Discordant Protein and mRNA Expression in Lung Adenocarcinomas
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% for silver for and for For protein identification, protein polypeptides by using a mass The were compared with using the of of the polypeptides included in the analysis had been identified prior to this on the of sequencing S.M. J.R. Y. Kuick R. D. N. D.R. J. D. Data analysis of protein expression patterns and Natl. Acad. Sci. U. S. A. Scholar). The identified protein spots used in this are in The for 2D-PAGE was as described previously Misek D.E. T.J. Beer D.G. Hanash S.M. by the common of I and and high levels of in lung Natl. Acad. Sci. U. S. A. 2001; 98: Scholar). The 2D of and are in and the such as and are described for values were with the of the protein The was applied to all protein expression The relationship between protein and mRNA expression levels within the same samples was using the correlation coefficient analysis Jr., The and are expressed at the protein level but not at the RNA level in human J. Mol. 2000; Scholar). identify potentially significant between gene and protein expression, we used analytical to analysis of R. G. analysis of microarrays applied to the Natl. Acad. Sci. U. S. A. 2001; 98: Scholar), a to determine the of in gene expression between correlation between gene and protein expression, genes were in such a that correlation coefficient were calculated on of genes and The of correlation after was then to of correlation For each of the the of genes and proteins were such that the correlation coefficient for the correlation was the average the A of the is in For this study, we = that correlation be significant if of between and was than the one with correlation coefficient of 165 of gene and protein expression were significant in such and the data average of significant of gene and protein of of gene and protein expression identified by for data have 165 protein spots on 2D gels representing 98 genes and compared protein levels with mRNA levels for a cohort of 85 lung adenocarcinomas and lung Of the 165 protein proteins were by only one spot on 2D gels for individual protein spots showed multiple protein from 2D the proteins identified by mass spectrometry were correlation of the proteins and their mRNA for each protein spot were using all 76 lung adenocarcinomas and nine non-neoplastic lung tissues I and and and The correlation (r) ranged from −0.467 to A total of protein spots genes) were found to have a statistically significant correlation between expression of their protein and mRNA (r > 0.2445; p < for of the 165 protein spots. Among the genes for only a protein spot was nine genes were to a statistically significant relationship between protein and mRNA abundance (r > 0.2445; p < The proteins expression levels were with their mRNA abundance included in protein post-translational modification, proteins, and proteins of protein and mRNA only one spot was present on 2D tumor protein protein protein protein protein protein protein, protein in a new of protein and mRNA multiple were present on 2D protein protein protein protein in a new analysis of correlation protein protein cell and cell cell protein of in a new Of the 165 protein protein of genes with at Among these protein 19 protein showed a statistically significant correlation between their protein and mRNA expression (r > 0.2445; p < and genes of the same protein demonstrated correlation For analysis of in to but in molecular of the and showed a statistically significant correlation between their protein and mRNA abundance (r = and The showed no correlation between protein and mRNA expression (r = one of five of demonstrated a statistically significant correlation between protein and mRNA abundance (r = p < In to differences in the relationship between mRNA levels and protein expression among separate isoforms, genes with mRNA levels showed a in their protein with protein expression levels also showed to a in their mRNA The relationship between mRNA and protein expression was also by using the average expression values for all this relationship using this the average for each protein or mRNA was using all 85 lung tissue The of normalized average protein values ranged from to to and the for mRNA was from to for all 165 individual protein spots. The correlation coefficient for the data protein genes) was for the protein spots that were found to have a statistically significant correlation between their mRNA and protein, use of the average in a correlation coefficient of was not significant determine protein level influence the correlation with the of each protein and the correlation among all 85 samples were relationship between the protein abundance and the correlation was (r = p > A analysis of separate of proteins with levels of abundance than than or than also showed a of correlation between mRNA and protein expression among the and of 165 total protein (r = and determine the 21 genes protein a significant correlation between the protein and mRNA expression among all samples in this relationship tumor the were for stage I = and stage III = lung adenocarcinomas The number of non-neoplastic lung samples = was insufficient for a separate correlation analysis of this of the protein spots one of protein for a given The of genes not in the correlation between stage I and stage III tumors indicating a relationship between the mRNA and protein and however, were found to significant differences in the correlation between stage I and stage III lung adenocarcinomas. For and the in the correlation coefficient was because of a in protein expression in stage III tumors. For and the from a in expression of this protein in stage III tumors. little is the mechanisms the complex patterns of protein abundance and post-translational in tumors. Most the regulation of protein translation have focused on one or protein A. L. D. G. J.A. J.B. Jr., D.E. in the human cell of the 1996; Scholar). M. C. M. T. P. J. N. expression monitoring transcription and translation using DNA microarrays and 2000; Scholar) found a correlation between and protein levels among well proteins using a proteomic and of cancer. By the mRNA and protein expression levels within the same tumor samples, we found that of the protein spots genes) a statistically significant correlation between mRNA and proteins appear to a of gene and in protein modification, cell and that expression of this subset of 165 proteins is likely to be at the transcriptional level in these tissues. The of the protein isoforms, however, not with mRNA and their expression is by also a subset of proteins that demonstrated a correlation with the mRNA expression for demonstrated a correlation with mRNA expression may on the mRNA or the protein or the of that are not understood or in individual protein of the same gene to on 2D-PAGE gels and new new and new 1998; Scholar). the of all possible for each protein has not been characterized this may influence the correlation analyses performed in this study. is because of of the 2D-PAGE and mass spectrometry S.P. G.L. Y. Y. Aebersold R. of two-dimensional gel analysis Natl. Acad. Sci. U. S. A. 2000; Scholar, 2D or not gel Opin. 2001; Scholar). between mRNA and protein that have been may also be because of in the same in the mechanisms of protein translation among cells or as measured in S. D. P. N. M. protein at a level in human lung tumor cell Scholar). In this study, we 165 protein spots identified in lung adenocarcinomas. protein representing the of at protein of protein representing were to have a statistically significant correlation between their protein and mRNA expression, that the levels of these proteins the transcription of the corresponding in were found among the individual of a given For of the isoforms, showed a statistically significant correlation between the protein and mRNA expression The of relationship for the one however, that individual protein of the same gene can be is not and likely post-translational mechanisms that can influence abundance in tissues and cancer. In to the analyses of the correlation of mRNA/protein within the same tumor samples, we also the relationship between mRNA and the corresponding protein abundance across all 165 protein spots in the lung A protein and mRNA average for each gene was using all 85 lung tissues a of normalized average protein and mRNA The correlation coefficient using this average data was and for the protein spots that showed a statistically significant correlation between individual mRNA and proteins, the correlation was only that it is not possible to protein expression levels on average mRNA abundance in lung cancer is also by previous from and L. J. A of mRNA and protein in human Scholar), 19 genes in human and by S.P. Y. Aebersold R. Correlation between protein and mRNA abundance in Scholar), genes in studies found a of correlation between mRNA and protein expression average or levels were A correlation was the most proteins were in S.P. Y. Aebersold R. Correlation between protein and mRNA abundance in Scholar), that the level of protein abundance may be a that may influence the correlation between mRNA and In the present study, a of protein values among 165 protein spots in lung adenocarcinomas was and the correlation also varied from −0.467 to 0.442. A between the of each protein and the correlation coefficient using all 85 tissue samples not a relationship between the protein abundance and the correlation (r = p > analysis of of protein abundance also to a correlation between mRNA and protein in to a relationship between mRNA/protein correlation coefficient and protein abundance in human lung adenocarcinomas was not The of this that the level of protein abundance in lung adenocarcinomas is with the corresponding levels of mRNA in of the total 165 protein spots was than the to by was and that a transcriptional likely the abundance of these proteins in lung adenocarcinomas. also that the expression of individual of the same protein may or may not with the indicating that separate and likely post-translational mechanisms for the regulation of abundance. mechanisms may also for the differences in the correlation between stage I and stage III indicating that protein tumor studies in lung adenocarcinomas the relationship between the expression of individual protein and of these such as the of and or The potential to identify protein with in lung adenocarcinomas be of and to understanding of the regulation of gene by and post-translational A. D. C. and for in this study.