Correlation of mRNA and protein levels: Cell type-specific gene expression of cluster designation antigens in the prostate
© Pascal et al; licensee BioMed Central Ltd. 2008
Received: 07 December 2007
Accepted: 23 May 2008
Published: 23 May 2008
Expression levels of mRNA and protein by cell types exhibit a range of correlations for different genes. In this study, we compared levels of mRNA abundance for several cluster designation (CD) genes determined by gene arrays using magnetic sorted and laser-capture microdissected human prostate cells with levels of expression of the respective CD proteins determined by immunohistochemical staining in the major cell types of the prostate – basal epithelial, luminal epithelial, stromal fibromuscular, and endothelial – and for prostate precursor/stem cells and prostate carcinoma cells. Immunohistochemical stains of prostate tissues from more than 50 patients were scored for informative CD antigen expression and compared with cell-type specific transcriptomes.
Concordance between gene and protein expression findings based on 'present' vs. 'absent' calls ranged from 46 to 68%. Correlation of expression levels was poor to moderate (Pearson correlations ranged from 0 to 0.63). Divergence between the two data types was most frequently seen for genes whose array signals exceeded background (> 50) but lacked immunoreactivity by immunostaining. This could be due to multiple factors, e.g. low levels of protein expression, technological sensitivities, sample processing, probe set definition or anatomical origin of tissue and actual biological differences between transcript and protein abundance.
Agreement between these two very different methodologies has great implications for their respective use in both molecular studies and clinical trials employing molecular biomarkers.
Immunostaining and microarray analysis are techniques frequently used to characterize tissue phenotypes. Immunohistochemistry (IHC) is a method of assessing protein levels of gene expression that is based on the ability of antibodies to bind proteins expressed by cells in sections of frozen or formalin-fixed, paraffin-embedded tissues. IHC enables one to detect and localize a specific antigen to specific cell types. Gene arrays determine expression levels for thousands of genes simultaneously by detecting sequence segments or partial segments of mRNA in a sample. To fully understand the underlying mechanisms of biological processes, it is essential to determine whether observed changes in mRNA can also be seen in the translated protein, and to pinpoint what cell types are exhibiting these changes. Gene array analysis and immunostaining are powerful tools for determining gene and protein expression patterns in health and diseases. Establishing the extent of agreement between semi-quantitative immunostaining data and gene array data obtained from sorted cell populations and tissue specimens is important to account for possible discrepancies between these two very different methods.
Determining a direct relationship between protein and mRNA levels can be problematic, and previous efforts to find correlations have found variable success. A study comparing yeast proteomic and transcriptomic data showed that correlation was insufficient to predict protein expression levels from mRNA except for the most abundant proteins, suggesting that protein abundance may be a factor that influences the correlation between mRNA and protein . However, a relationship between mRNA/protein correlation coefficient and protein abundance was not observed in a study of human lung adenocarcinomas  or in a study of MMP-2, MMP-9 and TIMP-1 in human prostate cancers . For some genes, such as HER2/neu, expression levels assayed by RT-PCR, IHC and fluorescence in situ hybridization (FISH) in breast tumors show highly significant correlation among these techniques . However, studies evaluating the overall concordance between protein and RNA expression levels have found wide variability. For example, transcript and protein concordance in the LNCaP prostate cancer cell line has been reported to vary from 32%  to 83.5% . Highly significant correlations in mRNA changes and protein expression levels were found by Orntoft et al. in human carcinomas . Studies such as these suggest that external factors as well as actual biological differences between mRNA and protein abundance might affect the relationships between the two data types.
The assessment of CD24 as a potential prostate cancer biomarker through RNA expression profiling and IHC analysis in previous studies further illustrates the difficulties in directly comparing gene and protein expression levels. Normalized CD24 transcript levels showed an average 2.69-fold increase in prostate cancer as determined by qPCR  and an increase in staining intensity as determined by IHC . Two reports by Kristiansen et al. found differential CD24 gene expression in 38.5% of tumor cases as determined by Affymetrix GeneChip analysis  and 48% as examined by IHC . It is unclear if the 10% disparity between these studies has biological significance, or if a component of the measured difference is due to technical attributes of the assays. Although, based on these studies, CD24 could potentially be an important prognostic prostate cancer tissue marker, the relationship between mRNA levels and resulting protein expression remains unclear.
Our laboratory has previously characterized benign and neoplastic prostate tissues by CD phenotype [9, 12] and isolated the constituent cell types with magnetic cell sorting (MACS) for gene expression analysis . In the present study, we compared the expression levels of a panel of 58 informative CD antigens in prostate tissue scored by immunostaining with expression levels of the respective mRNA by microarray-based quantitation of MACS-sorted and laser capture microdissected (LCM) cell populations. Our objective was to characterize the relationship between protein and mRNA levels measured by these two techniques.
Immunolocalization and transcriptome data summary
Correlation of immunolocalization with array data
Correlation between immunostaining intensity and mRNA level for each prostate cell type
Correlation between immunostaining intensity and mRNA level using updated probeset definitions for each prostate cell type
Correlation between immunostaining intensity and mRNA level for LCM samples.
In this study, we analyzed the gene and protein expression of 58 informative CD antibodies in prostate tissue to determine the degree of concordance between immunohistochemical assessments of protein abundance and microarray-based measures of transcript levels. Based on previous characterization of prostate cancer by CD phenotype [13, 20], we were interested in the correlation with corresponding cell type-specific transcriptome data and the relationship between MACS-sorted cell and laser capture microdissected populations. The CD antibodies used for comparison were selected based on their previously determined cell type specificity in prostate tissue . For this study, there was good agreement between CD phenotype characterized by immunostaining and gene expression level based on 'absent' or 'present' call. Quantitative comparison of expression levels mirrored these results with correlation being lowest for stromal cells and highest for luminal cells and cancer cells. There was moderate positive correlation between measured gene expression level and immunostaining intensity for luminal, basal and cancer. There was moderate positive correlation for progenitor cells using updated probe set definitions.
Suggested CD profiling targets with good agreement between data types for prostate characterizations.
CD antigens 'present'
CD antigens 'absent'
31, 49f, 54, 58, 59, 73, 74, 90, 107a, 107b, 144
1a, 6, 10, 30, 32, 41, 43, 56, 66f, 70, 75s, 84, 91, 94, 179a, 226, 271
9, 10, 13, 26, 38, 63, 75s, 98, 107a, 107b, 147, 164
1a, 6, 30, 32, 41, 43, 49c, 50, 56, 66f, 70, 74, 84, 88, 91, 94, 104, 153, 179a, 226, 271
29, 47, 49a, 56, 59
1a, 6, 26, 30, 32, 41, 43, 50, 64, 66f, 70, 75s, 84, 91, 94, 104, 153, 179a, 271
9, 44, 47, 49b, 49f, 59, 74, 92, 95, 104, 107b, 138, 147, 271
1a, 6, 30, 32, 41, 43, 49c, 50, 56, 64, 66f, 70, 75s, 84, 91, 94, 153, 179a, 226
9, 44, 47, 49b, 49f, 59, 74, 92, 95, 138, 147
1a, 6, 30, 10, 13, 26, 30, 32, 41, 43, 49c, 50, 56, 64, 66f, 70, 84, 91, 94, 153, 179a, 226
9, 24, 26, 29, 38, 40, 46, 49b, 63, 75s, 147
10, 13, 30, 32, 41, 43, 49c, 50, 56, 62L, 66f, 84, 88, 90, 91, 94, 104, 144, 153, 179a, 226, 271
Many cross-platform studies have been conducted on DNA microarrays. These previous studies have discovered several factors that effect data comparison across different microarray platforms. In addition to our findings, updated annotation through RefSeq or UniGene databases has repeatedly been shown to increase cross-platform content and consistency [27–31]. Biological variability, and inter-laboratory variation have also been previously implicated as sources of cross-platform data discrepancies [32–34]. For this study, although conducted by two different laboratories, the correlation between the two platform types (Affymetrix and Agilent) was only different for prostate stromal cells. Therefore, it is probable that these discrepancies were due to varying sampling methods (e.g. MACS-sorting of digested tissue vs. LCM of frozen tissue sections). Additionally, discrepancy could be due to the differing tissue histology of the prostate and therefore possible differing purity for stromal vs. luminal cell types. The prostate stroma is fairly heterogenous, consisting of smooth muscle cells and fibroblasts, with embedded blood vasculature, peripheral nerves and ganglia, and tissue infiltrating white blood cells; whereas the prostate luminal layer contains luminal secretory cells and rare neuroendocrine cells.
In summary, CD molecules can be used to isolate cell populations from prostate tissue. These cell populations retain to a high degree their CD phenotype as determined by immunostaining in intact tissue. Observed discrepancies were mostly in expression levels detected by microarray and not immunohistochemistry; these differences are possibly due in part to varying sampling methods, problems related to probe and probe set identity as well as actual biological differences. It is hoped that continued improvements in cell sorting techniques and probe set development could further elucidate the relationship between mRNA levels and corresponding protein expression. In addition to comparison of immunohistochemistry and microarray data, there is further need for cross-comparison of results utilizing techniques such as in situ hybridization, western blotting and high throughput proteome analysis. Future studies analyzing mRNA and protein correlation will be required to determine the potential bias of various experimental methodologies as well as how mRNA expression differences are related to protein expression.
The methods of tissue collection, immunostaining and expression data collection and analysis used in this study have been published previously [13, 20, 35]. Briefly, tissue samples for IHC and Affymetrix array analysis consisted of both cancer-enriched and cancer-free samples obtained from 55 radical prostatectomies. Upon receipt of prostatectomy specimens, 3-mm thick transverse sections were made of the prostate after inking the exterior surface (the surgical margin). The same approach was used for both cancer-free and cancer-enriched (where at least 85% of the cells in the corresponding frozen section were cancer cells) samples. Between 4 and 6 blocks of tissue from the posterior aspect (the peripheral zone) of each prostate were frozen. Sections from the frozen blocks of tissue provided a histological template for characterizing the cell composition of the adjacent non-frozen tissue blocks. Cancer-free samples, weighing between 2 and 10 g, were harvested primarily from the anterior aspect of the prostate (transition zone) as described [12, 13, 35]. Cancer-enriched samples, weighing at least 100 mg, were dissected from the opposing aspect of the non-fixed section adjacent to the block of tissue that had been frozen. Tissue specimens for cell sorting were digested by collagenase [36, 37].
As an external control, IHC and array data from MACS-sorted cells were compared to LCM cells from prostate stromal and luminal cells analyzed by hybridization to long-oligonucleotide microarrays (Agilent Corporation). Additionally, LCM stromal cells were analyzed by Affymetrix array to control for potential platform variation. Tissue specimens for LCM were obtained from prostate needle biopsies from 14 men identified from a biospecimen repository with no evidence of cancer as well as a second or third set of biopsies that also showed no evidence of carcinoma. Each biopsy specimen was immediately embedded in OCT and flash frozen in isopentane immersed in liquid nitrogen. All protocols for tissue acquisition and processing were under approval by the University of Washington Institutional Review Board following a standard protocol.
Blocks of unfixed prostate tissue were harvested at surgery. Serial 5-μm sections were prepared from randomly selected frozen blocks, fixed in cold acetone, and processed for immunohistochemistry. We used frozen sections since a majority of the anti-CD antibodies we used do not immunoreact with antigens in formalin-fixed, paraffin-embedded tissue. Immunohistochemistry was performed as described previously . The primary antibodies used were mouse monoclonal CD antibodies (BD-PharMingen, San Diego, CA) diluted to 8 ng/μl or less. Antigen was localized using biotinylated anti-mouse IgG or IgM (Vector Labs, Burlingame, CA) as the secondary antibody and diaminobenzidine tetrahydrochloride as the chromogen. The sections were counterstained with hematoxylin.
The percentage of cells of a specific histological phenotype that expressed the antigen was estimated in five randomly selected fields at a final magnification of 40× (ocular 10×; objective 4×). Staining intensity was evaluated by two parameters (staining intensity and percentage of cells exhibiting each level of intensity). Intensity of reaction product was based on a 3-point scale – none, faint/equivocal and intense. These categories of staining intensity were defined as follows. Intense: immunoreaction deposit is distinctly more optically dense than background and than tissue that does not express the antigen. Equivocal: immunoreaction deposit is either similar enough in optical density to the background and/or to tissue that does not express the antigen, or is so focal, i.e. < 5% of cells, that there is reasonable uncertainty regarding whether the cells express the antigen. None: there is either no immunoreaction deposit or reaction product is no more optically dense than background.
WF = weighting factor, usually 0.2
The value of the weighting factor can be changed on our web site by independent observers of the images. We did this since we thought that observers of the images should be able to alter the value assigned to the weighting factor based on whether they thought that faint/equivocal immunostained cells either did or did not truly represent immunolocalization of each respective CD antigen. For the present study we used a weighting factor of 0.2.
n = number of immunostains for each CD by cell type
A = staining value for each immunostain by cell type
Average scores in the range of 0.751 to 1.0 were categorized as intense, 0.251 to 0.750 as equivocal or 0 to 0.250 as none. Immunostained sections were imaged with an Olympus BX41 microscope (Olympus, Melville, NY) equipped with a MicroFire digital camera (Optronics, Goleta, CA). Composite images were constructed with Photoshop CS (Adobe Systems, San Jose, CA) and are publicly available online .
Affymetrix gene expression profiling of MACS-sorted cell populations and laser captured stromal cells
Transcriptomes of the following prostate cell types have been previously determined: CD104+ basal epithelial cells, CD26+ luminal epithelial cells, CD49a+ stromal fibromuscular cells, and CD31+ endothelial cells , ABCG2+ stem cells  and CD26+ cancer cells [in preparation]. The CD molecules cited above were the ones used for sorting each specific cell type for gene expression profiling. The expression of genes documented in the literature as cell-type specific was compared to Affymetrix datasets; analyses showed that the transcriptomes were representative of their respective cell type (p < 0.05) . Five separate biological replicates of each MACS-sorted cell populations (except the CD26+ cancer cells which comprised of one biological sample) and three separate biological replicates of laser captured stromal cells were assayed to produce a dataset using the Human Genome U133 Plus 2.0 GeneChips (Affymetrix, Santa Clara, CA). The U-133 Plus 2.0 array contains probesets representing 54,675 genes, splice variants, and ESTs. The GeneChips were prepared, hybridized, and scanned according to the protocols provided by Affymetrix. Briefly, 200 ng of RNA was reverse transcribed with poly (dT) primer containing a T7 promoter, and the cDNA was made double-stranded. In vitro transcription was performed to produce unlabeled cRNA. Next, first-strand cDNA was produced with random primer. cDNA was made double-stranded with poly (dT) primer/T7 promoter. Finally, in vitro transcription was performed with biotinylated ribonucleotides. The biotin-labeled cRNA was hybridized to the GeneChips. The chips were washed and stained with streptavidin-PE using an Affymetrix FS-450 fluidics station. Data was collected with an Affymetrix GeneChip Scanner 3000.
LCM and RNA isolation of luminal and stromal cells for Agilent array analysis
Approximately two thousand luminal epithelial or stromal cells from histologically benign glands were captured using the Veritas Microdissection System (Molecular Devices Corporation, Sunnyvale, CA) as previously described . Frozen sections (8 μm) were cut from tissue embedded in OCT (Miles, Inc. Diagonostic Division, Elkhart, IN) and fixed in cold 95% ethanol. After brief (5–10 seconds) staining with hematoxylin using the HistoGene staining solution (Arcturus Engineering, Mountain View, CA), the sections were dehydrated in 100% ethanol, followed by sequential ethanol-xylene solutions. Three stromal cell samples and 14 luminal cell samples were captured. Digital photographs were taken of tissue sections before, during, and after LCM and assessed independently by two investigators to confirm that the laser-captured cells were stromal and luminal cells, respectively.
Captured individual specimens were lysed and RNA was isolated using Arcturus PicoPure RNA Isolation. Samples were treated with RNase-free DNase (Qiagen, Inc., Valencia, CA). This RNA was converted to a cDNA library and amplified using the TransPlex WTA (Rubicon Genomics, Ann Arbor, MI) using Titanium Taq polymerase (Clontech Laboratories, Inc., Mountain View, CA) in the presence of amino-allyl deoxyuridine triphosphate (dUTP) for postamplification labeling. Real-time PCR amplifications were terminated at plateau phase, as measured by fluorescence incorporation, to preserve maximum representation. Concentration and purity was determined using the ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE). To provide a reference standard RNA for use on 2-color microarrays, we isolated total RNA from LNCaP, DU145, PC3, and CWR22 cell lines (American Type Culture Collection, Manassas, VA) growing at log phase in dye-free RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS; Life Technologies, Rockville, MD). RNA was purified using Trizol (Life Technologies). The RNA was then further purified by Qiagen RNeasy maxi and treated with Qiagen RNase-free DNase. This total RNA was then amplified using the TransPlex WTA similar to the experimental samples.
Gene expression profiling on Agilent microarrays
Two μg of WTA-amplified amino-allyl cDNA from each of 14 luminal and 3 stromal samples were labeled with Cy3 fluorescent dye (cell line reference cDNA was labeled with Cy5) as described previously , and hybridized to Agilent 44 K whole human genome expression oligonucleotide microarray slides (Agilent Technologies, Inc., Santa Clara, CA) following the manufacturer's suggested protocols. Fluorescence array images were collected for both Cy3 and Cy5 using an Agilent fluorescent scanner, and Feature Extraction software (Agilent Technologies, Santa Clara, CA, USA) was used to grid, extract image intensities, and normalize data. Spots of poor quality, as determined by the software, were removed from further analysis. Data from the 14 luminal or 3 stromal samples were combined and the average signal values were used for comparative analyses. For genes with multiple probes on the array, the data from the probe with the highest sequence specificity was chosen.
Data comparison and statistical analysis
Immunohistochemistry data was compared to Affymetrix array data for the following prostate cell types: MACS-sorted basal cells, luminal cells, stromal fibromuscular cells, endothelial cells, stem cells and cancer cells from a primary tumor of Gleason 3+3 = 6. Additionally IHC data was compared to Affymetrix array data for LCM stromal and Agilent array data for LCM stromal and epithelial cells. Data was analyzed for concordance and correlation of expression levels. Data concordance was defined as agreement between data types based on 'present' or 'absent' calls, i.e., if both techniques detected a gene, they were considered to be in agreement. Genes in the Affymetrix array dataset with an average raw fluorescence signal < 50 were considered to be undetected by the experiment and were classified as 'absent' by GCOS version 1.0 software (Affymetrix) . Genes were also classified as undetected if the majority (> 50%) of individual probe sets were classified as 'absent'. All other hybridization patterns were classified as 'present'. Genes with signal 51–500 were considered to have 'low' expression, those with signal 501–5000 were considered to have 'medium' expression, and those with signal > 5000 were considered to have 'high' expression. Genes in the Agilent array dataset with an average raw fluorescence signal < 100 were considered to be 'absent', those with signal 100–999 were considered to have 'low' expression, those with signal 1000–9999 were considered to have 'medium' expression, and those with signal > 9999 were considered to have 'high' expression. Immunohistochemistry data having a staining level of intense (score range 0.751 – 1.0) or equivocal (score range 0.251 – 0.750) were classified as in agreement with genes classified as 'present'. Immunostaining results of none (score range 0 – 0.250) were considered equivalent to 'absent'.
The correlation between mRNA expression levels and immunostaining intensity for each CD antibody was described by Pearson product-moment and Spearman nonparametric correlation measures and tests. Correlations were calculated for mean and median values. Median was included as a more robust measurement in the presence of outlier or skewed values especially for the array data. Individual Affymetrix probes within a probe set were originally designated to hybridize with the same unique mRNA transcript. However, increasing knowledge of genomic sequences has shown that a substantial number of the manufacturer's original probe groupings and mappings are inaccurate and must be corrected . To address the possibility that disparity in expression level was due to probe set definition, correlation was additionally determined using updated custom CDF definitions . Dai et al., have reorganized probe set definitions based on the latest genome and transcriptome information . These updated definitions based on Version 8 Entrez Gene mappings were downloaded and used in addition to the Affymetrix probe set definitions. To control for possible gene expression changes induced by the cell sorting process, LCM data for stromal cells were also compared for correlation. Additionally, LCM data for luminal and stromal cells assayed by Agilent array analysis was also used for correlation calculations. Correlations were calculated using both original and updated probe set definitions and were categorized as follows: no positive correlation (0.00 to 0.30), moderate positive correlation (0.31 to 0.79), and strong positive correlation (0.80 to 0.99).
We thank Tracy Sherertz and Martin Korb for their contribution to the database construction. We thank Paul Shannon for assistance in statistical analysis.
This work was supported by grant DK63630 and DK069690 from NIDDK. Additional funding came from grants CA85859, CA98699 and CA111244 from NCI, and PM50 GMO76547/Center for Systems Biology.
- Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol Cell Biol. 1999, 19 (3): 1720-1730.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen G, Gharib TG, Huang CC, Taylor JM, Misek DE, Kardia SL, Giordano TJ, Iannettoni MD, Orringer MB, Hanash SM, Beer DG: Discordant protein and mRNA expression in lung adenocarcinomas. Mol Cell Proteomics. 2002, 1 (4): 304-313. 10.1074/mcp.M200008-MCP200.PubMedView ArticleGoogle Scholar
- Lichtinghagen R, Musholt PB, Lein M, Romer A, Rudolph B, Kristiansen G, Hauptmann S, Schnorr D, Loening SA, Jung K: Different mRNA and protein expression of matrix metalloproteinases 2 and 9 and tissue inhibitor of metalloproteinases 1 in benign and malignant prostate tissue. Eur Urol. 2002, 42 (4): 398-406. 10.1016/S0302-2838(02)00324-X.PubMedView ArticleGoogle Scholar
- Schlemmer BO, Sorensen BS, Overgaard J, Olsen KE, Gjerdrum LM, Nexo E: Quantitative PCR--new diagnostic tool for quantifying specific mRNA and DNA molecules: HER2/neu DNA quantification with LightCycler real-time PCR in comparison with immunohistochemistry and fluorescence in situ hybridization. Scand J Clin Lab Invest. 2004, 64 (5): 511-522. 10.1080/00365510410002922.PubMedView ArticleGoogle Scholar
- Waghray A, Feroze F, Schober MS, Yao F, Wood C, Puravs E, Krause M, Hanash S, Chen YQ: Identification of androgen-regulated genes in the prostate cancer cell line LNCaP by serial analysis of gene expression and proteomic analysis. Proteomics. 2001, 1 (10): 1327-1338. 10.1002/1615-9861(200110)1:10<1327::AID-PROT1327>3.0.CO;2-B.PubMedView ArticleGoogle Scholar
- Lin B, White JT, Lu W, Xie T, Utleg AG, Yan X, Yi EC, Shannon P, Khrebtukova I, Lange PH, Goodlett DR, Zhou D, Vasicek TJ, Hood L: Evidence for the presence of disease-perturbed networks in prostate cancer cells by genomic and proteomic analyses: a systems approach to disease. Cancer Res. 2005, 65 (8): 3081-3091.PubMedGoogle Scholar
- Orntoft TF, Thykjaer T, Waldman FM, Wolf H, Celis JE: Genome-wide study of gene copy numbers, transcripts, and protein levels in pairs of non-invasive and invasive human transitional cell carcinomas. Mol Cell Proteomics. 2002, 1 (1): 37-45. 10.1074/mcp.M100019-MCP200.PubMedView ArticleGoogle Scholar
- Schostak M, Krause H, Miller K, Schrader M, Weikert S, Christoph F, Kempkensteffen C, Kollermann J: Quantitative real-time RT-PCR of CD24 mRNA in the detection of prostate cancer. BMC Urol. 2006, 6: 7-10.1186/1471-2490-6-7.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu AY, Roudier MP, True LD: Heterogeneity in primary and metastatic prostate cancer as defined by cell surface CD profile. Am J Pathol. 2004, 165 (5): 1543-1556.PubMedPubMed CentralView ArticleGoogle Scholar
- Kristiansen G, Pilarsky C, Wissmann C, Kaiser S, Bruemmendorf T, Roepcke S, Dahl E, Hinzmann B, Specht T, Pervan J, Stephan C, Loening S, Dietel M, Rosenthal A: Expression profiling of microdissected matched prostate cancer samples reveals CD166/MEMD and CD24 as new prognostic markers for patient survival. J Pathol. 2005, 205 (3): 359-376. 10.1002/path.1676.PubMedView ArticleGoogle Scholar
- Kristiansen G, Pilarsky C, Pervan J, Sturzebecher B, Stephan C, Jung K, Loening S, Rosenthal A, Dietel M: CD24 expression is a significant predictor of PSA relapse and poor prognosis in low grade or organ confined prostate cancer. Prostate. 2004, 58 (2): 183-192. 10.1002/pros.10324.PubMedView ArticleGoogle Scholar
- Liu AY, Peehl DM: Characterization of cultured human prostatic epithelial cells by cluster designation antigen expression. Cell Tissue Res. 2001, 305 (3): 389-397. 10.1007/s004410100419.PubMedView ArticleGoogle Scholar
- Oudes AJ, Campbell DS, Sorensen CM, Walashek LS, True LD, Liu AY: Transcriptomes of human prostate cells. BMC Genomics. 2006, 7: 92-10.1186/1471-2164-7-92.PubMedPubMed CentralView ArticleGoogle Scholar
- Pascal LE, Deutsch EW, Campbell DS, Korb M, True LD, Liu AY: The urologic epithelial stem cell database (UESC) - a web tool for cell type-specific gene expression and immunohistochemistry images of the prostate and bladder. BMC Urol. 2007, 7: 19-10.1186/1471-2490-7-19.PubMedPubMed CentralView ArticleGoogle Scholar
- Deutsch EW, Ball CA, Berman JJ, Bova GS, Brazma A, Bumgarner RE, Campbell D, Causton HC, Christiansen JH, Daian F, Dauga D, Davidson D, Gimenez G, Goo YA, Grimmond S, Henrich T, Gerrmann BG, Johnson MH, Korb M, Mills JC, Oudes AJ, Parkinson HE, Pascal LE, Pollet N, Quackenbush J, Ramialison M, Ringwald M, Salgado D, Sansone SA, Sherlock G, Christian J Stoeckert J, Swedlow J, Taylor RC, Walashek L, Warford A, Wilkinson DG, Zhou Y, Zon LI, Liu AY, True LD: Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE). Nature Biotechnology.
- Dall P, Heider KH, Sinn HP, Skroch-Angel P, Adolf G, Kaufmann M, Herrlich P, Ponta H: Comparison of immunohistochemistry and RT-PCR for detection of CD44v-expression, a new prognostic factor in human breast cancer. Int J Cancer. 1995, 60 (4): 471-477. 10.1002/ijc.2910600408.PubMedView ArticleGoogle Scholar
- Yu H, Wang F, Tu K, Xie L, Li YY, Li YX: Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data. BMC Bioinformatics. 2007, 8: 194-10.1186/1471-2105-8-194.PubMedPubMed CentralView ArticleGoogle Scholar
- Jackson DG, Buckley J, Bell JI: Multiple variants of the human lymphocyte homing receptor CD44 generated by insertions at a single site in the extracellular domain. J Biol Chem. 1992, 267 (7): 4732-4739.PubMedGoogle Scholar
- Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, Watson SJ, Meng F: Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005, 33 (20): e175-10.1093/nar/gni179.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu AY, True LD: Characterization of prostate cell types by CD cell surface molecules. Am J Pathol. 2002, 160 (1): 37-43.PubMedPubMed CentralView ArticleGoogle Scholar
- Oudes AJ, Roach JC, Walashek LS, Eichner LJ, True LD, Vessella RL, Liu AY: Application of Affymetrix array and Massively Parallel Signature Sequencing for identification of genes involved in prostate cancer progression. BMC Cancer. 2005, 5: 86-10.1186/1471-2407-5-86.PubMedPubMed CentralView ArticleGoogle Scholar
- Greenbaum D, Colangelo C, Williams K, Gerstein M: Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 2003, 4 (9): 117-10.1186/gb-2003-4-9-117.PubMedPubMed CentralView ArticleGoogle Scholar
- Nie L, Wu G, Zhang W: Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis. Genetics. 2006, 174 (4): 2229-2243. 10.1534/genetics.106.065862.PubMedPubMed CentralView ArticleGoogle Scholar
- Tian Q, Stepaniants SB, Mao M, Weng L, Feetham MC, Doyle MJ, Yi EC, Dai H, Thorsson V, Eng J, Goodlett D, Berger JP, Gunter B, Linseley PS, Stoughton RB, Aebersold R, Collins SJ, Hanlon WA, Hood LE: Integrated genomic and proteomic analyses of gene expression in Mammalian cells. Mol Cell Proteomics. 2004, 3 (10): 960-969. 10.1074/mcp.M400055-MCP200.PubMedView ArticleGoogle Scholar
- Nie L, Wu G, Zhang W: Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: a multiple regression to identify sources of variations. Biochem Biophys Res Commun. 2006, 339 (2): 603-610. 10.1016/j.bbrc.2005.11.055.PubMedView ArticleGoogle Scholar
- Pollard K, Lunny D, Holgate CS, Jackson P, Bird CC: Fixation, processing, and immunochemical reagent effects on preservation of T-lymphocyte surface membrane antigens in paraffin-embedded tissue. J Histochem Cytochem. 1987, 35 (11): 1329-1338.PubMedView ArticleGoogle Scholar
- Hessner MJ, Xiang B, Jia S, Geoffrey R, Holmes S, Meyer L, Muheisen S, Wang X: Three-color cDNA microarrays with prehybridization quality control yield gene expression data comparable to that of commercial platforms. Physiol Genomics. 2006, 25 (1): 166-178. 10.1152/physiolgenomics.00243.2005.PubMedView ArticleGoogle Scholar
- Mah N, Thelin A, Lu T, Nikolaus S, Kuhbacher T, Gurbuz Y, Eickhoff H, Kloppel G, Lehrach H, Mellgard B, Costello CM, Schreiber S: A comparison of oligonucleotide and cDNA-based microarray systems. Physiol Genomics. 2004, 16 (3): 361-370. 10.1152/physiolgenomics.00080.2003.PubMedView ArticleGoogle Scholar
- Mecham BH, Klus GT, Strovel J, Augustus M, Byrne D, Bozso P, Wetmore DZ, Mariani TJ, Kohane IS, Szallasi Z: Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucleic Acids Res. 2004, 32 (9): e74-10.1093/nar/gnh071.PubMedPubMed CentralView ArticleGoogle Scholar
- Mecham BH, Wetmore DZ, Szallasi Z, Sadovsky Y, Kohane I, Mariani TJ: Increased measurement accuracy for sequence-verified microarray probes. Physiol Genomics. 2004, 18 (3): 308-315. 10.1152/physiolgenomics.00066.2004.PubMedView ArticleGoogle Scholar
- Tan PK, Downey TJ, Spitznagel EL, Xu P, Fu D, Dimitrov DS, Lempicki RA, Raaka BM, Cam MC: Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res. 2003, 31 (19): 5676-5684. 10.1093/nar/gkg763.PubMedPubMed CentralView ArticleGoogle Scholar
- Pritchard CC, Hsu L, Delrow J, Nelson PS: Project normal: defining normal variance in mouse gene expression. Proc Natl Acad Sci U S A. 2001, 98 (23): 13266-13271. 10.1073/pnas.221465998.PubMedPubMed CentralView ArticleGoogle Scholar
- Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, Bradford BU, Bumgarner RE, Bushel PR, Chaturvedi K, Choi D, Cunningham ML, Deng S, Dressman HK, Fannin RD, Farin FM, Freedman JH, Fry RC, Harper A, Humble MC, Hurban P, Kavanagh TJ, Kaufmann WK, Kerr KF, Jing L, Lapidus JA, Lasarev MR, Li J, Li YJ, Lobenhofer EK, Lu X, Malek RL, Milton S, Nagalla SR, O'Malley J P, Palmer VS, Pattee P, Paules RS, Perou CM, Phillips K, Qin LX, Qiu Y, Quigley SD, Rodland M, Rusyn I, Samson LD, Schwartz DA, Shi Y, Shin JL, Sieber SO, Slifer S, Speer MC, Spencer PS, Sproles DI, Swenberg JA, Suk WA, Sullivan RC, Tian R, Tennant RW, Todd SA, Tucker CJ, Van Houten B, Weis BK, Xuan S, Zarbl H: Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods. 2005, 2 (5): 351-356. 10.1038/nmeth754.PubMedView ArticleGoogle Scholar
- Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson E, Garcia JG, Geoghegan J, Germino G, Griffin C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki E, Martinez-Murillo F, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang Y, Ye SQ, Yu W: Multiple-laboratory comparison of microarray platforms. Nat Methods. 2005, 2 (5): 345-350. 10.1038/nmeth756.PubMedView ArticleGoogle Scholar
- Pascal LE, Oudes AJ, Petersen TW, Goo YA, Walashek LS, True LD, Liu AY: Molecular and cellular characterization of ABCG2 in the prostate. BMC Urol. 2007, 7 (1): 6-10.1186/1471-2490-7-6.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu AY, True LD, LaTray L, Nelson PS, Ellis WJ, Vessella RL, Lange PH, Hood L, van den Engh G: Cell-cell interaction in prostate gene regulation and cytodifferentiation. Proc Natl Acad Sci U S A. 1997, 94 (20): 10705-10710. 10.1073/pnas.94.20.10705.PubMedPubMed CentralView ArticleGoogle Scholar
- Kassen A, Sutkowski DM, Ahn H, Sensibar JA, Kozlowski JM, Lee C: Stromal cells of the human prostate: initial isolation and characterization. Prostate. 1996, 28 (2): 89-97. 10.1002/(SICI)1097-0045(199602)28:2<89::AID-PROS3>3.0.CO;2-I.PubMedView ArticleGoogle Scholar
- Pascal LE, Deutsch EW, Campbell DS, Korb M, True LD, Liu AY: The urologic epithelial stem cell database (UESC) - a web tool for cell type-specific gene expression and immunohistochemistry images of the prostate and bladder. BMC Urology.
- Goo YA, Goodlett DR, Pascal LE, Worthington KD, Vessella RL, True LD, Liu AY: Stromal mesenchyme cell genes of the human prostate and bladder. BMC Urol. 2005, 5: 17-10.1186/1471-2490-5-17.PubMedPubMed CentralView ArticleGoogle Scholar
- True L, Coleman I, Hawley S, Huang CY, Gifford D, Coleman R, Beer TM, Gelmann E, Datta M, Mostaghel E, Knudsen B, Lange P, Vessella R, Lin D, Hood L, Nelson PS: A molecular correlate to the Gleason grading system for prostate adenocarcinoma. Proc Natl Acad Sci U S A. 2006, 103 (29): 10991-10996. 10.1073/pnas.0603678103.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu H, Zeeberg BR, Qu G, Koru AG, Ferrucci A, Kahn A, Ryan MC, Nuhanovic A, Munson PJ, Reinhold WC, Kane DW, Weinstein JN: AffyProbeMiner: a web resource for computing or retrieving accurately redefined Affymetrix probe sets. Bioinformatics. 2007, 23 (18): 2385-2390. 10.1093/bioinformatics/btm360.PubMedView ArticleGoogle Scholar
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