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(American Journal of Pathology. 2003;162:925-932.)
© 2003 American Society for Investigative Pathology

Gene Expression Patterns in Renal Cell Carcinoma Assessed by Complementary DNA Microarray

John P.T. Higgins*, Rajesh Shinghal{dagger}, Harcharan Gill{dagger}, Jeffrey H. Reese{ddagger}, Martha Terris§, Ronald J. Cohen, Michael Fero||, Jonathan R. Pollack*, Matt van de Rijn* and James D. Brooks{dagger}

From the Department of Pathology,* Stanford University School of Medicine, Stanford, California; the Department of Urology,{dagger} Stanford University School of Medicine, Stanford, California; the Department of Urology,{ddagger} Santa Clara Valley Medical Center, San Jose, California; the Department of Urology,§ Palo Alto VA Hospital, Palo Alto, California; the Department of Surgery, Urological Research Centre, University of Western Australia, Perth, Western Australia, Australia; and the Department of Genetics,|| Stanford University School of Medicine, Stanford, California


   Abstract
Top
 Abstract
Materials and Methods
 Results
 Discussion
 References
 
Renal cell carcinoma comprises several histological types with different clinical behavior. Accurate pathological characterization is important in the clinical management of these tumors. We describe gene expression profiles in 41 renal tumors determined by using DNA microarrays containing 22,648 unique cDNAs representing 17,083 different UniGene Clusters, including 7230 characterized human genes. Differences in the patterns of gene expression among the different tumor types were readily apparent; hierarchical cluster analysis of the tumor samples segregated histologically distinct tumor types solely based on their gene expression patterns. Conventional renal cell carcinomas with clear cells showed a highly distinctive pattern of gene expression. Papillary carcinomas formed a tightly clustered group, as did tumors arising from the distal nephron and the normal kidney samples. Surprisingly, conventional renal cell carcinomas with granular cytoplasm were heterogeneous, and did not resemble any of the conventional carcinomas with clear cytoplasm in their pattern of gene expression. Characterization of renal cell carcinomas based on gene expression patterns provides a revised classification of these tumors and has the potential to supply significant biological and clinical insights.

Renal cell carcinoma comprises 3% of malignancies worldwide and is increasing in incidence.1-8 Between one-third and one-half of the 30,000 patients diagnosed with renal cell carcinoma in the United States each year will die of their disease. Only a fraction of renal cell carcinomas are responsive to radiotherapy, immunotherapy, or chemotherapy, making the disease difficult to control once it has spread beyond the kidney. Tumor stage and surgical resectability are the most important prognostic factors for renal cell carcinoma, but the histological subtype and grade provide additional prognostic information. Renal cell carcinomas can be classified into conventional (both granular and clear cell variants), papillary, and chromophobe carcinomas based on their histological appearance. Although each of the histological variants displays a spectrum of clinical behavior, conventional carcinomas tend to behave aggressively, while chromophobe carcinomas and papillary carcinomas follow a more indolent clinical course.9

Each histological variant of renal cell carcinoma shows distinct karyotypic abnormalities including loss of chromosome 3p in clear cell carcinomas10 and trisomy of chromosomes 7, 12, 16, 17, and 20 in papillary carcinomas.11 These molecular genetic alterations have been coupled with histological features to form a revised classification of renal cell tumors.12 Since changes in gene copy number (gene amplification, aneuploidy, and allelic loss) as well as differences in histological appearance have been associated with altered gene expression patterns, we anticipated that each of the histological subtypes of renal cell carcinoma would have unique and easily identifiable gene expression signatures. In addition, an analysis of seven renal cell carcinomas by Young et al13 suggested that gene expression profiles could be associated with histological subtype.

To further test whether gene expression patterns can add to the classification of renal cell carcinomas, we performed DNA microarray analysis on 41 renal tumors of diverse histological types and on three normal kidney samples using 22,648 element-spotted DNA microarrays representing 17,083 different human genes. We report that gene expression patterns can readily distinguish between the histological subtypes of renal cell carcinoma and that conventional renal cell carcinomas with predominantly granular cytoplasm may represent a heterogeneous group distinct from conventional clear cell carcinoma. Our data set is a potential rich source for novel markers of the subtypes of renal cell carcinoma and may offer unique biological insights into these tumors.


   Materials and Methods
Top
 Abstract
 Materials and Methods
Results
 Discussion
 References
 
Tissue Samples

Samples were obtained from fresh nephrectomy specimens and immediately frozen on dry ice. Paraffin sections from each specimen were reviewed by a single pathologist (J.P.T.H.) and classified according to Union International Contre le Cancer (UICC) and American Joint Committee on Cancer (AJCC) criteria14 and can be reviewed at http://genome-www.stanford.edu/renal_cell_carcinoma/. Our series included 28 conventional renal cell carcinomas (three of which had accompanying renal vein tumor thrombi), four papillary carcinomas, three chromophobe carcinomas, two oncocytomas, and one angiomyolipoma. The conventional carcinomas were subdivided into 23 clear cell and five granular cell carcinomas based on whether they contained cytoplasmic granules in the majority of the tumor cells. Three normal kidney samples were obtained fromareas of the nephrectomy specimens uninvolved by carcinoma.

Microarray Analysis

Methods for mRNA extraction, hybridization to 17,083 gene (represented by 22,648 cDNAs) DNA microarrays, and interpretation of data have been described elsewhere15-18 and detailed protocols are available at http://cmgm.Stanford.EDU/pbrown/. Each tumor mRNA was hybridized against a common reference pool of mRNA made from 11 different cell lines as we have described previously.17

Average linkage hierarchical cluster analysis was used to organize gene expression data. Expression levels for each transcript were centered across all samples. We selected transcripts whose expression level differed by a factor of four or greater from the normalized mean level of expression in at least two of the samples. We further restricted our analysis to those genes with a fluorescent hybridization signal greater than 100 over background in 80% of the experimental samples. Of the initial list of 22,648 cDNAs, 1550 cDNAs met these criteria.

The significance analysis of microarrays (SAM) procedure was used to identify genes significantly associated with the conventional granular cell carcinoma subtype.19 SAM computes a two-sample T-statistic for the normalized log ratios of the gene expression levels for each gene. It thresholds the T-statistics to produce a "significant" gene list and provides an estimate of the false discovery rate (genes that differ between samples by chance alone) from randomly permuted data. SAM also allows identification of genes associated with classes of tumors that may not be evident by hierarchical clustering analysis.

Immunohistochemistry

Paraffin blocks were available from 30 of the 38 cases. Four-µm sections were cut and immunohistochemistry was performed using monoclonal antibodies for CK7 (clone OV-TL 12/30, BioGenex, San Ramon, CA; 1:200), CD10 (clone 56C6, Novocastra, Newcastle-on-Tyne, UK; 1:50), and vimentin (clone V9, BioGenex; ready to use) according to previously published protocols.20


   Results
Top
 Abstract
 Materials and Methods
 Results
Discussion
 References
 
We used 22,648 element-spotted DNA microarrays to characterize the gene expression patterns in 41 renal tumors and three normal kidney samples. We included renal tumors of diverse histology including conventional clear cell, conventional granular cell, papillary, and chromophobe carcinomas as well as two oncocytomas and an angiomyolipoma. Hierarchical cluster analysis was used to group samples, based on the degree of similarity of their gene expression profiles, as well as genes, based on their pattern of expression across all of the samples (Figure 1 and on-line at http://genome_www.Stanford.edu/renal_cell_carcinoma). We selected a set of 1550 transcripts with significant variation over the samples measured (fourfold from the mean) that were well measured above background in at least 80% of the samples. Expression profiles over this set of transcripts separated the tumors into two broad classes: one consisting entirely of conventional clear cell carcinomas and the other of the remaining tumor subtypes and normal kidney samples. Three samples harvested from renal vein thrombidisplayed expression patterns highly similar to their respective primary tumors, suggesting that each tumor has a unique and characteristic gene expression pattern (Figure 1) .



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  		Figure 1. Hierarchical clustering analysis for all genes across all arrays. A representation of the variation in expression of 1550 genes in the 38 tumors and three normal samples is shown at the left. The right panel depicts the detailed sample dendrogram in which three primary tumors and their associated renal vein/vena cava thrombi are designated on the dendrogram by branches with filled circles at the ends. Each row represents a single gene. Each column represents a single sample. The hierarchical clustering algorithm organizes the experimental samples only on the basis of overall similarity in their gene expression. Green squares indicate transcript levels below the mean; black squares, transcript levels equal to the mean; red squares, transcript levels greater than the mean; gray squares, technically inadequate or missing data. Gene filtering criteria were for fourfold variation from the normalized mean and at least 80% well measured spots. Colored bars adjacent to the clustering table indicate the position of the enlarged images. The color of the bar indicates the type of sample in which the cluster of genes is expressed. Green, papillary gene cluster; blue, normal gene cluster; orange, chromophobe-oncocytoma gene cluster; red, conventional clear cell carcinoma gene cluster. Selected gene names are shown.

 
Like the conventional carcinomas with clear cytoplasm, the papillary carcinomas showed expression patterns highly similar to each other, as did all of the normal kidney samples. The gene expression patterns of the remaining tumor samples were somewhat more diverse. The two oncocytomas and three chromophobe carcinomas showed strikingly similar patterns over the 1550 transcripts, as reflected in the short branch length of the dendrogram connecting these tumors. Interestingly, cases of conventional renal cell carcinoma in which the majority of the tumor cells show granular cytoplasm did not group together. One sample showed an expression pattern that was most similar to the papillary carcinomas and three others resembled the chromophobe/oncocytoma group (Figure 1) . One tumor had a highly distinctive expression pattern and did not group with any other carcinoma, but clustered instead with the angiomyolipoma. The histology of the conventional carcinomas with granular cytoplasm was re-reviewed in light of the expression profiles and in no case did they meet diagnostic criteria for any other histological category. Histology for all samples analyzed can be viewed at the supplemental website: http://genome-www.stanford.edu/renal_cell_carcinoma/.

Inspection of the genes that characterize each of the histological subtypes of renal cell carcinoma offers some insights into the biology of these tumors. A group of 230 transcripts was overexpressed in all conventional carcinomas with clear cytoplasm, most of which are unnamed or poorly characterized (Figure 1) . VEGF, the glucose transporters 1 and 3 (SLC2A1 and SLC2A3),21 endothelin-1,22,23 and insulin-like growth factor binding protein 323 were all expressed at high levels in the clear cell carcinomas and are all primarily regulated by the HIF-1 transcription factor. Normally, HIF-1 levels are regulated by the von Hippel-Lindau (VHL) protein, which targets HIF-1 protein for ubiquitylation-mediated degradation.24 The VHL gene is inactivated in most conventional clear cell carcinomas, and HIF-1 protein is expressed at high levels. In our data set, neither VHL nor HIF-1 transcript levels varied across the tumor samples. Since the VHL gene is usually inactivated by loss of one allele and mutation of the second, and HIF-1 levels are regulated post-translationally, it is not surprising that neither gene shows altered transcript levels. Interestingly, HIF2-{alpha} (EPAS1) was highly expressed in the clear cell carcinomas. Although less well characterized than HIF1-{alpha}, EPAS1 is thought to be regulated by VHL,25 and our data suggests it may be regulated transcriptionally. Collagen types I, III, IV, V, and VI, lysyl oxidase, heparan sulfate proteoglycan 2, and fibronectin may be expressed by cells in the interstitium, while PECAM1 (CD31), EPAS1, VEGF receptor 2, and cadherin 5 are known to be expressed in endothelial cells and probably reflect the rich vascularity of these tumors.

Genes overexpressed in papillary carcinoma consist of two distinct clusters of 15 and 44 genes (Figure 1) . These genes include cytokeratin subsets that appear to be uniquely overexpressed in these tumors and could be exploited diagnostically. Papillary carcinomas also express high levels of {alpha}-methylacyl-CoA racemase (AMACR), a gene found to be overexpressed in prostate cancer by DNA microarray analysis.26,27 The oncogenes GRO 1 and GRO 2 are also expressed at high levels in these tumors, although it is unclear whether they are expressed by the malignant cells or by the characteristic foamy macrophages that line the papillae in these tumors. Additionally, other transcripts, such as osteoprotegerin and I factor, are known to be expressed by macrophages.

Chromophobe carcinomas and oncocytomas both show increased expression of the stem cell factor receptor (KIT, CD117), a gene not previously implicated in these neoplasms (Figure 1) . This gene is also highly expressed in gastrointestinal stromal sarcoma (GIST) by immunohistochemistry28 and by DNA microarray analysis29 and is a potential therapeutic target.30 Several genes that are highly expressed in oncocytomas and chromophobe carcinomas, including nicotinamide nucleotide transhydrogenase, fumarate hydratase, and solute carrier family 25 members 4&5, encode mitochondrial proteins. Oncocytomas are known to be rich in mitochondria,31 and chromophobe carcinomas contain abundant microvesicles that likely represent altered mitochondria.32,33

The gene expression profiles of conventional carcinomas with granular cytoplasm are distinct from those with clear cytoplasm and appeared heterogeneous. Since their expression patterns over the 1550 transcripts were highly diverse and shared similarities with the other histological subtypes of renal malignancies, we were not able to identify a discrete set of genes that characterize these malignancies using hierarchical clustering analysis. We used the SAM procedure to compare the gene expression patterns of the conventional granular renal carcinomas to the other renal tumors in our data set to identify genes that characterize the granular cell phenotype. Although SAM analysis identified 91 transcripts highly expressed in the granular cell carcinomas and two expressed at significantly lower levels compared to the other tumors, none of these 93 transcripts showed consistent expression changes across all of the conventional carcinomas with granular cytoplasm, nor did they reliably distinguish these tumors from other histological subtypes (data not shown).

To evaluate whether the transcripts we identified could help distinguish between tumor subtypes, we selected commercially available antibodies for three proteins whose transcripts were differentially expressed (keratin 7, CD10, and vimentin) and performed immunohistochemical analysis of paraffin-embedded tissues from the set of tumors that were analyzed by DNA microarray (Table 1) . In each case, the protein expression pattern usually reflected the RNA expression pattern observed in the DNA microarray analysis (Figure 2) . Most notably, vimentin readily distinguished between the clear cell carcinoma and chromophobe carcinoma subtypes, a distinction that may be difficult on hematoxylin and eosin-stained sections. Thus, these data sets represent a potential source of immunohistochemical markers that may aid in distinguishing renal tumor subclasses.


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  		Table 1. Results of Immunohistochemistry

 


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  		Figure 2. Immunohistochemical analysis of CK7, CD10, and vimentin and correlated mRNA expression data. mRNA expression data for the keratin 7, CD10, and vimentin genes, displayed using the same color key used in Figure 1 . Immunohistochemical stains for the proteins encoded by these genes are shown for selected tumors below the corresponding mRNA expression results.

 

   Discussion
Top
 Abstract
 Materials and Methods
 Results
 Discussion
References
 
Classification of renal neoplasms based on gene expression largely recapitulates that based on the histological appearance. Conventional clear cell carcinomas showed expression patterns highly distinct from the other subtypes of renal cell carcinoma and from normal kidney. Papillary carcinoma and normal kidney also showed remarkably characteristic gene expression profiles. Chromophobe carcinomas shared many features of gene expression with the oncocytomas, while conventional renal carcinomas with granular cytoplasm were more heterogeneous and shared expression patterns with several histological subtypes, yet were clearly distinct from conventional clear cell carcinoma. Similar correlation between gene expression patterns and histological class has been observed in tumors from several different organ sites.16,29,34,35

We were surprised that conventional renal cell carcinomas with granular cytoplasm are molecularly distinct from typical conventional carcinomas with clear cytoplasm. Before standardization of renal tumor pathology by the Heidelberg classification and the Union International Contre le Cancer, many tumors designated as granular cell were actually oncocytomas, papillary carcinomas, chromophobe carcinomas, and other rare variants.12,14 Now, only those tumors with granular cytoplasm that fail to meet criteria for any of these diagnoses are classified with clear cell carcinomas as "conventional carcinomas" because of their similar clinical behavior. Some investigators have even suggested that clear cell and granular carcinomas are variants of a single histological and molecular subclass. However, our data suggest that the histological differences between clear and granular cell carcinoma correlate with distinct differences in gene expression patterns. Perhaps more surprising, the conventional carcinomas with granular cytoplasm display significant heterogeneity in their gene expression profiles and do not appear to represent a single tumor subtype. This heterogeneity will need to be confirmed by analysis of additional tumors; however, if true, it could signal differences in the biology of these tumors and may help in identifying tumors that respond differently to therapy.

Despite their distinctive appearance histologically, oncocytomas and chromophobe carcinomas had highly similar patterns of gene expression. In part, this similarity may be due to the small number of these tumors we analyzed or the set of transcripts we used in our analysis. However, this similarity also could reflect their common histogenesis from the distal renal tubule, or similar biological behavior since both tumors are often clinically indolent.36,37 One intriguing possibility is that these tumors arise from a common molecular genetic lesion and therefore display similar gene expression profiles. Individuals affected with the Birt-Hogg-Dubé syndrome develop multifocal chromophobe carcinomas and oncocytomas, often within the same kidney, suggesting these tumors may arise from a common genetic alteration.38 Transcriptional profiling of additional chromophobe carcinomas and oncocytomas will be necessary to shed light on the molecular genetic similarities and differences between these tumors.

Two groups, using techniques similar to ours, have analyzed gene expression profiles of renal cell carcinoma, and reported expression profiles similar to ours. Young et al13 analyzed seven renal carcinomas on DNA microarrays with 7075 genes and identified 32 transcripts overexpressed and 48 transcripts underexpressed in conventional compared to chromophobe renal cell carcinoma (RCC) and an oncocytoma. Forty-eight of these 80 genes were well measured in our tumors and all but three showed expression patterns identical to their report. Takahashi et al39 identified 32 transcripts with increased expression and 77 with decreased expression in clear cell carcinoma relative to normal kidney. Eighty-five of the 89 transcripts represented in our data set showed patterns of expression identical to that seen in their clear cell cancers.39 Using high-density DNA arrays spotted on nylon membranes, Boer et al40 described 1738 transcripts differentially expressed between normal and cancerous renal tissues. Tumors were not characterized by histological subtype, making comparison with our results difficult. However, 123 transcripts (representing 89 unique Unigene clusters) were differentially expressed between conventional and chromophobe carcinomas. Of the 64 that were well measured on our microarrays, 53 showed expression patterns that matched those reported by Boer et al.40 Therefore, microarray analysis of gene expression appears to be robust and reproducible despite the use of different tumor sets and measurement on different array platforms.

Expression patterns associated with papillary and granular cell carcinomas have not been described. Our data set, therefore, may serve as a rich source of molecular markers that aid in the discrimination of renal cell carcinoma subtypes. The very good correlation of keratin 7, CD10, and vimentin mRNA expression noted on microarray with protein expression seen by immunohistochemistry highlights the potential of cDNA microarray technology to identify novel diagnostic antibodies that could be used clinically.

Microarray analysis of malignancies from other organ sites has revealed molecular subtypes of tumors that are histologically indistinguishable and discrete gene expression profiles have been identified that correlate with clinical outcomes.16,17,34,35,41-44 Takahashi et al39 identified 51 genes with expression patterns that correlated with adverse outcome in 29 patients followed 10 years after resection of their clear cell carcinoma. When we performed hierarchical clustering analysis of the conventional clear cell carcinomas in our data set using 45 of their 51 genes that were present on our arrays, the tumors were sorted into two groups with expression patterns similar to those reported (data not shown). Additional clinical follow-up of patients in our series will be necessary to determine whether the expression patterns observed for this set of genes or additional sets of genes carry prognostic information.

Global analysis of gene expression using cDNA microarray technology offers significant opportunities to identify novel markers that discriminate between classes of renal tumors and holds promise in identifying molecular subclasses of tumors with differing prognosis. These data provide a starting point for identification of proteins with altered expression in renal cell carcinomas. Such proteins may be measurable in the serum or urine and could serve as new markers for this disease. They could be used clinically to monitor response to therapy or, possibly, to screen those at high risk for renal cell carcinoma such as individuals with hereditary forms of the disease. Ultimately, biological insights gleaned from microarray analysis of gene expression in renal cell carcinomas may provide new targets for immune or biological therapies.


   Acknowledgements
 
We thank Patrick O. Brown and David Botstein for their support of this project and helpful comments.


   Footnotes
 
Address reprint requests to James D. Brooks, Department of Urology, Stanford University School of Medicine, Stanford, CA 94305. E-mail: jbrooks{at}stanford.edu

Supported by National Institutes of Health grants CA85129 and CA84967, the Kidney Cancer Association Eugene P. Schonfeld Award, and the Howard Hughes Medical Institute.

John P.T. Higgins and Rajesh Shinghal contributed equally to this work.

Accepted for publication December 9, 2002.


   References
Top
 Abstract
 Materials and Methods
 Results
 Discussion
 References
 

  1. Godley PA, Escobar MA: Renal cell carcinoma. Curr Opin Oncol 1998, 10:261-265[Medline]
  2. McLaughlin JK, Lipworth L: Epidemiologic aspects of renal cell cancer. Semin Oncol 2000, 27:115-123[Medline]
  3. Franklin JR, Figlin R, Belldegrun A: Renal cell carcinoma: basic biology and clinical behavior. Semin Urol Oncol 1996, 14:208-215[Medline]
  4. Couillard DR, DeVere White RW: Surgery of renal cell carcinoma. Urol Clin North Am 1993, 20:263-275[Medline]
  5. Yagoda A, Abi-Rached B, Petrylak D: Chemotherapy for advanced renal cell carcinoma: 1983–1993. Semin Oncol 1995, 22:42-60[Medline]
  6. Thrasher JB, Paulson DF: Prognostic factors in renal cancer. Urol Clin North Am 1993, 20:247-262[Medline]
  7. Strohmeyer T, Ackermann R: Classic and modern prognostic indicators in renal cell carcinoma: review of the literature. Urol Int 1991, 47:203-212[Medline]
  8. Reese JH: Renal cell carcinoma. Curr Opin Oncol 1992, 4:427-434[Medline]
  9. Amin MB, Tamboli P, Javidan J, Stricker H, de-Peralta Venturina M, Deshpande A, Menon M: Prognostic impact of histologic subtyping of adult renal epithelial neoplasms: an experience of 405 cases. Am J Surg Pathol 2002, 26:281-291[Medline]
  10. Zhao WP, Gnarra JR, Liu S, Knutsen T, Linehan WM, Whang-Peng J: Renal cell carcinoma: cytogenetic analysis of tumors and cell lines. Cancer Genet Cytogenet 1995, 82:128-139[Medline]
  11. Kovacs G, Fuzesi L, Emanual A, Kung HF: Cytogenetics of papillary renal cell tumors. Genes Chromosomes Cancer 1991, 3:249-255[Medline]
  12. Kovacs G, Akhtar M, Beckwith BJ, Bugert P, Cooper CS, Delahunt B, Eble JN, Fleming S, Ljungberg B, Medeiros LJ: The Heidelberg classification of renal cell tumours. J Pathol 1997, 183:131-133[Medline]
  13. Young AN, Amin MB, Moreno CS, Lim SD, Cohen C, Petros JA, Marshall FF, Neish AS: Expression profiling of renal epithelial neoplasms: a method for tumor classification and discovery of diagnostic molecular markers. Am J Pathol 2001, 158:1639-1651[Abstract/Free Full Text]
  14. Storkel S, Eble JN, Adlakha K, Amin M, Blute ML, Bostwick DG, Darson M, Delahunt B, Iczkowski K: Classification of renal cell carcinoma: workgroup no. 1; Union Internationale Contre le Cancer (UICC) and the American Joint Committee on Cancer (AJCC). Cancer 1997, 80:987-989[Medline]
  15. Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95:14863-14868[Abstract/Free Full Text]
  16. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403:503-511[Medline]
  17. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA: Molecular portraits of human breast tumours. Nature 2000, 406:747-752[Medline]
  18. Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, Iyer V, Jeffrey SS, Van de Rijn M, Waltham M: Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 2000, 24:227-235[Medline]
  19. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001, 98:5116-5121[Abstract/Free Full Text]
  20. Higgins JP, Warnke RA: CD30 expression is common in mediastinal large B-cell lymphoma. Am J Clin Pathol 1999, 112:241-247[Medline]
  21. Iyer NV, Kotch LE, Agani F, Leung SW, Laughner E, Wenger RH, Gassmann M, Gearhart JD, Lawler AM, Yu AY: Cellular and developmental control of O2 homeostasis by hypoxia-inducible factor 1 {alpha}. Genes Dev 1998, 12:149-162[Abstract/Free Full Text]
  22. Hu J, Discher DJ, Bishopric NH, Webster KA: Hypoxia regulates expression of the endothelin-1 gene through a proximal hypoxia-inducible factor-1 binding site on the antisense strand. Biochem Biophys Res Commun 1998, 245:894-899[Medline]
  23. Feldser D, Agani F, Iyer NV, Pak B, Ferreira G, Semenza GL: Reciprocal positive regulation of hypoxia-inducible factor 1 {alpha} and insulin-like growth factor 2. Cancer Res 1999, 59:3915-3918[Abstract/Free Full Text]
  24. Maxwell PH, Wiesener MS, Chang GW, Clifford SC, Vaux EC, Cockman ME, Wykoff CC, Pugh CW, Maher ER, Ratcliffe PJ: The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis. Nature 1999, 399:271-275[Medline]
  25. Clifford SC, Maher ER: Von Hippel-Lindau disease: clinical and molecular perspectives. Adv Cancer Res 2001, 82:85-105[Medline]
  26. Luo J, Zha S, Gage WR, Dunn TA, Hicks JL, Bennett CJ, Ewing CM, Platz EA, Ferdinandusse S, Wanders RJ: {alpha}-Methylacyl-CoA racemase: a new molecular marker for prostate cancer. Cancer Res 2002, 62:2220-2226[Abstract/Free Full Text]
  27. Rubin MA, Zhou M, Dhanasekaran SM, Varambally S, Barrette TR, Sanda MG, Pienta KJ, Ghosh D, Chinnaiyan AM: {alpha}-Methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer. JAMA 2002, 287:1662-1670[Abstract/Free Full Text]
  28. Sarlomo-Rikala M, Kovatich AJ, Barusevicius A, Miettinen M: CD117: a sensitive marker for gastrointestinal stromal tumors that is more specific than CD34. Mod Pathol 1998, 11:728-734[Medline]
  29. Nielsen TO, West RB, Linn SC, Alter O, Knowling MA, O’Connell JX, Zhu S, Fero M, Sherlock G, Pollack JR: Molecular characterisation of soft tissue tumours: a gene expression study. Lancet 2002, 359:1301-1307[Medline]
  30. Buchdunger E, Cioffi CL, Law N, Stover D, Ohno-Jones S, Druker BJ, Lydon NB: Abl protein-tyrosine kinase inhibitor STI571 inhibits in vitro signal transduction mediated by c-kit and platelet-derived growth factor receptors. J Pharmacol Exp Ther 2000, 295:139-145[Abstract/Free Full Text]
  31. Skinnider BF, Jones EC: Renal oncocytoma and chromophobe renal cell carcinoma: a comparison of colloidal iron staining and electron microscopy. Am J Clin Pathol 1999, 111:796-803[Medline]
  32. Tickoo SK, Lee MW, Eble JN, Amin M, Christopherson T, Zarbo RJ, Amin MB: Ultrastructural observations on mitochondria and microvesicles in renal oncocytoma, chromophobe renal cell carcinoma, and eosinophilic variant of conventional (clear cell) renal cell carcinoma. Am J Surg Pathol 2000, 24:1247-1256[Medline]
  33. Akhtar M, Kardar H, Linjawi T, McClintock J, Ali MA: Chromophobe cell carcinoma of the kidney: a clinicopathologic study of 21 cases. Am J Surg Pathol 1995, 19:1245-1256[Medline]
  34. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537[Abstract/Free Full Text]
  35. Garber ME, Troyanskaya OG, Schluens K, Petersen S, Thaesler Z, Pacyna-Gengelbach M, van de Rijn M, Rosen GD, Perou CM, Whyte RI: Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci USA 2001, 98:13784-13789[Abstract/Free Full Text]
  36. Martignoni G, Pea M, Chilosi M, Brunelli M, Scarpa A, Colato C, Tardanico R, Zamboni G, Bonetti F: Parvalbumin is constantly expressed in chromophobe renal carcinoma. Mod Pathol 2001, 14:760-767[Medline]
  37. Markovic-Lipkovski J, Brasanac D, Todorovic V, Muller CA, Muller GA: Immunomorphological characteristics of renal cell carcinoma. Histol Histopathol 1995, 10:651-659[Medline]
  38. Zbar B, Alvord WG, Glenn G, Turner M, Pavlovich CP, Schmidt L, Walther M, Choyke P, Weirich G, Hewitt SM: Risk of renal and colonic neoplasms and spontaneous pneumothorax in the Birt-Hogg-Dube syndrome. Cancer Epidemiol Biomarkers Prev 2002, 11:393-400[Abstract/Free Full Text]
  39. Takahashi M, Rhodes DR, Furge KA, Kanayama H, Kagawa S, Haab BB, Teh BT: Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification. Proc Natl Acad Sci USA 2001, 98:9754-9759[Abstract/Free Full Text]
  40. Boer JM, Huber WK, Sultmann H, Wilmer F, von Heydebreck A, Haas S, Korn B, Grunawan B, Vente A, Fuzesi L, Vingron M, Poustka A: Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31,500-element cDNA array. Genome Res 2001, 11:1861-1870[Abstract/Free Full Text]
  41. Belbin TJ, Singh B, Barber I, Socci N, Wenig B, Smith R, Prystowsky MB, Childs G: Molecular classification of head and neck squamous cell carcinoma using cDNA microarrays. Cancer Res 2002, 62:1184-1190[Abstract/Free Full Text]
  42. Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, Behm FG, Raimondi SC, Relling MV, Patel A: Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 2002, 1:133-143[Medline]
  43. Ferrando AA, Neuberg DS, Staunton J, Loh ML, Huard C, Raimondi SC, Behm FG, Pui CH, Downing JR, Gilliland DG: Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell 2002, 1:75-87[Medline]
  44. Armstrong SA, Staunton JE, Silverman LB, Pieters R, den Boer ML, Minden MD, Sallan SE, Lander ES, Golub TR, Korsmeyer SJ: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat Genet 2002, 30:41-47[Medline]



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[Abstract] [Full Text] [PDF]

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 J. Exp. Med.Home page
K. G. Leong, K. Niessen, I. Kulic, A. Raouf, C. Eaves, I. Pollet, and A. Karsan
Jagged1-mediated Notch activation induces epithelial-to-mesenchymal transition through Slug-induced repression of E-cadherin
J. Exp. Med., November 26, 2007; 204(12): 2935 - 2948.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
J. Jones and T. A. Libermann
Genomics of Renal Cell Cancer: The Biology Behind and the Therapy Ahead
Clin. Cancer Res., January 15, 2007; 13(2): 685s - 692s.
[Abstract] [Full Text] [PDF]

Home page
 J. Biol. Chem.Home page
V. S. Tompkins, J. Hagen, A. A. Frazier, T. Lushnikova, M. P. Fitzgerald, A. di Tommaso, V. Ladeveze, F. E. Domann, C. M. Eischen, and D. E. Quelle
A Novel Nuclear Interactor of ARF and MDM2 (NIAM) That Maintains Chromosomal Stability
J. Biol. Chem., January 12, 2007; 282(2): 1322 - 1333.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
K.-i. Shioi, A. Komiya, K. Hattori, Y. Huang, F. Sano, T. Murakami, N. Nakaigawa, T. Kishida, Y. Kubota, Y. Nagashima, et al.
Vascular Cell Adhesion Molecule 1 Predicts Cancer-Free Survival in Clear Cell Renal Carcinoma Patients
Clin. Cancer Res., December 15, 2006; 12(24): 7339 - 7346.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
S. Rohan, J. J. Tu, J. Kao, P. Mukherjee, F. Campagne, X. K. Zhou, E. Hyjek, M. A. Alonso, and Y.-T. Chen
Gene Expression Profiling Separates Chromophobe Renal Cell Carcinoma from Oncocytoma and Identifies Vesicular Transport and Cell Junction Proteins as Differentially Expressed Genes
Clin. Cancer Res., December 1, 2006; 12(23): 6937 - 6945.
[Abstract] [Full Text] [PDF]

Home page
 J. Mol. Diagn.Home page
K. Hotakainen, S. Lintula, B. Ljungberg, P. Finne, A. Paju, U.-H. Stenman, and J. Stenman
Expression Of Human Chorionic Gonadotropin {beta}-Subunit Type I Genes Predicts Adverse Outcome In Renal Cell Carcinoma
J. Mol. Diagn., November 1, 2006; 8(5): 598 - 603.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
Y.-T. Chen, J. J. Tu, J. Kao, X. K. Zhou, and M. Mazumdar
Messenger RNA Expression Ratios among Four Genes Predict Subtypes of Renal Cell Carcinoma and Distinguish Oncocytoma from Carcinoma
Clin. Cancer Res., September 15, 2005; 11(18): 6558 - 6566.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
J. Jones, H. Otu, D. Spentzos, S. Kolia, M. Inan, W. D. Beecken, C. Fellbaum, X. Gu, M. Joseph, A. J. Pantuck, et al.
Gene Signatures of Progression and Metastasis in Renal Cell Cancer
Clin. Cancer Res., August 15, 2005; 11(16): 5730 - 5739.
[Abstract] [Full Text] [PDF]

Home page
 Cancer Res.Home page
X. J. Yang, M.-H. Tan, H. L. Kim, J. A. Ditlev, M. W. Betten, C. E. Png, E. J. Kort, K. Futami, K. A. Furge, M. Takahashi, et al.
A Molecular Classification of Papillary Renal Cell Carcinoma
Cancer Res., July 1, 2005; 65(13): 5628 - 5637.
[Abstract] [Full Text] [PDF]

Home page
 Cancer Res.Home page
A. Togashi, T. Katagiri, S. Ashida, T. Fujioka, O. Maruyama, Y. Wakumoto, Y. Sakamoto, M. Fujime, Y. Kawachi, T. Shuin, et al.
Hypoxia-Inducible Protein 2 (HIG2), a Novel Diagnostic Marker for Renal Cell Carcinoma and Potential Target for Molecular Therapy
Cancer Res., June 1, 2005; 65(11): 4817 - 4826.
[Abstract] [Full Text] [PDF]

Home page
 J. Mol. Diagn.Home page
A. N. Schuetz, Q. Yin-Goen, M. B. Amin, C. S. Moreno, C. Cohen, C. D. Hornsby, W. L. Yang, J. A. Petros, M. M. Issa, J. G. Pattaras, et al.
Molecular Classification of Renal Tumors by Gene Expression Profiling
J. Mol. Diagn., May 1, 2005; 7(2): 206 - 218.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
H. Sultmann, A. v. Heydebreck, W. Huber, R. Kuner, A. Buness, M. Vogt, B. Gunawan, M. Vingron, L. Fuzesi, and A. Poustka
Gene Expression in Kidney Cancer Is Associated with Cytogenetic Abnormalities, Metastasis Formation, and Patient Survival
Clin. Cancer Res., January 15, 2005; 11(2): 646 - 655.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
M.-H. Tan, C. G. Rogers, J. T. Cooper, J. A. Ditlev, T. J. Maatman, X. Yang, K. A. Furge, and B. T. Teh
Gene Expression Profiling of Renal Cell Carcinoma
Clin. Cancer Res., September 15, 2004; 10(18): 6315S - 6321S.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
S. Volinia, R. Evangelisti, F. Francioso, D. Arcelli, M. Carella, and P. Gasparini
GOAL: automated Gene Ontology analysis of expression profiles
Nucleic Acids Res., July 1, 2004; 32(suppl_2): W492 - W499.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
D. R. Rhodes, J. Yu, K. Shanker, N. Deshpande, R. Varambally, D. Ghosh, T. Barrette, A. Pandey, and A. M. Chinnaiyan
Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression
PNAS, June 22, 2004; 101(25): 9309 - 9314.
[Abstract] [Full Text] [PDF]

Home page
 Cancer Res.Home page
K. A. Furge, K. A. Lucas, M. Takahashi, J. Sugimura, E. J. Kort, H.-o. Kanayama, S. Kagawa, P. Hoekstra, J. Curry, X. J. Yang, et al.
Robust Classification of Renal Cell Carcinoma Based on Gene Expression Data and Predicted Cytogenetic Profiles
Cancer Res., June 15, 2004; 64(12): 4117 - 4121.
[Abstract] [Full Text] [PDF]

Home page
 Mol. Biol. CellHome page
J. P.T. Higgins, L. Wang, N. Kambham, K. Montgomery, V. Mason, S. U. Vogelmann, K. V. Lemley, P. O. Brown, J. D. Brooks, and M. van de Rijn
Gene Expression in the Normal Adult Human Kidney Assessed by Complementary DNA Microarray
Mol. Biol. Cell, February 1, 2004; 15(2): 649 - 656.
[Abstract] [Full Text] [PDF]

Home page
 Biol Res NursHome page
A. K. Cashion, C. J. Driscoll, and O. Sabek
Emerging Genetic Technologies in Clinical and Research Settings
Biol Res Nurs, January 1, 2004; 5(3): 159 - 167.
[Abstract] [PDF]

Home page
 Am. J. Pathol.Home page
A. Tsuchiya, M. Sakamoto, J. Yasuda, M. Chuma, T. Ohta, M. Ohki, T. Yasugi, Y. Taketani, and S. Hirohashi
Expression Profiling in Ovarian Clear Cell Carcinoma: Identification of Hepatocyte Nuclear Factor-1{beta} as a Molecular Marker and a Possible Molecular Target for Therapy of Ovarian Clear Cell Carcinoma
Am. J. Pathol., December 1, 2003; 163(6): 2503 - 2512.
[Abstract] [Full Text]

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