Stratification of clear cell renal cell carcinoma (ccRCC) genomes by gene-directed copy number alteration (CNA) analysis.

Tumorigenic processes are understood to be driven by epi-/genetic and genomic alterations from single point mutations to chromosomal alterations such as insertions and deletions of nucleotides up to gains and losses of large chromosomal fragments including products of chromosomal rearrangements e.g. fusion genes and proteins. Overall comparisons of copy number alterations (CNAs) presented in 48 clear cell renal cell carcinoma (ccRCC) genomes resulted in ratios of gene losses versus gene gains between 26 ccRCC Fuhrman malignancy grades G1 (ratio 1.25) and 20 G3 (ratio 0.58). Gene losses and gains of 15762 CNA genes were mapped to 795 chromosomal cytoband loci including 280 KEGG pathways. CNAs were classified according to their contribution to Fuhrman tumour gradings G1 and G3. Gene gains and losses turned out to be highly structured processes in ccRCC genomes enabling the subclassification and stratification of ccRCC tumours in a genome-wide manner. CNAs of ccRCC seem to start with common tumour related gene losses flanked by CNAs specifying Fuhrman grade G1 losses and CNA gains favouring grade G3 tumours. The appearance of recurrent CNA signatures implies the presence of causal mechanisms most likely implicated in the pathogenesis and disease-outcome of ccRCC tumours distinguishing lower from higher malignant tumours. The diagnostic quality of initial 201 genes (108 genes supporting G1 and 93 genes G3 phenotypes) has been successfully validated on published Swiss data (GSE19949) leading to a restricted CNA gene set of 171 CNA genes of which 85 genes favour Fuhrman grade G1 and 86 genes Fuhrman grade G3. Regarding these gene sets overall survival decreased with the number of G3 related gene losses plus G3 related gene gains. CNA gene sets presented define an entry to a gene-directed and pathway-related functional understanding of ongoing copy number alterations within and between individual ccRCC tumours leading to CNA genes of prognostic and predictive value.

Mass spectrometric and peptide chip characterization of an assembled epitope: analysis of a polyclonal antibody model serum directed against the Sjøgren/systemic lupus erythematosus autoantigen TRIM21.

We demonstrate the development of a mass spectrometry-based epitope-mapping procedure in combination with Western blot analysis that works also with antigens that are insoluble in nondenaturing buffers consuming minute amounts of antigen (approximately 200 pmol) and antibody (approximately 15 pmol), respectively. A polyclonal anti-TRIM21 rabbit antibody serum is applied as a model serum for future patient analyses to set up the system. The major epitope that is recognized by the anti-TRIM21 serum spans the central TRIM21 region LQ-ELEKDEREQLRILGE-KE, showing that immunization with a 139-amino acid residue long peptide resulted in a 'monospecific' polyclonal antibody repertoire. Protein structure investigations, secondary structure predictions, and surface area calculations revealed that the best matching partial sequence to fulfill all primary and secondary structure requirements was the four amino acid spanning motif 'L-E-Q-L', which is present in both the sequential and the α-helical peptide conformation. Peptide chip analyses confirmed the mass spectrometric results and showed that the peptide chip platform is an appropriate method for displaying secondary structure-relying epitope conformations. As the same secondary structures are present in vivo, patient antibody screening, e.g., to identify subgroups of patients according to distinct epitope antibody reactivities, is feasible.

Oxidatively modified LDL particles in the human placenta in early and late onset intrauterine growth restriction.

INTRODUCTION: Reduced serum LDL concentrations have been observed in pregnancies complicated by intrauterine growth restriction (IUGR) as compared to healthy pregnant women. Since increased oxidative stress has been suggested to play a major role in IUGR we now hypothesized that the lower LDL concentrations are accompanied by an accumulation of oxidized LDLs in the placenta. METHODS: Fifteen placentas of near term and preterm born IUGR, and a gestational age matched control group (CTRL n = 15) were analyzed. Placental minimal modified LDL and fully oxidized LDL particles were measured by ELISA, and by immunohistochemistry, and were related to maternal and fetal serum lipid profiles. RESULTS: We found fully oxidized LDL but not minimal modified LDL being increased in the preterm subgroup of IUGR (n = 10) as compared to preterm CTRL (n = 10; p < 0.05). An increased staining intensity of trophoblasts in preterm IUGR subjects as compared to preterm CTRL has been confirmed by immunohistochemistry (p < 0.05). No difference could be found between the term groups (n = 5 each). Correlation analysis revealed an inverse relationship of maternal LDL (ρ = −0.49, p = 0.03) and fetal HDL cholesterol (ρ = −0.46, p = 0.04) with placental fully oxidized LDL particle concentration within preterms. DISCUSSION: IUGR is a heterogeneous entity. Different pathomechanisms seem to underlie the disease in preterm and term subjects with oxidation of LDL within the placenta possibly taking place in preterm IUGRs. CONCLUSIONS: We conclude that the reduced maternal LDL cholesterol concentration in IUGR pregnancies is attributed to increased accumulation of oxidized LDL particles within the placenta at least in early onset IUGR

Network analysis of transcriptional regulation in response to intramuscular interferon-ß-1a multiple sclerosis treatment.

Interferon-ß (IFN-ß) is one of the major drugs for multiple sclerosis (MS) treatment. The purpose of this study was to characterize the transcriptional effects induced by intramuscular IFN-ß-1a therapy in patients with relapsing-remitting form of MS. By using Affymetrix DNA microarrays, we obtained genome-wide expression profiles of peripheral blood mononuclear cells of 24 MS patients within the first 4 weeks of IFN-ß administration. We identified 121 genes that were significantly up- or downregulated compared with baseline, with stronger changed expression at 1 week after start of therapy. Eleven transcription factor-binding sites (TFBS) are overrepresented in the regulatory regions of these genes, including those of IFN regulatory factors and NF-κB. We then applied TFBS-integrating least angle regression, a novel integrative algorithm for deriving gene regulatory networks from gene expression data and TFBS information, to reconstruct the underlying network of molecular interactions. An NF-κB-centered sub-network of genes was highly expressed in patients with IFN-ß-related side effects. Expression alterations were confirmed by real-time PCR and literature mining was applied to evaluate network inference accuracy.

Putative biomarker genes for grading clear cell renal cell carcinoma.

OBJECTIVE: The initial objective of this renal cancer study was to identify gene sets in clear cell renal cell carcinoma (ccRCC) to support grading of ccRCC histopathology. MATERIALS AND METHODS: Preselected ccRCC tumor tissues of grade 1 (G1, n = 14) and grade 3 (G3, n = 15) as well es 14 normal kidney tissues thereof were subjected to microarray expression analysis using Human Genome U133 Plus 2.0 Array. Event ratio scoring, hierarchical clustering and principal component analysis were used to determine gene sets that distinguish expression profiles from normal kidney tissue, G1 and G3 tumor tissues. RESULTS: An initial set of 73 genes provided seven gene subclusters (SC01 to SC07) that distinguish RNA expression profiles from G1, G3 tumor and normal kidney tissues. A ranked list of 24 genes was determined that separated G1 from G3 tumors in high concordance with histopathological grading confirmed by immunohistochemical analysis of ceruloplasmin protein expression. CONCLUSION: A final set of 24 genes has been determined awaiting further validation on the RNA as well as on the protein level by studying an additional cohort of ccRCC patients. A reliable separation of G1 and G3 tumor grades will be instrumental to foster and direct the administration of upcoming targeted therapeutics of ccRCC tumors in a more predictive and reliable manner.

Gene expression analysis in clear cell renal cell carcinoma using gene set enrichment analysis for biostatistical management.

OBJECTIVE: To improve the workflow for standardizing the statistical interpretation provides an opportunity for the analysis of gene expression in clear cell renal cell carcinoma (ccRCC). RCC as a solid tumour entity represents a very suitable tumour model for such investigations. Although it is possible to investigate expression profiles by microarray technologies, the main problem is how to adequately interpret the accumulated mass of data derived from microarray technologies. There is a clear lack of a defined, consistent and comparable biostatistical analysis system, with no specific biostatistical standard methodology being available to compare the results of microarray analyses. We used the gene set enrichment analysis (GSEA) method to analyze microarray data from RCC tissue. The present study aimed to analyze differential expression profiles and establish biomarkers suitable for prognostication at the time of renal surgery by comparing RCC patients with long-term survival data against RCC samples of patients with poorly differentiated (grade 3) RCC, concomitant metastatic disease and short survival. PATIENTS AND METHODS: In the present study, a total of 29 ccRCC fresh-frozen tissue samples were used; 14 samples from grade 1 (G1) RCC patients without metastatic disease and 15 from grade 3 (G3) RCC patients with synchronous metastatic disease. Expression profiling was performed with the Human Genome U133 Plus 2.0 Array (Affymetrix Corp., Santa Clara, CA, USA). Clinical data and long-term follow-up were obtained for all patients. The primary probe level analysis was performed using the Affymetrix MAS 5 algorithm. Further statistical processing was carried out by GSEA, using the Molecular Signatures Database, MSigDB (http://www.broad.mit.edu/gsea/msigdb/index.jsp). After selecting gene sets with the highest leading edge subsets, a cluster and a further analyses based on MSigDB data bank analysis was performed. RESULTS: In total, 15 poorly G3 ccRCC, 14 well differentiated G1 ccRCC and 14 normal renal tissue samples were analyzed for comparative gene expression profiling. There were 12 of 15 G3 ccRCC patients who had synchronous metastatic disease at the time of surgery (pN+ and/or distant metastases: pN+ only = 4, M+ only = 11 and pN+M+ = 3). The GSEA identified 700 gene sets. Out of these, 120 sets with the highest leading edge subset were selected monitored by hierarchical clustering G1 vs G3. Comparative analysis using the the MSigDB data bank for pathway network identified 16 gene sets that were differentially strongly over- or underexpressed in G3 vs G1 tumours and are involved in various aspects of tumour physiology, such as metastases and cell motility, signalling and cell proliferation, as well as gene products that are involved in the building of the extracellular matrix and as cell surface markers. CONCLUSIONS: We analyzed microarray data of gene expression in ccRCC comparing poorly differentiated and well differentiated tumour tissue samples. Using GSEA, we found a number of genes set candidates relevant to biological network processes with high complexity; conspicuously, these comprised members of the interleukin- and chemokine-family, cyclin-dependent kinases, angiogenic growth factors and transcriptional factors. This suggests that, in poorly differentiated aggressive ccRCC, there may be a limited number of gene sets that are responsible for the very aggressive biological behaviour. This comparison performed at a gene set level enables the identification of such congruency between different gene sets and whole data sets with respect to a specific biological question. GSEA embedded in the statistical workflow procedure for the suitable preparation of expression data may improve the analysis and avoid missing changes at the molecular level. A systematic approach such as GSEA is clearly needed to analyze raw data from microarray analyses, although these data can only be descriptive and the mass of raw data is derived from a relatively small number of tissue samples. However, consistent alterations of gene expression found in specific tumour entities may allow a better understanding of certain aspects of specific tumour biology. Therefore, the molecular characterization of individual tumours may potentially be useful for the better individual assessment of prognosis and, finally, the identification of biomarkers and targets of specific treatments may eventually help to improve treatment.

Molecular signatures and new candidates to target the pathogenesis of rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic, inflammatory joint disease of unknown etiology and pronounced interpatient heterogeneity. To characterize RA at the molecular level and to uncover pathomechanisms, we performed genome-wide gene expression analysis. We identified a set of 1,054 genes significantly deregulated in pair-wise comparisons between RA and osteoarthritis (OA) patients, RA and normal donors (ND), or OA and ND. Correlation analysis revealed gene sets regulated identically in all three groups. As a prominent example secreted phosphoprotein 1 (SPP1) was identified to be significantly upregulated in RA compared with both OA and ND. SPP1 expression was found to correlate with genes expressed during an inflammatory response, T-cell activation and apoptosis, suggesting common underlying regulatory networks. A subclassification of RA patients was achieved on the basis of proteoglycan 4 (PRG4) expression, distinguishing PRG4 high and low expressors and reflecting the heterogeneity of the disease. In addition, we found that low PRG4 expression was associated with a more aggressive disease stage, which is in accordance with PRG4 loss-of-function mutations causing camptodactyly-arthropathy-coxa vara-pericarditis syndrome. Altogether we provide evidence for molecular signatures of RA and RA subclasses, sets of new candidate genes as well as for candidate gene networks, which extend our understanding of disease mechanisms and may lead to an improved diagnosis.

Mass spectrometric and peptide chip epitope analysis on the RA33 autoantigen with sera from rheumatoid arthritis patients.

As the potential of epitope chips for routine application in diagnostics relies on the careful selection of peptides, reliable epitope mapping results are of utmost interest to the medical community. Mass spectrometric epitope mapping in combination with peptide chip analysis showed that autoantibodies from patients who suffered from rheumatoid arthritis (RA) were directed against distinct surface structures on the full-length human autoantigen RA33 as well as against partial sequences. Using the combined mass spectrometric epitope extraction and peptide chip analysis approach, four sequence motifs on RA33 emerged as immuno-positive, showing that epitopes were not randomly distributed on the entire RA33 amino acid sequence. A sequential epitope motif ((245)GYGGG(249)) was determined on the C-terminal part of RA33 which matched with the Western blot patient screening results using the full-length protein and, thus, was regarded as a disease-associated epitope. Other epitope motifs were found on N-terminal partial sequences ((59)RSRGFGF(65), (111)KKLFVG(116)) and again on the C-terminal part ((266)NQQPSNYG(273)) of RA33. As recognition of these latter three motifs was also recorded by peptide chip analysis using control samples which were negative in the Western blot screening, these latter motifs were regarded as "cryptic epitopes". Knowledge of disease-associated epitopes is crucial for improving the design of a customized epitope peptide chip for RA and mass spectrometric epitope mapping pivotally assisted with selecting the most informative peptide(s) to be used for future diagnostic purposes.

Time course transcriptomics of IFNB1b drug therapy in multiple sclerosis.

Despite its generalized use as drug therapy for multiple sclerosis (MS), the molecular mechanisms of action of interferon beta (IFNB) are still poorly understood. IFNB therapy is long-termed and clinical effects are not immediate, therefore reliable early biomarkers for IFNB activity should maintain a differential expression over time, but longitudinal studies at a transcriptional level have been rare. Microarrays were used to monitor 18 IFNB1b treated MS patients at four time points spanning a period of 1 year. Genes showing in the majority of patients the greatest and most consistent changes in their expression levels were studied. Interferon regulated genes were significantly overrepresented. Fifteen markers were differentially expressed during all three time points and followed a consistent time course pattern: EIF2AK2, IFI6, IFI44, IFI44L, IFIH1, IFIT1, IFIT2, IFIT3, ISG15, MX1, OASL, RSAD2, SN, XAF1 and the marker 238704_at. Except for the last one, these biomarkers were all formerly identified as being indicative for IFNB activity. Expression changes were both early detectable and long lasting and could thus be optimal biomarkers for IFNB activity in long-term studies. Other known biomarkers of IFNB activity were found to be differentially expressed just for certain periods after therapy onset: Interleukin-8 was a short lasting marker and changes in STAT1 were detected with delay.

Mass spectrometric and peptide chip epitope mapping of rheumatoid arthritis autoantigen RA33.

The protein termed RA33 was determined to be one major autoantigen in rheumatoid arthritis (RA) patients and antiRA33 auto-antibodies were found to appear shortly after onset of RA. They are often detectable before a final diagnosis can be made in the clinic. The aim of our study is to characterise the epitope of a monoclonal antiRA33 antibody on recombinant RA33 using mass spectrometric epitope mapping. Recombinant RA33 has been subjected to BrCN cleavage and fragments were separated by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). Subsequent in-gel proteolytic digestion and mass spectrometric analysis determined the partial sequences in the protein bands. Western blotting of SDS-PAGE-separated protein fragments revealed immuno-positive, i.e. epitope-containing bands. BrCN-derived RA33 fragments were also separated by high- performance liquid chromatography (HPLC) and immuno-reactivity of peptides was measured by dot-blot analysis with the individual HPLC fractions after partial amino acid sequences were determined. The epitope region identified herewith was compared to data from peptide chip analysis with 15-meric synthetic peptides attached to a glass surface. Results from all three analyses consistently showed that the epitope of the monoclonal antiRA33 antibody is located in the aa79-84 region on recombinant RA33; the epitope sequence is MAARPHSIDGRVVEP. Sequence comparisons of the 15 best scoring peptides from the peptide chip analysis revealed that the epitope can be separated into two adjacent binding parts. The N-terminal binding parts comprise the amino acid residues "DGR", resembling the general physico-chemical properties "acidic/polar-small-basic". The C-terminal binding parts contain the amino acid residues "VVE", with the motif "hydrophobic-gap-acidic". The matching epitope region that emerged from our analysis on both the full-length protein and the 15-meric surface bound peptides suggests that peptide chips are indeed suitable tools for screening patterns of autoantibodies in patients suffering from autoimmune diseases.

Molecular subtypes of systemic sclerosis in association with anti-centromere antibodies and digital ulcers.

The objective of this study was to identify molecular profiles that may distinguish clinical subtypes in systemic sclerosis (SSc). Large-scale gene expression profiling was performed on peripheral blood (PB) from 12 SSc patients and 6 healthy individuals. Significance analysis of microarrays, two-way hierarchical cluster analysis and PANTHER (Protein ANalysis THrough Evolutionary Relationships) ontology classification were used to analyze the data. Quantitative PCR was applied for validation in a cohort of 43 SSc patients. The results show that the expression of genes involved in immune defense, cell cycle and signal transduction was significantly elevated in PB of SSc patients (n=12) compared with healthy individuals (n=6). SSc patients could be stratified into subgroups based on differential expression of genes induced by type I interferon (IFN) and genes involved in antimicrobial (AM) activity. Differential expression of type I IFN or AM signature genes was validated and extended in an independent cohort of 31 patients by quantitative PCR. Low expression of IFN response genes was associated with the presence of anti-centromere antibodies, whereas increased expression was associated with the appearance of digital ulcers. In conclusion, patients with SSc can be classified on the basis of differential expression of immune defense genes. Differences in the activity of the type I IFN response program stratify patients into two clinically relevant subgroups.

Gene expression module-based chemical function similarity search.

Investigation of biological processes using selective chemical interventions is generally applied in biomedical research and drug discovery. Many studies of this kind make use of gene expression experiments to explore cellular responses to chemical interventions. Recently, some research groups constructed libraries of chemical related expression profiles, and introduced similarity comparison into chemical induced transcriptome analysis. Resembling sequence similarity alignment, expression pattern comparison among chemical intervention related expression profiles provides a new way for chemical function prediction and chemical-gene relation investigation. However, existing methods place more emphasis on comparing profile patterns globally, which ignore noises and marginal effects. At the same time, though the whole information of expression profiles has been used, it is difficult to uncover the underlying mechanisms that lead to the functional similarity between two molecules. Here a new approach is presented to perform biological effects similarity comparison within small biologically meaningful gene categories. Regarding gene categories as units, a reduced similarity matrix is generated for measuring the biological distances between query and profiles in library and pointing out in which modules do chemical pairs resemble. Through the modularization of expression patterns, this method reduces experimental noises and marginal effects and directly correlates chemical molecules with gene function modules.

Overlapping humoral autoimmunity links rheumatic fever and the antiphospholipid syndrome.

OBJECTIVE: Rheumatic fever (RF) and the antiphospholipid syndrome (APS) are autoimmune diseases that share similar cardiac and neurological pathologies. We assessed the presence of shared epitopes between M protein, N-acetyl-beta-D-glucosamine (GlcNAc) and beta2 glycoprotein-I (beta2GPI), the pathogenic molecules engaged in these autoimmune conditions. METHODS: Sera from the APS patients were affinity-purified on beta2GPI and beta2GPI-related peptide columns. Sera from RF patients were affinity-purified on protein G column. The beta2GPI and M protein-related peptides were prepared by conventional solid-phase peptide synthesis. The enzyme-linked immunosorbent assay direct binding and inhibition studies were performed on the RF and APS sera for the presence, and cross-reactivity, of antibodies against beta2GPI, beta2GPI-related peptides, streptococcal M protein, M-derived peptides and GlcNAc. RESULTS: Antibodies (Abs) to beta2GPI were found in 24.4% of 90 RF patients. Antibodies against various beta2GPI-related peptides were found in 1.1-36.7% of the patients. The immunoglobulin G sera from RF patients possessed significant anti-beta2GPI activity, while sera from APS patients contained a considerable anti-streptococcal M protein as well as anti-GlcNAc activity. Furthermore, affinity-purified anti-beta2GPI and anti-beta2GPI-related peptide Abs from APS patients cross-reacted with streptococcal M protein and M5 peptide, while beta2GPI and beta2GPI-related peptides inhibited anti-streptococcal M protein activity from RF patients. The results were confirmed by immunoblot analyses. The beta2GPI also inhibited anti-GlcNAc activity from APS patients with chorea. CONCLUSIONS: The results of our study, showing a considerable overlap of humoral immunity in RF and APS, support a hypothesis that common pathogenic mechanisms underlie the development of cardiac valve lesions and Central Nervous System abnormalities in both diseases.

[Research strategies towards a holistic characterization of rheumatoid arthritis--a systems biology approach]. / Forschungsstrategien zur ganzheitlichen Charakterisierung der Rheumatoiden Arthritis -- Ein systembiologischer Ansatz.

Genome-wide screening methods used in functional genomics (genome, transcriptome, proteome and metabolom analysis) have increasingly been conducted in integrative research platforms to enable a comprehensive holistic characterization of multifactorial polygenic diseases. First results of this research strategy demonstrate that extended data sets are compiled whose quality is ensured by the application of standard operating procedures (SOPs) and the integration of specific laboratory information management systems (LIMS). Experimental data derived from this technology and methodology platform are obtained by applying standardized sampling procedures followed by comprehensive experimental validation and bioinformatic comparisons with the world knowledge publicly available. This research strategy should finally lead to a holistic understanding of the pathogenesis presented in rheumatoid arthritis by identifying disease-associated regulatory networks (pathways) and assigning them to cell populations involved in the disease mechanisms. In addition, it has to be investigated to what extent genetic as well as epigenetic factors direct disease initiation and progression in potential conjunction with environmental impacts (infections, smoking, etc.).

Gene expression profiling of arthritis using a QTL chip reveals a complex gene regulation of the Cia5 region in mice.

One of the major quantitative trait loci (QTLs) associated with arthritis in crosses between B10.RIII and RIIIS/J mice is the Cia5 on chromosome 3. Early in the congenic mapping process it was clear that the locus was complex, consisting of several subloci with small effects. Therefore, we developed two novel strategies to dissect a QTL: the partial advanced inter-cross (PAI) strategy, with which we recently found the Cia5 region to consist of three loci, Cia5, Cia21 and Cia22, and now we introduce the QTL-chip strategy, where we have combined congenic mapping with a QTL-restricted expression profiling using a novel microarray design. The expression of QTL genes was compared between parental and congenic mice in lymph node, spleen and paw samples in five biological replicates and in dye-swapped experiments at three time points during the induction phase of arthritis. The QTL chip approach revealed 4 genes located in Cia21, differently expressed in lymph nodes, and 14 genes in Cia22, located within two clusters. One cluster contains six genes, differently expressed in spleen, and the second cluster contains eight genes, differently expressed in paws. We conclude the QTL-chip strategy to be valuable in the selection of candidate genes to be prioritized for further investigation.

Intergenomic consensus in multifactorial inheritance loci: the case of multiple sclerosis.

Genetic linkage and association studies define chromosomal regions, quantitative trait loci (QTLs), which influence the phenotype of polygenic diseases. Here, we describe a global approach to determine intergenomic consensus of those regions in order to fine map QTLs and select particularly promising candidate genes for disease susceptibility or other polygenic traits. Exemplarily, human multiple sclerosis (MS) susceptibility regions were compared for sequence similarity with mouse and rat QTLs in its animal model experimental allergic encephalomyelitis (EAE). The number of intergenomic MS/EAE consensus genes (295) is significantly higher than expected if the animal model was unrelated to the human disease. Hence, this approach contributes to the empirical evaluation of animal models for their applicability to the study of human diseases.

DNA microarray technology and its applications in dermatology.

The use of DNA microarray technology in biomedical research has dramatically increased during the past years. In the present report, we provide an overview on the basic DNA microarray technology and biostatistical methods for gene expression analysis. A focus is then put on its applications in dermatological research. In recent years, a series of gene expression studies have been performed for various dermatological diseases, such as malignant melanoma, psoriasis and lupus erythematosus. These analyses have identified interesting target genes as well as putative disease susceptibility loci. However, further functional studies will be needed for a more complete understanding of the pathogenesis of these diseases. This may be performed by means of the recently developed RNA interference technology. Besides its role in large-scale gene expression studies, DNA microarray technology has proved to be a valuable tool for genomic screens of genetic alterations, e.g. single nucleotide polymorphisms. These play a role in tumour development and progression, and also function as genetic markers for disease susceptibility. Taken together, DNA microarray technology opens enormous perspectives for dermatologists. It may help us understand the complex pathogenesis of a wide variety of dermatologic diseases and identify their genetic background.

Differential expression of thrombospondin 2 in primary and metastatic malignant melanoma.

In the present report we used oligonucleotide microarray analysis for the identification of genes characterizing the late-stage metastatic phenotype of malignant melanoma. A panel of 5,600 genes was analysed in a low aggressive and a highly aggressive (metastatic) human malignant melanoma cell line, respectively. More than 300 differentially regulated genes were identified. High metastatic potential correlated with upregulated mRNA expression in the groups of cytoskeletal proteins, apoptosis and cell cycle proteins, GTP binding proteins and oncogenes, extracellular ligands and receptors, transcription and translation factors. In contrast, most angiogenesis factors, extracellular matrix molecules, and melanoma-specific antigens were downregulated. Particular target genes were further analysed by in situ hybridization and immunohistochemical staining of primary malignant melanomas and melanoma metastases. Here, we show that thrombospondin 2, an extracellular matrix molecule which was differentially regulated in the microarray analysis, was strongly expressed in melanoma metastases, but not in primary tumours. The identification of thrombospondin 2 as a target molecule emphasizes the importance of cell-matrix interactions for the pathogenesis of malignant melanoma metastasis and may open future perspectives for treatment of this tumour.