Genomic deletions at 1p and 14q are associated with an abnormal cDNA microarray gene expression pattern in meningiomas but not in schwannomas

The molecular pathology of meningiomas and shwannomas involve the inactivation of the NF2 gene to generate grade I tumors. Genomic losses at 1p and 14q are observed in both neoplasms, although more frequently in meningiomas. The inactivation of unidentified genes located in these regions appears associated with tumor progression in meningiomas, but no clues to its molecular/clinical meaning are available in schwannomas. Recent microarray gene expression studies have demonstrated the existence of molecular subgroups in both entities. In the present study, we correlated the presence of genomic deletions at 1p, 14q, and 22q with the expression patterns of 96 tumor-related genes obtained by cDNA low-density microarrays in a series of 65 tumors including 42 meningiomas and 23 schwannomas. Two expression pattern groups were identified by cDNA mycroarray analysis when compared to the expression pattern in normal control RNA in both meningiomas and schwannomas, each one with patterns similar and different from the normal control. Meningioma and schwannoma subgroups differed in the expression of 38 and 16 genes, respectively. Using MLPA and microsatellites, we identified genomic losses at 1p, 14q, and 22q at nonrandom frequencies (12.5-69%) in meningiomas and schwannomas. Losses at 22q were almost equally frequent in both molecular expression subgroups in both neoplasms. However, deletions at 1p and 14q accumulated in meningiomas with a gene expression pattern different from the normal pattern, whereas the inverse situation occurred in schwannomas. Those anomalies characterized the schwannomas with expression pattern similar to the normal control. These findings suggest that deletions at 1p and 14q enhance the development of an abnormal tumor-related gene expression pattern in meningiomas, but this fact is not corroborated in schwannomas. (C) 2010 Elsevier Inc. All rights reserved.

Loss of samples in the tissue microarray technique: comparison between slides using adhesive tape and silanized slides

The tissue microarray (TMA) technique allows multiple tissue samples in a single block. Commercial adhesive tape is used to avoid the loss of tissue samples during the immunostaining process. Few reports exist in the literature comparing the use of these adhesive tapes to other adhesive techniques. The objective of this study was to compare loss of sections adhered to slides using commercial adhesive tapes versus using silanized only slides. TMA was constructed with varying tissues using a fixed-base device (Beecher Instruments), placing 108 cylinders of 1 mm diameter in duplicate, spaced 1.2 mm apart. Section of 4 mu m were cut from the TMA block and adhered to 30 silanized slides and 30 commercial glass slides using adhesive tape, according to manufacturer`s recommendations. Vimentin immunoexpression was evaluated by immunohistochemistry. Antigenic recovery was realized in citrate buffer using a microwave oven. Cylinder loss in the immunohistochemical process was quantified and expressed as: total (>80%), almost complete (75-79%), or partial (50-74%). The commercial adhesive tape group presented lesser total loss (1.1 versus 6.4%), almost complete loss (2.2 versus 3.5%), and partial loss (2.1 versus 3.8%) than the silanized slide group (ANOVA, P < 0.05). The sum of total and almost complete losses in the silanized slide group was 9.9%, greater than the losses in slides using commercial adhesive tapes (3.3%) and less than reported and considered acceptable in the literature (10-30%). In conclusion, the use of silanized only slides presents very satisfactory results, requires less training, and reduces costs significantly, thus justifying their use in research.

Nucleophosmin, p53, and Ki-67 expression patterns on an oral squamous cell carcinoma tissue microarray

Oral cancer is the eighth most prevalent cancer worldwide. It causes significant mortality and morbidity rates, which have motivated the search for prognostic factors to better tailor the individual management of oral squamous cell carcinoma patients. Nucleophosmin is a multifunctional protein that is involved in many cellular activities, such as, regulation of the tumor suppressor genes TP53 and p14(ARF). and is associated with proliferative and growth suppressive roles in the cell. Nucleophosmin is overexpressed in many solid tumors in human, including tumors of the colon, liver, stomach, ovary, and prostate. In this study, we analyzed the expression of nucleophosmin, Ki-67, and p53 by immunohistochemistry in oral squamous cell carcinomas. Less than 10% of nuclear staining was observed in 90.3%, 50.6%, and 65.3% of the cases for nucleophosmin, p53, and Ki-67, respectively. Expression of p53 was not significantly associated with any of the clinicopathologic parameters analyzed. Increased expression of Ki-67 was associated with the presence of lymph node metastasis (P < .0001), advanced stages of disease (P = .0030), tumors occurring in the floor of mouth (P = .0018), and moderately/well-differentiated tumors (P = .0287). Local recurrence was associated with higher expression of nucleophosmin (P = .0233), and disease-free survival rate was significantly better in patients with low expression of nucleophosmin. Multivariate analysis suggested that expression of nucleophosmin could be an independent prognostic factor for oral squamous cell carcinoma patients. (C) 2010 Elsevier Inc. All rights reserved.

Oral squamous cell carcinoma: status of tight junction claudins in the different histopathological patterns and relationship with clinical parameters. A tissue-microarray-based study of 136 cases

Aims Claudins are integral transmembrane proteins of the tight junctions, critical for maintaining cell adhesion and polarity. Alterations in the expression of individual claudins have been detected in carcinomas and appear to correlate with tumour progression. Methods In this study, a panel of anti-claudin antibodies (anti-claudins 1, 2, 3, 4, 5 and 7) was employed to map claudin expression in 136 cases of oral squamous cell carcinoma (OSCC) organised in a tissue microarray. Results Claudins were expressed in a reticular pattern up to the prickle layer in normal mucosal epithelium. In OSCC, claudins were strongly present in well-differentiated tumours, they presented mild and low expression in moderately differentiated OSCC, and were negative in poorly differentiated OSCC; the absences of claudin 1 (p = 0.002) and claudin 4 (p<0.001) were associated with moderately/poorly differentiated tumours. Strong expression of claudin 4 was associated with decreased perineural infiltration (p = 0.024). Claudins 5 and 7 were mostly negative or weakly expressed in all cases studied. Expression of claudin 7 was associated with the early clinical stages of the disease, whereas loss of claudin 7 tended to be more frequent in advanced stages of OSCC (p = 0.054). Absence of claudin 7 was also associated with absent vascular infiltration (p = 0.045) and with presence of recurrence (p = 0.052). Conclusions Claudin expression patterns showed a strong correlation with histological type of OSCC; claudin expression was decreased in areas of invasion, and negative in poorly differentiated tumours. This pattern may be related to evolution and prognosis of these tumours, especially in the case of claudin 7, which seems to be associated with a poor prognosis in OSCC.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Development of polyaniline microarray electrodes for cadmium analysis

Disposable screen-printed electrodes (SPCE) were modified using a cosmetic product to partially block the electrode surface in order to obtain a microelectrode array. The microarrays formed were electropolymerized with aniline. Scanning electron microscopy was used to evaluate the modified and polymerized electrode surface. Electrochemical characteristics of the constructed sensor for cadmium analysis were evaluated by cyclic and square-wave voltammetry. Optimized stripping procedure in which the preconcentration of cadmium was achieved by depositing at –1.20 V (vs. Ag/AgCl) resulted in a well defined anodic peak at approximately –0.7 V at pH 4.6. The achieved limit of detection was 4 × 10−9 mol dm−3. Spray modified and polymerized microarray electrodes were successfully applied to quantify cadmium in fish sample digests.

Arrow plot and CA maps on microarray preprocessing methods

Microarray allow to monitoring simultaneously thousands of genes, where the abundance of the transcripts under a same experimental condition at the same time can be quantified. Among various available array technologies, double channel cDNA microarray experiments have arisen in numerous technical protocols associated to genomic studies, which is the focus of this work. Microarray experiments involve many steps and each one can affect the quality of raw data. Background correction and normalization are preprocessing techniques to clean and correct the raw data when undesirable fluctuations arise from technical factors. Several recent studies showed that there is no preprocessing strategy that outperforms others in all circumstances and thus it seems difficult to provide general recommendations. In this work, it is proposed to use exploratory techniques to visualize the effects of preprocessing methods on statistical analysis of cancer two-channel microarray data sets, where the cancer types (classes) are known. For selecting differential expressed genes the arrow plot was used and the graph of profiles resultant from the correspondence analysis for visualizing the results. It was used 6 background methods and 6 normalization methods, performing 36 pre-processing methods and it was analyzed in a published cDNA microarray database (Liver) available at http://genome-www5.stanford.edu/ which microarrays were already classified by cancer type. All statistical analyses were performed using the R statistical software.

Development of computational tools for the integrated analysis of DNA microarray data with applications in cancer research

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto

Integrating data from heterogeneous DNA microarray platforms

DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.

IQRray, a new method for Affymetrix microarray quality control, and the homologous organ conservation score, a new benchmark method for quality control metrics.

MOTIVATION: Microarray results accumulated in public repositories are widely reused in meta-analytical studies and secondary databases. The quality of the data obtained with this technology varies from experiment to experiment, and an efficient method for quality assessment is necessary to ensure their reliability. RESULTS: The lack of a good benchmark has hampered evaluation of existing methods for quality control. In this study, we propose a new independent quality metric that is based on evolutionary conservation of expression profiles. We show, using 11 large organ-specific datasets, that IQRray, a new quality metrics developed by us, exhibits the highest correlation with this reference metric, among 14 metrics tested. IQRray outperforms other methods in identification of poor quality arrays in datasets composed of arrays from many independent experiments. In contrast, the performance of methods designed for detecting outliers in a single experiment like Normalized Unscaled Standard Error and Relative Log Expression was low because of the inability of these methods to detect datasets containing only low-quality arrays and because the scores cannot be directly compared between experiments. AVAILABILITY AND IMPLEMENTATION: The R implementation of IQRray is available at: ftp://lausanne.isb-sib.ch/pub/databases/Bgee/general/IQRray.R. CONTACT: Marta.Rosikiewicz@unil.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein-Binding Microarray Analysis of Tumor Suppressor AP2α Target Gene Specificity.

Cheap and massively parallel methods to assess the DNA-binding specificity of transcription factors are actively sought, given their prominent regulatory role in cellular processes and diseases. Here we evaluated the use of protein-binding microarrays (PBM) to probe the association of the tumor suppressor AP2α with 6000 human genomic DNA regulatory sequences. We show that the PBM provides accurate relative binding affinities when compared to quantitative surface plasmon resonance assays. A PBM-based study of human healthy and breast tumor tissue extracts allowed the identification of previously unknown AP2α target genes and it revealed genes whose direct or indirect interactions with AP2α are affected in the diseased tissues. AP2α binding and regulation was confirmed experimentally in human carcinoma cells for novel target genes involved in tumor progression and resistance to chemotherapeutics, providing a molecular interpretation of AP2α role in cancer chemoresistance. Overall, we conclude that this approach provides quantitative and accurate assays of the specificity and activity of tumor suppressor and oncogenic proteins in clinical samples, interfacing genomic and proteomic assays.

Selecting control genes for RT-QPCR using public microarray data.

Background: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e. g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. Results: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/similar to vpopovic/research/ Conclusion: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.

Impact of normalization on miRNA microarray expression profiling.

Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. We have developed a method to select nonchanging miRNAs (invariants) and use them to compute linear regression normalization coefficients or variance stabilizing normalization (VSN) parameters. We compared the invariants normalization to normalization by scaling, quantile, and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only a few miRNAs are affected (by p53 overexpression in squamous carcinoma cells versus control). All normalization methods performed better than no normalization. Normalization procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other data sets including those from one color miRNA microarray platforms, focused gene expression arrays, and gene expression analysis using quantitative PCR.

Effects of mitotane on gene expression in the adrenocortical cell line NCI-H295R: a microarray study.

Aim: The adrenolytic agent mitotane is widely used in the treatment of adrenocortical cancer; however, its mechanism of action is poorly elucidated. We have studied mitotane-induced mRNA expression changes in the NCI-H295R adrenocortical cancer cell line. Materials & methods: Cell viability and hormone assays were used to select the optimal mitotane concentration effectively inhibiting hormone secretion without affecting cell viability. RNA isolated from cultures treated for 48 and 72 h was subjected to Agilent 4×44K microarray platforms. Microarray results were validated by quantitative reverse-transcription PCR. Results: Altogether, 117 significantly differentially expressed genes were detected at 48 h and 72 h (p < 0.05) in mitotane-treated samples relative to controls. Three significantly underexpressed genes involved in steroid hormone biosynthesis (HSD3B1, HSD3B2 and CYP21A2) and four significantly overexpressed genes (GDF15, ALDH1L2, TRIB3 and SERPINE2) have been validated. Conclusion: Gene-expression changes might be involved in the adrenal action of mitotane and in the inhibition of hormone secretion. Original submitted 20 January 2012; Revision submitted 17 May 2012.