top, >I 26 nonsmokers; bottom, smokers). ADCA, adenocarcinoma. Abbreviation: ADCA, adenocarcinoma. aA salivary and an adenoid- cystic carcinoma. Microarray results were validated by kinetically monitored, reverse transcriptase-initiated PCR (kRT– PCR) of 27 genes in the same RNA pool. Results of the relative quantity values ranging from 0.02 to 24.1 two assays correlated significantly (r ¼ 0.28; P ¼ 0.003), (mean7s.e. ¼ 1.670.3). No statistical association was indicating that microarray analysis detected real varia- observed between AZGP1 mRNA levels and sex, smoker tions in the gene-expression levels (not shown). status, age at diagnosis or clinical stage (stage I versus Among the group of 85 genes, a preliminary statistical higher stages). However, a significant association was analysis of microarray data to identify genes whose found between disease status and AZGP1 mRNA levels expression levels are associated with the lung cancer or (P ¼ 0.01), with ADCA patients showing about twofold non-lung cancer status pointed to AZGP1, IGLJ3 and higher mean AZGP1 mRNA levels as compared to non- FLJ22965 (Po0.05) and to HSPD1, BC006216 and ADCA patients (Figure 2). RAB12 (P-values between 0.05 and 0.1) (Table 2). Immunohistochemical analysis revealed no detectable IGLJ3 and BC006216 were excluded from further AZGP1 product in the normal lung, with weak analyses since they did not identify any specific cytoplasmic staining of only very few alveolar cells transcript. Expression levels of AZGP1, FLJ22965, (Figure 3, top). Most of the lung cancer samples were HSPD1 and RAB12 genes were validated by kRT– negative for AZGP1 protein expression and staining was PCR in the same RNA pool used for microarray clearly detected in the cytoplasm of only four samples, hybridization. FLJ22965, HSPD1 and RAB12 tran- including a squamous cell carcinoma, an ADCA scripts showed no significant association with disease (Figure 3, bottom left), a bronchoalveolar carcinoma status, whereas the AZGP1 transcript confirmed the (Figure 3, bottom right) and a mucoepidermoid carci- microarray association and was analysed in all indivi- noma. Another squamous cell carcinoma and another dual samples constituting the four RNA pools plus ADCA also showed positive staining in a few cells. 49 additional specimens (Table 1). kRT–PCR was By western blotting analysis, AZGP1 protein expres- carried out using TaqMan gene-expression assay sion was apparent in normal lung tissue from either normalized against hydroxymethylbilane synthase ADCA or non-lung cancer patients (Figure 4). (HMBS), and relative quantity was calculated using a mix of equal amounts of the four RNA pools as a calibration sample. Discussion To test variability and reproducibility of the kRT– PCR assay, analysis of AZGP1 was carried out twice in In the present study, we asked whether the gene- 12 lung ADCA patients and in 15 controls; results of the expression profile of human normal lung tissue might two independent experiments were highly correlated represent a potential signature of the individual genetic (r ¼ 0.95, Po0.0001). risk for lung cancer development. We compared normal AZGP1 expression levels were highly variable in the lung tissue excised during lobectomy to remove lung normal lung tissue of the analysed individuals, with cancer (cases) or lung metastasis of a non-lung cancer

Oncogene Profile of lung cancer predisposition FS Falvella et al 1652 Table 2 List of 85 genes whose transcript levels showed the highest coefficient of variation and X4-fold differences among samplesa Gene symbol ADCA smoker ADCA nonsmoker Control smoker Control nonsmokers Ratio of ADCA/control Ratio of smoker/nonsmoker

ACTR2 0.6 1.9 1.9 0.5 1 1.1 ADAMTS1 0.7 2.5 1 0.6 2 0.5 AGTR2 2.2 1.1 1.3 0.3 2.1 2.4 AHRR 1.5 0.7 2.3 0.4 0.8 3.46* ANK2 0.4 3.1 1.2 0.7 1.8 0.4 APOL6 4.3 1 0.2 1.1 4 2.2 AQP4 0.9 2 1.3 0.4 1.7 0.9 ATP6V0D2 2 0.7 2.2 0.4 1 4.05* AZGP1 2.1 1.7 0.6 0.5 3.41* 1.2 B3GNT5 0.9 2.7 1.7 0.3 1.9 0.9 BC006216 1.4 2.9 0.4 0.6 4.23* 0.5 BTG2 0.7 2.7 1 0.5 2.3 0.5 C1S 0.6 2.7 1.2 0.5 1.9 0.6 CAMKK2 0.5 3.4 0.9 0.6 2.6 0.4 CCL20 2.2 0.7 1.5 0.5 1.5 3.2* CLG 0.3 1.1 1.8 1.5 0.5 0.8 CTNNA1 0.8 2 1.4 0.4 1.5 0.9 CYP1A1 5.3 0.2 10.4 0.1 0.5 58.02* DDX3X 0.7 2.5 1.2 0.5 1.9 0.7 DSCR1 0.8 2.2 1.3 0.5 1.7 0.8 DUSP1 0.7 2.6 1 0.6 2.1 0.5 EIF1 1.1 1.5 1.9 0.3 1.2 1.7 ENG 0.3 3.8 1.2 0.8 2 0.3 EPAS1 0.5 2.1 1.8 0.5 1.1 0.9 FER 0.3 1.4 2.3 1.2 0.5 1 FGG 3 0.8 1.4 0.3 2.3 4 FKBP5 0.5 6.8 3.4 0.1 2.1 0.6 FLJ22965 6.9 2.7 0.3 0.2 20.15* 2.5 FLJ23471 0.6 5.7 0.9 0.3 5.5 0.3 FOLH1 1.5 3 0.2 1.3 3 0.4 FUS 0.5 2.2 1.5 0.6 1.2 0.7 GABPB2 1.7 2.9 0.2 0.9 4.3 0.5 GAGED2 4.8 0.3 2.7 0.2 1.8 13.17* GPR110 10.6 1.1 0.2 0.4 18.6 6.9 HAS1 0.3 2.7 0.4 3.2 0.8 0.11* HOXC8 0.4 0.4 6.1 1 0.1 4.4 Hs.449575 1 2.6 0.5 0.8 2.9 0.5 HSHIN1 0.6 1.1 3.1 0.5 0.5 2.3 HSPCB 0.8 2 1.3 0.5 1.5 0.9 HSPD1 7.5 1.7 0.3 0.3 15.96* 4.1 IBRDC2 0.4 2.4 1.4 0.8 1.2 0.6 IGLJ3 3.4 3.9 0.3 0.3 12.97* 0.9 IL6ST 0.7 2.9 1.2 0.4 2.2 0.6 IQGAP1 0.3 1.5 1.6 1.5 0.6 0.6 ITGB6 0.9 2.2 1 0.5 2.1 0.7 ITLN1 0.5 1.3 0.8 2.1 0.6 0.4 KIAA1361 0.4 1.1 1.5 1.6 0.5 0.7 MAPKAPK2 0.9 1.9 1.5 0.4 1.4 1 MAT2A 0.5 2.3 1.2 0.7 1.5 0.6 MATR3 0.9 2 1.3 0.4 1.7 0.9 MCL1 1.1 2.7 0.8 0.4 3.1 0.6 MECT1 0.5 0.6 1 3.2 0.3 0.4 MET 1 2.3 1 0.4 2.3 0.7 MVD 1.9 0.5 4.5 0.2 0.5 8.84* MYH11 0.4 3 1.4 0.7 1.6 0.5 NFASC 0.4 1.4 0.5 3.7 0.4 0.17* NR4A3 0.8 2.4 1.1 0.4 2.1 0.7 PBEF1 0.8 2.2 1.1 0.5 1.9 0.7 PCDHpsi-5 1 0.2 7.7 0.7 0.1 9.6 PGS1 0.6 2.5 1.1 0.6 1.8 0.5 PRKG1 0.5 2.2 2.2 0.5 1 1 PSCD1 1.7 3 1.2 0.2 3.5 0.9 RAB12 0.2 0.6 4.1 2.7 0.11* 1.3 RASEF 0.4 1.6 1.8 0.9 0.8 0.9 RHOB 0.6 2.4 1.7 0.5 1.4 0.8 RNF125 3.6 1.3 0.1 1.8 2.6 1.2 RP13-36C9.1 0.7 0.5 3 1 0.3 2.4 SEC61A1 1 2 1.2 0.4 1.8 0.9 SERPINB2 3.9 2 0.1 1.4 3.9 1.2 SERPINE1 1.1 0.9 2.1 0.5 0.8 2.3

Oncogene Profile of lung cancer predisposition FS Falvella et al 1653 Table 2 (continued ) Gene symbol ADCA smoker ADCA nonsmoker Control smoker Control nonsmokers Ratio of ADCA/control Ratio of smoker/nonsmoker

SFRS2IP 0.4 1.3 1.7 1.2 0.6 0.9 SFRS5 0.6 3.1 1 0.6 2.3 0.4 SOD2 2.6 0.9 0.8 0.5 2.7 2.4 SRPR 0.7 2.4 1.3 0.4 1.8 0.7 STC1 0.6 2.3 1.4 0.5 1.5 0.7 SUPT16H 0.7 2.5 1.2 0.5 2 0.6 TncRNA 0.7 2.4 1.8 0.3 1.4 0.9 TOP1 0.4 1.7 1.3 1.2 0.9 0.6 TTC3 0.3 2 2.9 0.6 0.7 1.2 UFM1 0.8 2 1.3 0.5 1.5 0.8 USP10 0.1 3 2.2 1.5 0.9 0.5 VIL2 0.5 2.1 1.4 0.7 1.3 0.7 WDR1 0.7 2.5 1 0.5 2.1 0.6 ZFP36L2 0.4 1.7 1.6 0.9 0.9 0.8 ZNF649 0.3 1.3 8.8 0.3 0.2 5.7

a Abbreviations: ADCA, adenocarcinoma; ANOVA, analysis of variance. *Po0.1, ANOVA analysis on log2-transformed data. Normalized fold-change.

Our use of pooled RNA to analyse four groups of specimens constituted by smoker or nonsmoker lung ADCA or non-lung cancer subjects is a cost-effective approach and allows for an overall picture of the differences among RNA pools (Kendziorski et al., 2005). The expression profile of 85 independent transcripts in the normal lung tissue was able to distinguish lung ADCA from non-lung cancer patients (Figure 1). How- ever, kRT–PCR analysis did not detect significant differences between cases and controls in gene-expression levels except for AZGP1. Most likely, the changes in expression of individual transcripts are of relatively small magnitude, although the 85 transcripts collectively may Figure 2 AZGP1 expression levels in normal lung tissue assessed provide a signature of individual genetic predisposition by kRT–PCR. Mean AZGP1 transcript levels were B2-fold higher to lung cancer. in lung ADCA patients (n ¼ 55) than in non-lung cancer patients The transcript of the AZGP1 gene, encoding a soluble (n ¼ 53) (P ¼ 0.014, Kruskal–Wallis’s test). ADCA, adenocarcino- 41-kDa protein (zinc-a2-glycoprotein, ZAG), was among ma; kRT–PCR, kinetically monitored, reverse transcriptase-in- itiated PCR. the transcripts showing a statistical association with lung ADCA status. Independent kRT–PCR analysis in a series of 55 cases and 53 controls indicated B2-fold higher AZGP1 mRNA levels in normal lung of ADCA (controls). Although we cannot exclude the possible patients as compared to the corresponding tissue from presence of micrometastases in the normal lung tissue non-lung cancer patients (Figure 2), while immuno- samples examined, macroscopic analysis indicated no histochemical analysis of a tissue array did not detect apparent cancer contamination. Normal lung tissue AZGP1 expression in normal lung tissue, perhaps due from healthy individuals as control was not available for to the lower sensitivity of tissue immunostaining as our study, but comparative analysis using non-lung compared to kRT–PCR. AZGP1 protein was instead cancer patient samples seemed appropriate in light of clearly detectable by western blot analysis, thus con- previous results indicating tissue specificity of genetic firming that AZGP1 mRNA expression is associated susceptibility to lung tumorigenesis in mice (Dragani, with expression of its protein product in normal lung. 2003). While the genetic heterogeneity of the human The low sensitivity of tissue immunostaining as com- population and the heterogeneity of exposure to pared to western blot analysis may perhaps be environmental factors may modulate gene expression associated with low local concentration of AZGP1 in the lung and limit the sensitivity of our analysis, we protein in tissue compartments, as opposed to relatively analysed separately smoker and nonsmoker subjects to high protein concentration in electrophoretic bands. In reduce the confounding effect of the smoking habit. addition, we detected AZGP1 protein expression in Notwithstanding these limitations, our study provides at some lung cancer specimens of different histotype. least preliminary evidence supporting the hypothesis AZGP1 expression was reported previously to be a that genetic predisposition to lung cancer is accompa- predictor of prostate cancer prognosis, with decreased nied by alteration of gene expression already apparent in mRNA or protein levels associated with a higher risk of the normal lung tissue. recurrence, metastatic progression or death (Lapointe

Oncogene Profile of lung cancer predisposition FS Falvella et al 1654

Figure 3 Immunostaining of ZAG protein. In normal lung tissue (top panels), most alveoli are not immunostained (left), although sporadic alveolar cells show weak cytoplasmic staining (right). In tumor specimens (bottom panels), weak granular staining concentrated along the inner borders of the tumor glands is observed in a lung ADCA sample (left); bronchoalveolar carcinoma shows weak and uniform cytoplasmic staining (right). ADCA, adenocarcinoma; ZAG, zinc-a2-glycoprotein.

Consistent with previous reports (Spira et al., 2004; Harvey et al., 2007), we found that the smoking habit is an important source of variability in the expression pattern of the normal lungs and that a specific gene- expression profile can distinguish ever- from never- smoker subjects (Figure 1). The profile we identified in our samples did not overlap with profiles reported Figure 4 Western blotting analysis of expression of ZAG protein previously (Spira et al., 2004; Harvey et al., 2007), but in normal lung tissue from either ADCA (lanes 1–3) or non-lung cancer subjects (lanes 4–6). ADCA, adenocarcinoma; ZAG, zinc- the discrepancy might reside in the use of different a2-glycoprotein. biological materials (bronchial cells recovered by bronchial brushing versus lung tissue specimens), et al., 2004; Henshall et al., 2006). At present, no data different recruitment criteria and exposure to cigarette are available on the potential role of AZGP1 in lung smoking, population genetic differences and different cancer onset and/or progression. There is evidence that microarray platforms and methods of analysis. Among AZGP1 is present in most body fluids and in the secre- the genes confirmed by kRT–PCR to be associated with tory epithelial cells of many human tissues (Tada et al., tobacco smoke exposure were HAS1 (hyaluronan 1991). AZGP1 is an adipokine that may be involved in synthase), CYP1A1 (cytochrome P450, family 1, sub- the local regulation of function by family A, polypeptide 1) and AHRR (aryl-hydrocarbon stimulating lipid degradation in adipocytes; the gene is receptor repressor). mRNA levels of HAS1 were lower markedly upregulated in mice with cancer cachexia in smokers than in nonsmokers, whereas mRNA levels (Bing et al., 2004). of CYP1A1 and AHRR were higher in smokers. Higher Comparison of our present microarray hybridization CYP1A1 levels in smokers as compared to nonsmokers data in humans with the data obtained previously on were also found in a previous study using broncho- mouse normal lungs (Gariboldi et al., 2003) identified alveolar lavage cells (Thum et al., 2006). Interestingly, a only one gene, MCL1, with an expression profile shared genetic polymorphism of the CYP1A1 gene seems to by both species. However, kRT–PCR did not reveal modulate inducibility of this gene in lymphocytes and to significant differences between cases and controls, since be associated with risk of lung ADCA (Crofts et al., MCL1 mRNA levels differed in this respect in 1994; Larsen et al., 2006). nonsmokers but not in smokers (Table 2). The low Although our preliminary study awaits confirmation concordance rate may rest in species differences, but in independent and larger series, our results suggest that also in differences in exposure between mouse strains a specific gene-expression profile of normal lung tissue (no exposure to any chemicals) and humans (smokers). can distinguish lung ADCA from non-lung cancer

Oncogene Profile of lung cancer predisposition FS Falvella et al 1655 patients. These findings support the hypothesis that (Hs01391604_m1) were quantitated by kRT–PCR using Taq- genetic predisposition plays a role in lung ADCA risk Man Gene Expression Assays (Applied Biosystems). The real- through alteration of the gene-expression pattern in the time PCR amplification mixture contained cDNA template normal tissue, as observed in animal models. These diluted in RNase-free water, 10 ml2Â TaqMan Universal results might represent a basis for the development of PCR Master Mix No AmpErase UNG (Applied Biosystems) and 1 mlof20Â TaqMan Gene Expression Assay Mix tools to estimate individual risk for lung cancer, thus (Applied Biosystems), in a final volume of 20 ml. Reactions leading to improved lung cancer prevention and early were run in duplicate on the 7900HT System (Applied diagnostic strategies. Biosystems) for 40 cycles (95 1C for 15 s, 60 1C for 1 min) after enzyme activation (95 1C for 10 min). The HMBS gene (Hs00609297_m1) was used as a control for possible differ- Materials and methods ences in cDNA amounts. Relative expression levels were calculated using the comparative Ct method. Patients and samples Patients who underwent lung lobectomy for lung cancer or Immunohistochemistry and western blotting lung metastasis of non-lung cancers were recruited (Table 1). A Immunohistochemical analysis of the AZGP1 encoded ZAG small piece of apparently normal lung tissue, distant from the was conducted on normal or cancer lung tissues from our macroscopic lung cancer tissue, was excised from the lung lobe Pathology Department and also on commercial tissue arrays removed at surgery and stored in RNAlater solution (Ambion, consisting of either 59 resected lung cancer specimens (CC2 Austin, TX, USA). Smoking status was assessed by clinical Array) or their corresponding normal lung tissue (CCN2 records and subjects were scored as ever or never smokers. array) from Korean patients (SuperBioChips Lab, Seoul, Total RNA was obtained using the RNeasy MIDI Kit Korea). Tissue slides were preincubated in ethylenediaminete- (Qiagen, Valencia, CA, USA). RNA pools were prepared from traacetic acid buffer (pH 8.0) at 95 1C in an autoclave for nonsmoker lung ADCA cases (n ¼ 6), nonsmoker non-lung 30 min and mixed with anti-ZAG (1D4) mouse monoclonal cancer controls (n ¼ 16), smoker lung ADCA cases (n ¼ 18) and antibody raised against the human full-length ZAG (Santa smoker non-lung cancer controls (n ¼ 8). Cruz Biotechnology Inc., Santa Cruz, CA, USA), used at a concentration of 1:100. Immunoreactivity was revealed by incubating the tissue microarray sections with UltraVision Array hybridization and quantitative RT–PCR LP Detection System HRP Polymer (LabVision Corp., Double-stranded complementary DNA (cDNA) was synthe- Fremont, CA, USA). Expression was visualized using 3,30- sized from 5 mg of total RNA using a double-stranded cDNA diaminobenzidine (DAB þ , DAKO Inc., Carpinteria, CA, synthesis kit (Invitrogen, Carlsbad, CA, USA) with a poly-dT USA) as substrate and counterstained with hematoxylin primer incorporating a T7 promoter. The cDNA was purified solution. with the Affymetrix sample cleanup module (Affymetrix, Santa Normal lung tissue of ADCA and non-lung cancer subjects Clara, CA, USA). Biotin-labeled complementary RNA was was homogenized in lysis buffer (10 mM 4-(2-hydroxyethyl)-1- generated from the double-stranded cDNA template by in vitro piperazineethanesulfonic acid (pH 7.9), 1.5 mM MgCl2 and transcription with T7 polymerase using a nucleotide mix con- 10 mM KCl) containing 1 mM dithiothreitol and Protease taining biotinylated UTP (30-amplification reagents for IVT Inhibitor Cocktail diluted 1:100 (P8340, Sigma, St Louis, labelling kit; Affymetrix). The biotinylated complementary MO, USA). After incubation on ice for 5 min, 1% IGEPAL RNA was purified using the Affymetrix sample cleanup CA-630 and 0.1% sodium dodecyl sulphate were added. Equal module. For each sample, 20 mg of IVT product was digested amounts (120 mg) of protein extracts were resolved by 10% with fragmentation buffer (Affymetrix) for 35 min at 94 1Cto polyacrylamide gel and transferred onto Hybond-C super an average size of 35–200 bases, and 15 mg of each fragmented nitrocellulose membranes (Amersham, Buckinghamshire, biotinylated complementary RNA, along with hybridization UK). Membrane was incubated with anti-ZAG (1D4) mouse controls (Affymetrix), was hybridized to a Human U133 2.0 monoclonal antibody (1:200 dilution) (Santa Cruz Biotechno- plus GeneChip for 16 h at 45 1C and 60 r.p.m. Arrays were logy Inc.). The ECL system (Amersham) was used for washed and stained according to the standard Antibody detection. Amplification for Eukaryotic Targets protocol (Affymetrix). The stained arrays were scanned at 532 nm using an Statistical analysis Affymetrix GeneChip Scanner 3000. Principal component analysis was carried out on the correla- For quantitative PCR (kRT–PCR), RNAs from pooled or tion matrix of the gene-expression levels expressed as log2 individual samples were reverse-transcribed using the Thermo- values. Differences in kRT–PCR values by sex, smoker status Script RT–PCR system (Invitrogen). Gene-specific PCR or disease status were analysed by the Kruskal–Wallis test. primers amplifying 100–150 bp fragments were designed for Correlations between microarray and kRT–PCR results, 23 transcripts using the Unigene mRNA sequences. kRT–PCR between independent kRT–PCR assays, between kRT–PCR amplification mixtures contained 1 ml template cDNA, 12.5 ml values and age at cancer onset were assessed using Pearson’s 2 Â QuantiTect SYBRGreen PCR Master Mix (Qiagen), and correlation coefficient, r. Statistical procedures were carried 0.3 mM specific PCR primers in a final volume of 25 ml. out with SPSS 10.1 (SPSS Inc., Chicago, IL, USA) software. Reactions were run in triplicate on an ABI GeneAmp 7900 sequence detection system (Applied Biosystems, Foster City, CA, USA). The HMBS gene (GenBank accession no. Acknowledgements NM_000190) was amplified with primers 50-AGGATGGG CAACTGTACCTG-30 and 50-GCCTACCAACTGTGGGT We thank Francesca Dominoni for technical assistance. CAT-30 as a housekeeping control for possible differences in Microarrays were performed at the Boston University Micro- cDNA amounts. array Resource. This work was funded in part by grants from mRNA levels of AZGP1 (Hs00426651_m1), FLJ22965 Associazione and Fondazione Italiana Ricerca Cancro (AIRC (Hs00254412_m1), HSPD1 (Hs01866140_g1) and RAB12 and FIRC) of Italy.

Oncogene Profile of lung cancer predisposition FS Falvella et al 1656 References

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