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Discordant Protein and mRNA Expression in Lung Adenocarcinomas

2002; Elsevier BV; Volume: 1; Issue: 4 Linguagem: Inglês

10.1074/mcp.m200008-mcp200

1535-9484

Guoan Chen, Tarek G. Gharib, Chiang-Ching Huang, Jeremy M. G. Taylor, David E. Misek, Sharon L. R. Kardia, Thomas J. Giordano, Mark D. Iannettoni, Mark B. Orringer, Samir Hanash, David G. Beer,

Genomics and Chromatin Dynamics

The relationship between gene expression measured at the mRNA level and the corresponding protein level is not well characterized in human cancer. In this study, we compared mRNA and protein expression for a cohort of genes in the same lung adenocarcinomas. The abundance of 165 protein spots representing 98 individual genes was analyzed in 76 lung adenocarcinomas and nine non-neoplastic lung tissues using two-dimensional polyacrylamide gel electrophoresis. Specific polypeptides were identified using matrix-assisted laser desorption/ionization mass spectrometry. For the same 85 samples, mRNA levels were determined using oligonucleotide microarrays, allowing a comparative analysis of mRNA and protein expression among the 165 protein spots. Twenty-eight of the 165 protein spots (17%) or 21 of 98 genes (21.4%) had a statistically significant correlation between protein and mRNA expression (r > 0.2445; p < 0.05); however, among all 165 proteins the correlation coefficient values (r) ranged from −0.467 to 0.442. Correlation coefficient values were not related to protein abundance. Further, no significant correlation between mRNA and protein expression was found (r = −0.025) if the average levels of mRNA or protein among all samples were applied across the 165 protein spots (98 genes). The mRNA/protein correlation coefficient also varied among proteins with multiple isoforms, indicating potentially separate isoform-specific mechanisms for the regulation of protein abundance. Among the 21 genes with a significant correlation between mRNA and protein, five genes differed significantly between stage I and stage III lung adenocarcinomas. Using a quantitative analysis of mRNA and protein expression within the same lung adenocarcinomas, we showed that only a subset of the proteins exhibited a significant correlation with mRNA abundance. The relationship between gene expression measured at the mRNA level and the corresponding protein level is not well characterized in human cancer. In this study, we compared mRNA and protein expression for a cohort of genes in the same lung adenocarcinomas. The abundance of 165 protein spots representing 98 individual genes was analyzed in 76 lung adenocarcinomas and nine non-neoplastic lung tissues using two-dimensional polyacrylamide gel electrophoresis. Specific polypeptides were identified using matrix-assisted laser desorption/ionization mass spectrometry. For the same 85 samples, mRNA levels were determined using oligonucleotide microarrays, allowing a comparative analysis of mRNA and protein expression among the 165 protein spots. Twenty-eight of the 165 protein spots (17%) or 21 of 98 genes (21.4%) had a statistically significant correlation between protein and mRNA expression (r > 0.2445; p < 0.05); however, among all 165 proteins the correlation coefficient values (r) ranged from −0.467 to 0.442. Correlation coefficient values were not related to protein abundance. Further, no significant correlation between mRNA and protein expression was found (r = −0.025) if the average levels of mRNA or protein among all samples were applied across the 165 protein spots (98 genes). The mRNA/protein correlation coefficient also varied among proteins with multiple isoforms, indicating potentially separate isoform-specific mechanisms for the regulation of protein abundance. Among the 21 genes with a significant correlation between mRNA and protein, five genes differed significantly between stage I and stage III lung adenocarcinomas. Using a quantitative analysis of mRNA and protein expression within the same lung adenocarcinomas, we showed that only a subset of the proteins exhibited a significant correlation with mRNA abundance. Lung cancer is the leading cause of cancer death for both men and women in the United States. Adenocarcinomas of the lung comprise ∼40% of all new cases of non-small cell lung cancer and are now the most common histologic type. Functional genomics, broadly defined as the comprehensive analysis of genes and their products, have become a recent focus of the life sciences (1.Ideker T. Thorsson V. Ranish J.A. Christmas R. Buhler J. Eng J.K. Bumgarner R. Goodlett D.R. Aebersold R. Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.Science. 2001; 292: 929-934Google Scholar). Application of these approaches to lung adenocarcinomas has the potential to aid in the identification of high risk patients with resectable early stage lung cancer that may benefit from adjuvant therapy, as well as to identify new therapeutic targets. In human lung cancer, however, little is currently understood regarding the relationship between gene expression as determined by measuring mRNA levels and the corresponding abundance of the protein products. A number of powerful techniques for analysis of gene expression have been used including differential display (2.Liang P. Pardee A.B. Differential display. A general protocol.Mol. Biotechnol. 1998; 10: 261-267Google Scholar), serial analysis of gene expression (3.Porter D.A. Krop I.E. Nasser S. Sgroi D. Kaelin C.M. Marks J.R. Riggins G. Polyak K. A sage (serial analysis of gene expression) view of breast tumor progression.Cancer Res. 2001; 61: 5697-5702Google Scholar), DNA microarrays (4.Bittner M. Meltzer P. Chen Y. Jiang Y. Seftor E. Hendrix M. Radmacher M. Simon R. Yakhini Z. Ben-Dor A. Sampas N. Dougherty E. Wang E. Marincola F. Gooden C. Lueders J. Glatfelter A. Pollock P. Carpten J. Gillanders E. Leja D. Dietrich K. Beaudry C. Berens M. Alberts D. Sondak V. Molecular classification of cutaneous malignant melanoma by gene expression profiling.Nature. 2000; 406: 536-540Google Scholar), and proteomics via two-dimensional polyacrylamide gel electrophoresis and mass spectrometry (5.Fung E.T. Wright Jr., G.L. Dalmasso E.A. Proteomic strategies for biomarker identification: progress and challenges.Curr. Opin. Mol. Ther. 2000; 2: 643-650Google Scholar). Bioinformatics tools have also been developed to help determine quantitative mRNA/protein expression profiles of all types of cells and tissues (6.Davidson D. Baldock R. Bioinformatics beyond sequence: mapping gene function in the embryo.Nat. Rev. Genet. 2001; 2: 409-417Google Scholar) and now can be applied to benign and malignant tumors. DNA microarrays (cDNA and oligonucleotide) permit the parallel assessment of thousands of genes and have been utilized in gene expression monitoring (7.Chee M. Yang R. Hubbell E. Berno A. Huang X.C. Stern D. Winkler J. Lockhart D.J. Morris M.S. Fodor S.P. Accessing genetic information with high-density DNA arrays.Science. 1996; 274: 610-614Google Scholar), polymorphism analysis (8.Wang D.G. Fan J.B. Siao C.J. Berno A. Young P. Sapolsky R. Ghandour G. Perkins N. Winchester E. Spencer J. Kruglyak L. Stein L. Hsie L. Topaloglou T. Hubbell E. Robinson E. Mittmann M. Morris M.S. Shen N. Kilburn D. Rioux J. Nusbaum C. Rozen S. Hudson T.J. Lander E.S. Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome.Science. 1998; 280: 1077-1082Google Scholar), and DNA sequencing (9.Pease A.C. Solas D. Sullivan E.J. Cronin M.T. Holmes C.P. Fodor S.P. Light-generated oligonucleotide arrays for rapid DNA sequence analysis.Proc. Natl. Acad. Sci. U. S. A. 1994; 91: 5022-5026Google Scholar). Recent studies have focused on classification or identification of subgroups of lung tumors using DNA microarrays (10.Bhattacharjee A. Richards W.G. Staunton J. Li C. Monti S. Vasa P. Ladd C. Beheshti J. Bueno R. Gillette M. Loda M. Weber G. Mark E.J. Lander E.S. Wong W. Johnson B.E. Golub T.R. Sugarbaker D.J. Meyerson M. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 13790-13795Google Scholar, 11.Giordano T.J. Shedden K.A. Schwartz D.R. Kuick R. Taylor J.M.G. Lee N. Misek D.E. Greenson J.K. Kardia S.L.R. Beer D.G. Rennert G. Cho K.R. Gruber S.B. Fearon E.R. Hanash S. Organ-specific molecular classification of lung, colon and ovarian adenocarcinomas using gene expression profiles.Am. J. Pathol. 2001; 159: 1231-1238Google Scholar). The use of mRNA expression patterns by themselves, however, is insufficient for understanding the expression of protein products, as additional post-transcriptional mechanisms, including protein translation, post-translational modification, and degradation, may influence the level of a protein present in a given cell or tissue. Proteomic analyses, a complementary technology to DNA microarrays for monitoring gene expression, involves protein separation and quantitative assessment of protein spots using 2D 1The abbreviations used are: 2D, two-dimensional; MALDI-MS, matrix-assisted laser desorption/ionization mass spectrometry. -PAGE and protein identification using mass spectrometry. By combining proteomic and transcriptional analyses of the same samples, however, it may be possible to understand the complex mechanisms influencing protein expression in human cancer. In this study, we determined mRNA and protein levels for 165 proteins (98 genes) in 76 lung adenocarcinomas and nine non-neoplastic lung tissues. Protein levels were determined using quantitative 2D-PAGE analysis, and the separated protein polypeptides were identified using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). The corresponding mRNA levels for the identified proteins within the same samples were determined using oligonucleotide microarrays. Correlation analyses showed that protein abundance is likely a reflection of the transcription for a subset of proteins, but translation and post-translational modifications also appear to influence the expression levels of many individual proteins in lung adenocarcinomas. Fifty-seven stage I and 19 stage III lung adenocarcinomas, as well as nine non-neoplastic lung tissue samples, were used for protein and mRNA analyses. Patient consent was obtained, and the project was approved by the Institutional Review Board. All tissues were obtained after resection at the University of Michigan Health System between May 1991 and July 1998. Tissues were all snap-frozen in liquid nitrogen and then stored at −80°C. The patients included 46 females and 30 males ranging in age from 40.9 to 84.6 (average 63.8) years. Most patients (66/76) demonstrated a positive smoking history. Sixty-one tumor samples were classified as bronchial-derived, 14 were classified as bronchoalveolar, and one had both features. Eighteen tumor samples were classified as well differentiated, 38 were classified as moderate, and 19 were classified as poorly differentiated adenocarcinomas. Hematoxylin-stained cryostat sections (5 μm), prepared from the same tumor pieces to be utilized for protein and mRNA isolation, were evaluated by a pathologist and compared with hematoxylin- and eosin-stained sections made from paraffin blocks of the same tumors. Specimens were excluded from analysis if they showed unclear or mixed histology (e.g. adenosquamous), tumor cellularity less than 70%, potential metastatic origin as indicated by previous tumor history, extensive lymphocytic infiltration, or fibrosis or if the patient had received prior chemotherapy or radiotherapy. The HuGeneFL oligonucleotide arrays (Affymetrix, Santa Clara, CA) containing 6800 genes were used in this study. Total RNA was isolated from all samples using Trizol reagent (Invitrogen). The resulting RNA was then subjected to further purification using RNeasy spin columns (Qiagen). Preparation of cRNA, hybridization, and scanning of the HuGeneFL arrays were performed according to the manufacturer's protocol (Affymetrix, Santa Clara, CA). Data analysis was performed using GeneChip 4.0 software. The gene expression profile of each tumor was normalized to the median gene expression profile for the entire sample. Details of data trimming and normalization are described elsewhere (11.Giordano T.J. Shedden K.A. Schwartz D.R. Kuick R. Taylor J.M.G. Lee N. Misek D.E. Greenson J.K. Kardia S.L.R. Beer D.G. Rennert G. Cho K.R. Gruber S.B. Fearon E.R. Hanash S. Organ-specific molecular classification of lung, colon and ovarian adenocarcinomas using gene expression profiles.Am. J. Pathol. 2001; 159: 1231-1238Google Scholar). Tissue for both protein and mRNA isolation came from contiguous areas of each sample. Protein separation using 2D-PAGE, silver staining, and digitization were performed as described previously (12.Strahler J.R. Kuick R. Hanash S.M. Creighton T. Protein Structure: A Practical Approach. IRL Press, Oxford1989: 65-92Google Scholar, 13.Merril C.R. Dunau M.L. Goldman D. A rapid sensitive silver stain for polypeptides in polyacrylamide gels.Anal. Biochem. 1981; 101: 201-207Google Scholar). Our 2D-PAGE system allows us to run 20 gels at one time (one batch). Spot detection and quantification were accomplished utilizing Bio Image Visage System software (Bioimage Corp., Ann Arbor, MI). The integrated intensity of each spot was calculated as the measured optical density units × mm2. Of the total possible 2000 spots detectable on each gel, 820 spots on the gel of each sample were matched using a Gel-ed match program with the same spots on a chosen "master" gel. In each sample, 250 ubiquitously expressed reference spots were used to adjust for variations between gels, such as that created by subtle differences in protein loading or gel staining. Slight differences because of batch were corrected after spot-size quantification. Preparative 2D gels were run using extracts from A549 lung adenocarcinoma cells (obtained from ATCC) and using the identical experimental conditions as the analytical 2D gels, except 30% more protein was loaded. The resolved protein gels were silver-stained using successive incubations in 0.02% sodium thiosulfate for 2 min, 0.1% silver nitrate for 40 min, and 0.014% formaldehyde plus 2% sodium carbonate for 10 min. For protein identification, protein polypeptides underwent trypsin digestion followed by MALDI-MS using a MALDI-TOF Voyager-DE mass spectrometer (Perseptive Biosystems, Framingham, MA). The masses were compared with known trypsin digest databases using the MS-FIT database (University of California, San Francisco; prospector.ucsf.edu/ucsfhtml3.2/msfit.htm). Some of the polypeptides included in the analysis had been identified prior to this study on the basis of sequencing (14.Hanash S.M. Strahler J.R. Chan Y. Kuick R. Teichroew D. Neel J.V. Hailat N. Keim D.R. Gratiot-Deans J. Ungar D. Richardson B.C. Data base analysis of protein expression patterns during T-cell ontogeny and activation.Proc. Natl. Acad. Sci. U. S. A. 1993; 90: 3314-3318Google Scholar). The identified protein spots used in this paper are shown in Fig. 1A. The method for 2D-PAGE Western blot verification was as described previously (15.Brichory F.M. Misek D.E. Yim A.M. Krause M.C. Giordano T.J. Beer D.G. Hanash S.M. An immune response manifested by the common occurrence of annexins I and II autoantibodies and high circulating levels of IL-6 in lung cancer.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 9824-9829Google Scholar). The 2D Western blots of GRP58 and Op18 are shown in Fig. 1, C and E; the others, such as GRP78, GRP75, HSP70, HSC70, KRT8, KRT18, KRT19, Vimentin, ApoJ, 14–3-3, Annexin I, Annexin II, PGP9.5, DJ-1, GST-pi, and PGAM, are described elsewhere. 2Chen et al., submitted for publication. Missing values were replaced with the mean value of the protein spot. The transform x → log (1 + x) was applied to normalize all protein expression values. The relationship between protein and mRNA expression levels within the same samples was examined using the Spearman correlation coefficient analysis (16.Lavens-Phillips S.E. MacGlashan Jr., D.W. The tyrosine kinases p53/56lyn and p72syk are differentially expressed at the protein level but not at the messenger RNA level in nonreleasing human basophils.Am. J. Respir. Cell Mol. Biol. 2000; 23: 566-571Google Scholar). To identify potentially significant correlations between gene and protein expression, we used an analytical strategy similar to SAM (significance analysis of microarrays) (17.Tusher V.G. Tibshirani R. Chu G. Significance analysis of microarrays applied to the ionizing radiation response.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 5116-5121Google Scholar), which uses a permutation technique to determine the significance of changes in gene expression between different biological states. To obtain permuted correlation coefficients between gene and protein expression, genes were exchanged first in such a way that permutated correlation coefficient were calculated based on pseudo pairs of genes and proteins. The distribution of permutated correlation coefficients became stable after 60 permutations. This procedure was then repeated 60 times to obtain 60 sets of permutated correlation coefficients. For each of the 60 permutations, the correlations of genes and proteins were ranked such that ρp(i) denotes the ith largest correlation coefficient for pth permutation. Hence, the expected correlation coefficient, ρE(i), was the average over the 60 permutations, ρE(i)=∑p60=1ρp(i)/60. A scatter plot of observed correlations (ρ(i)) versus the expected correlations is shown in Fig. 2D. For this study, we chose threshold Δ = 0.115 so that correlation would be considered significant if absolute value of difference between ρ(i) and ρE(i) was greater than the threshold. Twenty-nine (including one with observed correlation coefficient −0.4672) of 165 pairs of gene and protein expression were called significant in such criteria, and the permuted data generated an average of 5.1 falsely significant pairs of gene and protein expression. This provided an estimated false discovery rate (the percentage of pairs of gene and protein expression identified by chance) for our data set. We have examined quantitatively 165 protein spots on 2D gels representing 98 genes and compared protein levels with mRNA levels for a cohort of 85 lung adenocarcinomas and normal lung samples. Of the 165 protein spots, 69 proteins were represented by only one known spot on 2D gels for an individual gene, whereas 96 protein spots showed multiple protein products from 29 different genes. 2D Western blotting verified the proteins identified by mass spectrometry when specific antibodies were available. Spearman correlation coefficients of the proteins and their associated mRNA for each protein spot were generated using all 76 lung adenocarcinomas and nine non-neoplastic lung tissues (see Tables I and II, and see Figs. 1 and 2). The correlation coefficients (r) ranged from −0.467 to 0.442 (Fig. 2D). A total of 28 protein spots (21 genes) were found to have a statistically significant correlation between expression of their protein and mRNA (r > 0.2445; p < 0.05). This accounts for 17% (28/165) of the 165 protein spots. Among the 69 genes for which only a single protein spot was known (Table I), nine genes (9/69, 13%) were observed to show a statistically significant relationship between protein and mRNA abundance (r > 0.2445; p < 0.05). The proteins whose expression levels were correlated with their mRNA abundance included those involved in signal transduction, carbohydrate metabolism, apoptosis, protein post-translational modification, structural proteins, and heat shock proteins (Table III).Table ICorrelation coefficients of protein and mRNA where only one spot was present on 2D gelsSpotUnigeneGene namer*Protein name1104Hs.184510SFN0.433714-3-3 ς0994Hs.77840ANXA40.4219Annexin IV1314Hs.10958DJ-10.3982DJ-1 protein/MER51454Hs.75428SOD10.3863Superoxide dismutase (Cu-Zn)1638Hs.227751LGALS10.3318Galectin 10264Hs.129548HNRPK0.3034Transformation up-regulated nuclear protein1405Hs.111334FTL0.2849Ferritin light chain0963Hs.300711ANXA50.2468Annexin V1252Hs.4745PSMC0.244526 S proteasome p280906Hs.234489LDHB0.4420L-lactate dehydrogenase H chain (LDH-B)1171Hs.241515COX110.2310COX 111160Hs.181013PGAM10.2023Phosphoglycerate mutase0759Hs.74635DLD0.1965Dihydrolipoamide dehydrogenase precursor1193Hs.83383AOE3720.1932Antioxidant enzyme AOE3720172Hs.3069HSPA9B0.1872GRP750777Hs.979PDHB0.1855Pyruvate dehydrogenase E1-β subunit precursor1249Hs.226795GSTP10.1773Glutathione S-transferase pi (GST-pi)1685Hs.76136TXN0.1732Thioredoxin1205Hs.82314HPRT10.1588HG phosphoribosyltransferase1230Hs.279860TPT10.1466Translationally controlled tumor protein (TCTP)0603Hs.181357LAMR10.1463LAMR1358Hs.28914APRT0.1399Adenine phosphoribosyl transferase1410Hs.82113DUT0.1213dUTP pyrophosphatase (dUTPase)1825Hs.112378LIMS10.1213Pinch-2 protein0871Hs.250502CA80.1122Carbonic anhydrase-related protein; Syntaxin0289Hs.82916CCT6A0.1106Chaperonin-like protein1143Hs.11465GSTTLp280.0997Glutathione S-transferase homolog (GST homolog)1456Hs.118638NME10.0932Nm23 (NDPKA)1598Hs.278503RIG0.0905RIIG (U32331)1354Hs.89761ATP5D0.0904FIFO-type ATP synthase subunit d1445Hs.155485HIP20.0843Huntingtin interacting protein 2 (HIP2)1479Hs.177486APP0.0746Amyloid B4A0608Hs.182265KRT190.0439Cytokeratin 191071Hs.10842RAN0.0277GTP-binding nuclear protein RAN(TC4)0991Hs.297939CTSB0.0254Cathepsin B0842Hs.77274PLAU0.0248Urokinase plasminogen activator0823Hs.198248B4GALT10.0183β 1,4-galactosyl transferase0613Hs.1247APOA40.0176Apolipoprotein A4 (ApoA4)1338Hs.104143CLTA0.0123Clathrin light chain A0902Hs.5123SID6–3060.0117Cytosolic inorganic pyrophosphatase1688Hs.1473GRP−0.0040Preprogastrin-releasing peptide0265Hs.274402HSPA1B−0.0071Heat shock-induced protein1414Hs.77541ARF5−0.0096ADP-ribosylation factor 10710Hs.97206HIP1−0.0114Huntingtin interacting protein 1 (HIP1)0532Hs.170328MSN−0.0132Moesin/E0525Hs.284255ALPP−0.0148Alkaline phosphate, placental0513Hs.76901PDIR−0.0289Protein disulfide isomerase-related protein 51659Hs.256697HINT−0.0312Protein kinase C inhibitor1262Hs.7016RAB7−0.0362Rab 7 protein0190Hs.184411ALB−0.0470Albumin0948Hs.2795LDHA−0.0549Lactate dehydrogenase-A (LDHA)0502Hs.180532GPI−0.0575Hsp890152Hs.75410HSPA5−0.0640GRP781054Hs.74276CLIC1−0.0686Nuclear chloride channel (RNCC protein)0709Hs.253495SFTPD−0.0936Pulmonary surfactant protein D0867Hs.78996PCNA−0.0982PCNA0165Hs.180414HSPA8−0.1014Heat shock cognate protein, 71 kDa1109Hs.75103YWHAZ−0.101814-3-3 ζ/Δ0137Hs.554SSA2−0.1032Ro/ss-A antigen0278Hs.4112TCP1−0.1237T-complex protein I, α subunit1769Hs.9614NPM1−0.1738B23/numatrin0089Hs.74335HSPCB−0.2049Hsp902511Hs.153179FABP5−0.2109E-FABP/FABP51739Hs.16488CALR−0.2344Calreticulin 321138Hs.301961GSTM4−0.2438Glutathione S-transferase M4 (GST m4)2533Hs.77060PSMB6−0.2512Macropain subunit Δ Open table in a new tab Table IICorrelation coefficients of protein and mRNA where multiple isoforms were present on 2D gelsSpotUnigeneGene namer*Protein name1494Hs.81915LAP180.4003OP18 (Stathmin)0957Hs.77899TPM10.3930Tropomyosins 1–50353Hs.289101GRP580.3802Protease disulfide isomerase (GRP58)0855Hs.169476GAPD0.3693Glyceraldehyde-3-phosphate dehydrogenase1198Hs.41707HSPB30.3668Hsp271203Hs.83848TPI10.3395Triose phosphate isomerase (TPI)0523Hs.65114KRT180.3335Cytokeratin 181492Hs.81915LAP180.3234OP18 (Stathmin)1493Hs.81915LAP180.3154OP18 (Stathmin)1181Hs.78225ANXA10.3102Annexin variant I0439Hs.242463KRT80.3049Cytokeratin 80505Hs.297753VIM0.2939Vimentin0593Hs.297753VIM0.2809Vimentin1874Hs.75313AKR1B10.2790Aldose reductase0935Hs.75544YWHAH0.277514-3-3 η2524Hs.78225ANXA10.2612Annexin I2324Hs.65114KRT180.2601Cytokeratin 181192Hs.41707HSPB30.2558Hsp270350Hs.289101GRP580.2516Phospholipase C (GRP58)0992Hs.75313AKR1B1−0.2460Aldose reductase0861Hs.75313AKR1B10.0761Aldose reductase0853Hs.75313AKR1B1−0.0675Aldose reductase2503Hs.76392ALDH1−0.0565Aldehyde dehydrogenase0381Hs.76392ALDH1−0.0371Aldehyde dehydrogenase0371Hs.76392ALDH1−0.0680Aldehyde dehydrogenase1179Hs.78225ANXA10.2052Annexin variant I0762Hs.78225ANXA1−0.0739Annexin I0760Hs.78225ANXA1−0.0228Annexin I2506Hs.217493ANXA20.2223Lipocotin (annexin II)0772Hs.217493ANXA20.2080Lipocotin (annexin II)0723Hs.217493ANXA20.0701Lipocotin1239Hs.93194APOA10.1133Apolipoprotein A1 (ApoA1)1237Hs.93194APOA1−0.0373Apolipoprotein A1 (ApoA1)1234Hs.93194APOA1−0.0894Apolipoprotein A1 (ApoA1)0428Hs.25ATP5B0.0080ATP synthase β subunit precursor0427Hs.25ATP5B0.0122ATP synthase β subunit precursor0424Hs.25ATP5B−0.0992ATP synthase β subunit precursor0863Hs.75106CLU−0.0483Apolipoprotein J (ApoJ)0780Hs.75106CLU−0.0443Apolipoprotein J (ApoJ)1527Hs.119140EIF5A−0.0726eIF-5A1484Hs.119140EIF5A−0.0376eIF-5A1728Hs.5241FABP1−0.1916L-FABP1712Hs.5241FABP1−0.0473L-FABP0947Hs.169476GAPD0.1745Glyceraldehyde-3-phosphate dehydrogenase1232Hs.75207GLO10.2249Glyoxalase-I1229Hs.75207GLO10.0450Glyoxalase-11595Hs.158300HAP1−0.0137Huntingtin-associated protein 1 (neuroan 1)1810Hs.75990HP−0.4672α-Haptoglobin1459Hs.75990HP0.0802α-Haptoglobin1458Hs.75990HP−0.0305α-Haptoglobin0619Hs.75990HP0.0461B-haptoglobin0615Hs.75990HP−0.0034B-haptoglobin1250Hs.41707HSPB3−0.1024Hsp270549Hs.79037HSPD10.1074Hsp600338Hs.79037HSPD10.2265Hsp600333Hs.79037HSPD10.1383Hsp600331Hs.79037HSPD10.1603Hsp602381Hs.65114KRT180.2016Cytokeratin 180535Hs.65114KRT180.1106Cytokeratin 180529Hs.65114KRT180.1279Cytokeratin 180528Hs.65114KRT180.0414Cytokeratin 180527Hs.65114KRT180.0436Cytokeratin 180514Hs.65114KRT180.0733Cytokeratin 180451Hs.242463KRT8−0.0111Cytokeratin 80446Hs.242463KRT80.0347Cytokeratin 80444Hs.242463KRT8−0.1311Cytokeratin 80443Hs.242463KRT80.0942Cytokeratin 81488Hs.81915LAP180.0495OP18 (Stathmin)0321Hs.75655P4HB−0.0546PDI (proly-4-OH-B)0320Hs.75655P4HB−0.0041PDI (proly-4-OH-B)1063Hs.75323PHB0.0441Prohibitin0837Hs.75323PHB0.1402Prohibitin0326Hs.297681SERPINA1−0.0227α-1-Antitripsin0322Hs.297681SERPINA1−0.0277α-1-Antitripsin0241Hs.297681SERPINA1−0.0148α-1-Antitripsin1280Hs.301254SFTPA1−0.1488Pulmonary surfactant-associated protein1278Hs.301254SFTPA1−0.2040Pulmonary surfactant-associated protein0866Hs.73980TNNT10.1162Troponin T0778Hs.73980TNNT10.0740Troponin T1213Hs.83848TPI10.0024Triose phosphate isomerase (TPI)1210Hs.83848TPI10.0490Triose phosphate isomerase (TPI)1207Hs.83848TPI1−0.1615Triose phosphate isomerase (TPI)1204Hs.83848TPI10.0209Triose phosphate isomerase (TPI)1202Hs.83848TPI10.0721Triose phosphate isomerase (TPI)1161Hs.83848TPI10.2265Triose phosphate isomerase (TPI)1052Hs.77899TPM1−0.1040Tropomysin clean-product1039Hs.77899TPM1−0.2999Cytoskeletal tropomyosin1035Hs.77899TPM1−0.3821Tropomyosin0783Hs.77899TPM10.0757Tropomyosins 1–51574Hs.194366TTR−0.0065Transthyretin0809Hs.194366TTR0.0399Transthyretin multimere2202Hs.76118UCHL1−0.0220Ubiquitin carboxyl-terminal hydrolase isozyme L11246Hs.76118UCHL1−0.1261Ubiquitin carboxyl-terminal hydrolase isozyme L11242Hs.76118UCHL10.1473Ubiquitin carboxyl-terminal hydrolase isozyme L10606Hs.297753VIM0.0951Vimentin0594Hs.297753VIM−0.2664Vimentin-derived protein (vid4)0508Hs.297753VIM0.1008Vimentin-derived protein (vid2)0419Hs.297753VIM0.0032Vimentin-derived protein (vid1)1279Hs.75544YWHAH0.005914-3-3 η Open table in a new tab Table IIIStage-dependent analysis of protein-mRNA correlation coefficientsSpotGene namer (Stage I)r (Stage III)Function1874AKR1B10.2690.106Carbohydrate metabolism; electron transporter2524ANXA10.1840.572Phospholipase inhibitor; signal transduction0994ANXA40.6600.362Phospholipase inhibitor0963ANXA50.2410.390Phospholipase inhibitor; calcium binding; phospholipid binding1314DJ-10.3630.354Signal transduction1405FTL0.1260.358Iron storage protein0855GAPD0.2430.581Carbohydrate metabolism (glycolysis regulation)0350GRP580.327−0.087Signal transduction; protein disulfide isomerase0264HNRPK0.3600.243RNA-binding protein (RNA processing/modification)1192HSPB30.4570.633Heat shock protein0523KRT180.1150.371Structural protein0439KRT80.3230.436Structural protein1492LAP180.4830.663Signal transduction; cell growth and maintenance1638LGALS10.2000.528Apoptosis; cell adhesion; cell size co31252PSMC0.2530.060Protein degradation1104SFN0.4650.475Signal transduction (protein kinase C inhibitor)1454SOD10.3520.079Oxidoreductase1203TPI10.3780.009Carbohydrate metabolism0957TPM10.4750.225Structural protein (muscle); control of heart0593VIM−0.0540.556Structural protein0935YWHAH0.2830.210Signal transduction Open table in a new tab Of the 165 protein spots, 96 represent protein products of 29 genes with at least two isoforms. Among these 96 protein spots, 19 (19/96 protein spots, 20%) showed a statistically significant correlation between their protein and mRNA expression (r > 0.2445; p < 0.05) (Table II) and represented 12 genes (12/29, 41%). Individual isoforms of the same protein demonstrated different protein/mRNA correlation coefficients. For example, 2D-PAGE/Western analysis revealed four isoforms of OP18 differing in regards to isoelectric point but similar in molecular weight. Three of the four isoforms (spots 1492, 1493, and 1494) showed a statistically significant correlation between their protein and mRNA abundance (r = 0.3234, 0.3154, and 0.4003, respectively). The forth isoform (spot 1488) showed no correlation between protein and mRNA expression (r = 0.0495). Similarly, just one of five quantified isoforms of cytokeratin 8 (spot 439) demonstrated a statistically significant correlation between protein and mRNA abundance (r = 0.3049; p < 0.05) (Table II). In addition to differences in the relationship between mRNA levels and protein expression among separate isoforms, some genes with very comparable mRNA levels showed a 24-fold difference in their protein expression. Genes with comparable protein expression levels also showed up to a 28-fold variance in their mRNA levels. The relationship between mRNA and protein expression was also examined by using the average expression values for all samples. To analyze this relationship using this approach, the average value for each protein or mRNA was generated using all 85 lung tissue samples. The range of normalized average protein values ranged from −0.0646 to 0.0979 (raw value 0.0036 to 4.1947), and the range for mRNA was from 0 to 15260.5 for all 165 individual protein spots. The Spearman correlation coefficient for the whole data set (165 protein spots/98 genes) was −0.025 (Fig. 3A). Even for the 28 protein spots (Fig. 2D) that were found to have a statistically significant correlation between their mRNA and protein, use of the average value resulted in a correlation coefficient value of −0.035, which was not significant (Fig. 3B). To determine whether an absolute protein level might influence the correlation with mRNA, the mean value of each protein (relative abundance) and the Spearman protein/mRNA correlation coefficients among all 85 samples were examined. No relationship between the protein abundance and the correlation coefficients was observed (r = 0.039; p > 0.05). A detailed analysis of separate subsets of proteins with differing levels of abundance (less than −0.0014, larger than −0.0014, or larger than 0.0077) also showed a lack of correlation between mRNA and protein expression among the 83 (50%), 82 (50%), and 41 (25%) of 165 total protein spots, respectively (r = 0.016, 0.08, and 0.172, respectively). To determine whether the 21 genes (28 protein spots) showing a significant correlation between the protein and mRNA expression among all samples demonstrate changes in this relationship during tumor progression, the correlations were examined separately for stage I (n = 57) and stage III (n = 19) lung adenocarcinomas (Table III). The number of non-neoplastic lung samples (n = 9) was insufficient for a separate correlation analysis of this group. Many of the protein spots represent one of several known protein isoforms for a given gene. The majority of genes (16/21) did not differ in the protein/mRNA correlation between stage I and stage III tumors indicating a similar regulatory relationship between the mRNA and protein spot. GRP-58, PSMC, SOD1, TPI1, and VIM, however, were found to demonstrate significant differences in the correlation coefficients between stage I and stage III lung adenocarcinomas. For GRP-58, PSMC, and VIM the change in the correlation coefficient was because of a relative increase in protein expression in stage III tumors. For SOD and TPI the change resulted from a relative decrease in expression of this specific protein in stage III tumors. Relatively little is known about the regulatory mechanisms controlling the complex patterns of protein abundance and post-translational modification in tumors. Most reports concerning the regulation of protein translation have focused on one or several protein products (18.Tew K.D. Monks A. Barone L. Rosser D. Akerman G. Montali J.A. Wheatley J.B. Schmidt Jr., D.E. Glutathione-associated enzymes in the human cell lines of the National Cancer Institute Drug Screening Program.Mol. Pharmacol. 1996; 50: 149-159Google Scholar). Celis et al. (19.Celis J.E. Kruhoffer M. Gromova I. Frederiksen C. Ostergaard M. Thykjaer T. Gromov P. Yu J. Palsdottir H. Magnusson N. Orntoft T.F. Gene expression profiling: monitoring transcription and translation products using DNA microarrays and proteomics.FEBS Lett. 2000; 480: 2-16Google Scholar) found a good correlation between transcript and protein levels among 40 well resolved, abundant proteins using a proteomic and microarray study of bladder cancer. By comparing the mRNA and protein expression levels within the same tumor samples, we found that 17% (28/165) of the protein spots (21/98 genes) show a statistically significant correlation between mRNA and protein. These proteins appear to represent a diverse group of gene products and include those involved in signal transduction, carbohydrate metabolism, protein modification, cell structure, heat shock, and apoptosis. These results suggest that expression of this subset of 165 proteins is likely to be regulated at the transcriptional level in these tissues. The majority of the protein isoforms, however, did not correlate with mRNA levels, and thus their expression is regulated by other mechanisms. We also observed a subset of proteins that demonstrated a negative correlation with the mRNA expression values; for example α-haptoglobin demonstrated a strong negative correlation with its mRNA expression values. This may reflect negative feedback on the mRNA or the protein or the presence of other regulatory influences that are not understood currently. Post-translational modification or processing will result in individual protein products of the same gene migrating to different locations on 2D-PAGE gels (20.Anderson N.L. Anderson N.G. Proteome and proteomics: new technologies, new concepts, and new words.Electrophoresis. 1998; 19: 1853-1861Google Scholar). Because the identity of all possible isoforms for each protein examined has not been characterized completely, this may influence the correlation analyses performed in this study. This is partly because of limitations of the 2D-PAGE and mass spectrometry technologies (21.Gygi S.P. Corthals G.L. Zhang Y. Rochon Y. Aebersold R. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology.Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 9390-9395Google Scholar, 22.Fey S.J. Larsen P.M. 2D or not 2D. Two-dimensional gel electrophoresis.Curr. Opin. Chem. Biol. 2001; 5: 26-33Google Scholar). Potential inconsistencies between mRNA and protein correlations that have been reported may also be because of differences, even in the same gene, in the mechanisms of protein translation among different cells or as measured in different laboratories (23.McBride S. Walsh D. Meleady P. Daly N. Clynes M. Bromodeoxyuridine induces keratin protein synthesis at a posttranscriptional level in human lung tumor cell lines.Differentiation. 1999; 64: 185-193Google Scholar). In this study, we examined 165 protein spots identified in lung adenocarcinomas. Ninety-six protein spots, representing the products of 29 genes, contained at least two protein isoforms. Nineteen of 96 protein spots, representing 12 genes, were shown to have a statistically significant correlation between their protein and mRNA expression, suggesting that the levels of these proteins reflects the transcription of the corresponding genes. Differences in protein/mRNA correlations were found among the individual isoforms of a given protein. For example, of the four OP18 isoforms, three showed a statistically significant correlation between the protein and mRNA expression levels. The lack of relationship for the one isoform, however, indicates that individual protein isoforms of the same gene product can be regulated differentially. This is not unexpected and likely reflects other post-translational mechanisms that can influence isoform abundance in tissues and cancer. In addition to the analyses of the correlation of mRNA/protein within the same tumor samples, we also tested the global relationship between mRNA and the corresponding protein abundance across all 165 protein spots in the lung samples. A protein and mRNA average value for each gene was generated using all 85 lung tissues samples. We observed a very wide range of normalized average protein and mRNA values. The correlation coefficient generated using this average value data set was −0.025, and even for the 28 protein spots that showed a statistically significant correlation between individual mRNA and proteins, the correlation value was only −0.035. This suggests that it is not possible to predict overall protein expression levels based on average mRNA abundance in lung cancer samples. This conclusion is also supported by previous results from Anderson and Seilhamer (24.Anderson L. Seilhamer J. A comparison of selected mRNA and protein abundances in human liver.Electrophoresis. 1997; 18: 533-537Google Scholar), who examined 19 genes in human liver cells, and by Gygi et al. (25.Gygi S.P. Rochon Y. Franza B.R. Aebersold R. Correlation between protein and mRNA abundance in yeast.Mol. Cell. Biol. 1999; 19: 1720-1730Google Scholar), who examined 106 genes in yeast. Both studies found a lack of correlation between mRNA and protein expression when average or overall levels were used. A good correlation was reported when the 11 most abundant proteins were examined in yeast (25.Gygi S.P. Rochon Y. Franza B.R. Aebersold R. Correlation between protein and mRNA abundance in yeast.Mol. Cell. Biol. 1999; 19: 1720-1730Google Scholar), suggesting that the level of protein abundance may be a factor that may influence the correlation between mRNA and protein. In the present study, a fairly wide range of mean protein values among 165 protein spots in lung adenocarcinomas was observed, and the correlation coefficients also varied from −0.467 to 0.442. A comparison between the mean value of each protein and the correlation coefficient generated using all 85 tissue samples did not reveal a strong relationship between the overall protein abundance and the correlation coefficients (r = 0.039; p > 0.05). Detailed analysis of different subsets of protein abundance also failed to show a correlation between mRNA and protein expression. Thus in contrast to yeast, a relationship between mRNA/protein correlation coefficient and protein abundance in human lung adenocarcinomas was not observed. The results of this study indicate that the level of protein abundance in lung adenocarcinomas is associated with the corresponding levels of mRNA in 17% (28 proteins) of the total 165 protein spots examined. This was substantially higher than the amount predicted to result by chance alone (which was 5.1) and suggests that a transcriptional mechanism likely underlies the abundance of these proteins in lung adenocarcinomas. We also demonstrate that the expression of individual isoforms of the same protein may or may not correlate with the mRNA, indicating that separate and likely post-translational mechanisms account for the regulation of isoform abundance. These mechanisms may also account for the differences in the correlation coefficients observed between stage I and stage III tumors, indicating that specific protein isoforms show regulatory changes during tumor progression. Further studies in lung adenocarcinomas will examine the relationship between the expression of individual protein isoforms and specific clinical-pathological features of these tumors, such as the presence of angiolymphatic invasion, and nodal or pleural surface involvement. The potential to identify specific protein isoforms associated with biological behavior in lung adenocarcinomas would be of considerable interest and will add to our understanding of the regulation of gene products by transcriptional, translational, and post-translational mechanisms. We thank Kerby A. Shedden, Rork D. Kuick, Eric Puravs, Robert Hinderer, Melissa C. Krause, and Christopher Wood for assistance in this study.