Gene Expression Profiling of Desmoid Tumors by cDNA Microarrays and Correlation with Progression-Free Survival.

PURPOSE: Because desmoid tumors exhibit an unpredictable clinical course, translational research is crucial to identify the predictive factors of progression in addition to the clinical parameters. The main issue is to detect patients who are at a higher risk of progression. The aim of this work was to identify molecular markers that can predict progression-free survival (PFS). EXPERIMENTAL DESIGN: Gene-expression screening was conducted on 115 available independent untreated primary desmoid tumors using cDNA microarray. We established a prognostic gene-expression signature composed of 36 genes. To test robustness, we randomly generated 1,000 36-gene signatures and compared their outcome association to our define 36-genes molecular signature and we calculated positive predictive value (PPV) and negative predictive value (NPV). RESULTS: Multivariate analysis showed that our molecular signature had a significant impact on PFS while no clinical factor had any prognostic value. Among the 1,000 random signatures generated, 56.7% were significant and none was more significant than our 36-gene molecular signature. PPV and NPV were high (75.58% and 81.82%, respectively). Finally, the top two genes downregulated in no-recurrence were FECH and STOML2 and the top gene upregulated in no-recurrence was TRIP6. CONCLUSIONS: By analyzing expression profiles, we have identified a gene-expression signature that is able to predict PFS. This tool may be useful for prospective clinical studies. Clin Cancer Res; 21(18); 4194-200. ©2015 AACR.

Evaluation of reference-based two-color methods for measurement of gene expression ratios using spotted cDNA microarrays

Abstract Background Spotted cDNA microarrays generally employ co-hybridization of fluorescently-labeled RNA targets to produce gene expression ratios for subsequent analysis. Direct comparison of two RNA samples in the same microarray provides the highest level of accuracy; however, due to the number of combinatorial pair-wise comparisons, the direct method is impractical for studies including large number of individual samples (e.g., tumor classification studies). For such studies, indirect comparisons using a common reference standard have been the preferred method. Here we evaluated the precision and accuracy of reconstructed ratios from three indirect methods relative to ratios obtained from direct hybridizations, herein considered as the gold-standard. Results We performed hybridizations using a fixed amount of Cy3-labeled reference oligonucleotide (RefOligo) against distinct Cy5-labeled targets from prostate, breast and kidney tumor samples. Reconstructed ratios between all tissue pairs were derived from ratios between each tissue sample and RefOligo. Reconstructed ratios were compared to (i) ratios obtained in parallel from direct pair-wise hybridizations of tissue samples, and to (ii) reconstructed ratios derived from hybridization of each tissue against a reference RNA pool (RefPool). To evaluate the effect of the external references, reconstructed ratios were also calculated directly from intensity values of single-channel (One-Color) measurements derived from tissue sample data collected in the RefOligo experiments. We show that the average coefficient of variation of ratios between intra- and inter-slide replicates derived from RefOligo, RefPool and One-Color were similar and 2 to 4-fold higher than ratios obtained in direct hybridizations. Correlation coefficients calculated for all three tissue comparisons were also similar. In addition, the performance of all indirect methods in terms of their robustness to identify genes deemed as differentially expressed based on direct hybridizations, as well as false-positive and false-negative rates, were found to be comparable. Conclusion RefOligo produces ratios as precise and accurate as ratios reconstructed from a RNA pool, thus representing a reliable alternative in reference-based hybridization experiments. In addition, One-Color measurements alone can reconstruct expression ratios without loss in precision or accuracy. We conclude that both methods are adequate options in large-scale projects where the amount of a common reference RNA pool is usually restrictive.

cDNA microarrays detect activation of a myogenic transcription program by the PAX3-FKHR fusion oncogene

Alveolar rhabdomyosarcoma is an aggressive pediatric cancer of striated muscle characterized in 60% of cases by a t(2;13)(q35;q14). This results in the fusion of PAX3, a developmental transcription factor required for limb myogenesis, with FKHR, a member of the forkhead family of transcription factors. The resultant PAX3-FKHR gene possesses transforming properties; however, the effects of this chimeric oncogene on gene expression are largely unknown. To investigate the actions of these transcription factors, both Pax3 and PAX3-FKHR were introduced into NIH 3T3 cells, and the resultant gene expression changes were analyzed with a murine cDNA microarray containing 2,225 elements. We found that PAX3-FKHR but not PAX3 activated a myogenic transcription program including the induction of transcription factors MyoD, Myogenin, Six1, and Slug as well as a battery of genes involved in several aspects of muscle function. Notable among this group were the growth factor gene Igf2 and its binding protein Igfbp5. Relevance of this model was suggested by verification that three of these genes (IGFBP5, HSIX1, and Slug) were also expressed in alveolar rhabdomyosarcoma cell lines. This study utilizes cDNA microarrays to elucidate the pattern of gene expression induced by an oncogenic transcription factor and demonstrates the profound myogenic properties of PAX3-FKHR in NIH 3T3 cells.

An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression

The cDNA microarray is one technological approach that has the potential to accurately measure changes in global mRNA expression levels. We report an assessment of an optimized cDNA microarray platform to generate accurate, precise and reliable data consistent with the objective of using microarrays as an acquisition platform to populate gene expression databases. The study design consisted of two independent evaluations with 70 arrays from two different manufactured lots and used three human tissue sources as samples: placenta, brain and heart. Overall signal response was linear over three orders of magnitude and the sensitivity for any element was estimated to be 2 pg mRNA. The calculated coefficient of variation for differential expression for all non-differentiated elements was 12–14% across the entire signal range and did not vary with array batch or tissue source. The minimum detectable fold change for differential expression was 1.4. Accuracy, in terms of bias (observed minus expected differential expression ratio), was less than 1 part in 10 000 for all non-differentiated elements. The results presented in this report demonstrate the reproducible performance of the cDNA microarray technology platform and the methods provide a useful framework for evaluating other technologies that monitor changes in global mRNA expression.

The application of cDNA and tissue microarray methods in the study of human carcinomas

Currently, numerous high-throughput technologies are available for the study of human carcinomas. In literature, many variations of these techniques have been described. The common denominator for these methodologies is the high amount of data obtained in a single experiment, in a short time period, and at a fairly low cost. However, these methods have also been described with several problems and limitations. The purpose of this study was to test the applicability of two selected high-throughput methods, cDNA and tissue microarrays (TMA), in cancer research. Two common human malignancies, breast and colorectal cancer, were used as examples. This thesis aims to present some practical considerations that need to be addressed when applying these techniques. cDNA microarrays were applied to screen aberrant gene expression in breast and colon cancers. Immunohistochemistry was used to validate the results and to evaluate the association of selected novel tumour markers with the outcome of the patients. The type of histological material used in immunohistochemistry was evaluated especially considering the applicability of whole tissue sections and different types of TMAs. Special attention was put on the methodological details in the cDNA microarray and TMA experiments. In conclusion, many potential tumour markers were identified in the cDNA microarray analyses. Immunohistochemistry could be applied to validate the observed gene expression changes of selected markers and to associate their expression change with patient outcome. In the current experiments, both TMAs and whole tissue sections could be used for this purpose. This study showed for the first time that securin and p120 catenin protein expression predict breast cancer outcome and the immunopositivity of carbonic anhydrase IX associates with the outcome of rectal cancer. The predictive value of these proteins was statistically evident also in multivariate analyses with up to a 13.1- fold risk for cancer specific death in a specific subgroup of patients.

Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations

We present statistical methods for analyzing replicated cDNA microarray expression data and report the results of a controlled experiment. The study was conducted to investigate inherent variability in gene expression data and the extent to which replication in an experiment produces more consistent and reliable findings. We introduce a statistical model to describe the probability that mRNA is contained in the target sample tissue, converted to probe, and ultimately detected on the slide. We also introduce a method to analyze the combined data from all replicates. Of the 288 genes considered in this controlled experiment, 32 would be expected to produce strong hybridization signals because of the known presence of repetitive sequences within them. Results based on individual replicates, however, show that there are 55, 36, and 58 highly expressed genes in replicates 1, 2, and 3, respectively. On the other hand, an analysis by using the combined data from all 3 replicates reveals that only 2 of the 288 genes are incorrectly classified as expressed. Our experiment shows that any single microarray output is subject to substantial variability. By pooling data from replicates, we can provide a more reliable analysis of gene expression data. Therefore, we conclude that designing experiments with replications will greatly reduce misclassification rates. We recommend that at least three replicates be used in designing experiments by using cDNA microarrays, particularly when gene expression data from single specimens are being analyzed.

Delineating developmental and metabolic pathways in vivo by expression profiling using the RIKEN set of 18,816 full-length enriched mouse cDNA arrays

We have systematically characterized gene expression patterns in 49 adult and embryonic mouse tissues by using cDNA microarrays with 18,816 mouse cDNAs. Cluster analysis defined sets of genes that were expressed ubiquitously or in similar groups of tissues such as digestive organs and muscle. Clustering of expression profiles was observed in embryonic brain, postnatal cerebellum, and adult olfactory bulb, reflecting similarities in neurogenesis and remodeling. Finally, clustering genes coding for known enzymes into 78 metabolic pathways revealed a surprising coordination of expression within each pathway among different tissues. On the other hand, a more detailed examination of glycolysis revealed tissue-specific differences in profiles of key regulatory enzymes. Thus, by surveying global gene expression by using microarrays with a large number of elements, we provide insights into the commonality and diversity of pathways responsible for the development and maintenance of the mammalian body plan.

Screening insertion libraries for mutations in many genes simultaneously using DNA microarrays

We describe a method to screen pools of DNA from multiple transposon lines for insertions in many genes simultaneously. We use thermal asymmetric interlaced–PCR, a hemispecific PCR amplification protocol that combines nested, insertion-specific primers with degenerate primers, to amplify DNA flanking the transposons. In reconstruction experiments with previously characterized Arabidopsis lines carrying insertions of the maize Dissociation (Ds) transposon, we show that fluorescently labeled, transposon-flanking fragments overlapping ORFs hybridize to cognate expressed sequence tags (ESTs) on a DNA microarray. We further show that insertions can be detected in DNA pools from as many as 100 plants representing different transposon lines and that all of the tested, transposon-disrupted genes whose flanking fragments can be amplified individually also can be detected when amplified from the pool. The ability of a transposon-flanking fragment to hybridize declines rapidly with decreasing homology to the spotted DNA fragment, so that only ESTs with >90% homology to the transposon-disrupted gene exhibit significant cross-hybridization. Because thermal asymmetric interlaced–PCR fragments tend to be short, use of the present method favors recovery of insertions in and near genes. We apply the technique to screening pools of new Ds lines using cDNA microarrays containing ESTs for ≈1,000 stress-induced and -repressed Arabidopsis genes.

Tissue-specific gene expression in soybean (Glycine max) detected by cDNA microarray analysis

We have constructed cDNA microarrays for soybean (Glycine max L. Merrill), containing approximately 4,100 Unigene ESTs derived from axenic roots, to evaluate their application and utility for functional genomics of organ differentiation in legumes. We assessed microarray technology by conducting studies to evaluate the accuracy of microarray data and have found them to be both reliable and reproducible in repeat hybridisations. Several ESTs showed high levels (>50 fold) of differential expression in either root or shoot tissue of soybean. A small number of physiologically interesting, and differentially expressed sequences found by microarray analysis were verified by both quantitative real-time RT-PCR and Northern blot analysis. There was a linear correlation (r(2) = 0.99, over 5 orders of magnitude) between microarray and quantitative real-time RT-PCR data. Microarray analysis of soybean has enormous potential not only for the discovery of new genes involved in tissue differentiation and function, but also to study the expression of previously characterised genes, gene networks and gene interactions in wild-type, mutant or transgenic; plants.

Systematic expression profiling of the mouse transcriptome using RIKEN cDNA mircoarrays

The number of known mRNA transcripts in the mouse has been greatly expanded by the RIKEN Mouse Gene Encyclopedia project. Validation of their reproducible expression in a tissue is an important contribution to the study of functional genomics. In this report, we determine the expression profile of 57,931 clones on 20 mouse tissues using cDNA microarrays. Of these 57,931 clones, 22,928 clones correspond to the FANTOM2 clone set. The set represents 20,234 transcriptional units (TUs) out of 33,409 TUs in the FANTOM2 set. We identified 7206 separate clones that satisfied stringent criteria for tissue-specific expression. Gene Ontology terms were assigned for these 7206 clones, and the proportion of 'molecular function' ontology for each tissue-specific clone was examined. These data will provide insights into the function of each tissue. Tissue-specific gene expression profiles obtained using our cDNA microarrays were also compared with the data extracted from the GNF Expression Atlas based on Affymetrix microarrays. One major outcome of the RIKEN transcriptome analysis is the identification of numerous nonprotein-coding mRNAs. The expression profile was also used to obtain evidence of expression for putative noncoding RNAs. In addition, 1926 clones (70%) of 2768 clones that were categorized as unknown EST, and 1969 (58%) clones of 3388 clones that were categorized as unclassifiable were also shown to be reproducibly expressed.

The potential of microarrays to assist shrimp breeding and production: a review

The shrimp aquaculture industry is a relatively new livestock industry, having developed over the past 30 years. Thus, it is poised to take advantage of new technologies from the outset of selective breeding programs. This contrasts with long established livestock industries, where there are already highly specialised breeds. This review focuses specifically on the potential application of microarrays to shrimp breeding. Potential applications of microarrays in selective breeding programs are summarised. Microarrays can be used as a rapid means to generate molecular markers for genetic linkage mapping, and genetic maps have been constructed for yeast, Arabidopsis and barley using microarray technology. Microarrays can also be used in the hunt for candidate genes affecting particular traits, leading to development of perfect markers for these traits (i.e. causative mutations). However, this requires that microarray analysis be combined with genetic linkage mapping, and that substantial genomic information is available for the species in question. A novel application of microarrays is to treat gene expression as a quantitative trait in itself and to combine this with linkage mapping to identify quantitative trait loci controlling the levels of gene expression; this approach may identify higher level regulatory genes in specific pathways. Finally, patterns of gene expression observed using microarrays may themselves be treated as phenotypic traits in selection programs (e.g. a particular pattern of gene expression might be indicative of a disease tolerant individual). Microarrays are now being developed for a number of shrimp species in laboratories around the world, primarily with a focus on identifying genes involved in the immune response. However, at present, there is no central repository of shrimp genomic information, which limits the rate at which shrimp genomic research can be progressed. The application of microarrays to shrimp breeding will be extremely limited until there is a shared repository of genomic information for shrimp, and the collective will and resources to develop comprehensive genomic tools for shrimp.

Sugarcane genes associated with sucrose content

Background -: Sucrose content is a highly desirable trait in sugarcane as the worldwide demand for cost-effective biofuels surges. Sugarcane cultivars differ in their capacity to accumulate sucrose and breeding programs routinely perform crosses to identify genotypes able to produce more sucrose. Sucrose content in the mature internodes reach around 20% of the culms dry weight. Genotypes in the populations reflect their genetic program and may display contrasting growth, development, and physiology, all of which affect carbohydrate metabolism. Few studies have profiled gene expression related to sugarcane's sugar content. The identification of signal transduction components and transcription factors that might regulate sugar accumulation is highly desirable if we are to improve this characteristic of sugarcane plants. Results -: We have evaluated thirty genotypes that have different Brix (sugar) levels and identified genes differentially expressed in internodes using cDNA microarrays. These genes were compared to existing gene expression data for sugarcane plants subjected to diverse stress and hormone treatments. The comparisons revealed a strong overlap between the drought and sucrose-content datasets and a limited overlap with ABA signaling. Genes associated with sucrose content were extensively validated by qRT-PCR, which highlighted several protein kinases and transcription factors that are likely to be regulators of sucrose accumulation. The data also indicate that aquaporins, as well as lignin biosynthesis and cell wall metabolism genes, are strongly related to sucrose accumulation. Moreover, sucrose-associated genes were shown to be directly responsive to short term sucrose stimuli, confirming their role in sugar-related pathways. Conclusion -: Gene expression analysis of sugarcane populations contrasting for sucrose content indicated a possible overlap with drought and cell wall metabolism processes and suggested signaling and transcriptional regulators to be used as molecular markers in breeding programs. Transgenic research is necessary to further clarify the role of the genes and define targets useful for sugarcane improvement programs based on transgenic plants.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

KeaA, a Dictyostelium kelch-domain protein that regulates the response to stress and development

Background: The protein kinase YakA is responsible for the growth arrest and induction of developmental processes that occur upon starvation of Dictyostelium cells. yakA-cells are aggregation deficient, have a faster cell cycle and are hypersensitive to oxidative and nitrosoative stress. With the aim of isolating members of the YakA pathway, suppressors of the death induced by nitrosoative stress in the yakA-cells were identified. One of the suppressor mutations occurred in keaA, a gene identical to DG1106 and similar to Keap1 from mice and the Kelch protein from Drosophila, among others that contain Kelch domains. Results: A mutation in keaA suppresses the hypersensitivity to oxidative and nitrosoative stresses but not the faster growth phenotype of yakA-cells. The growth profile of keaA deficient cells indicates that this gene is necessary for growth. keaA deficient cells are more resistant to nitrosoative and oxidative stress and keaA is necessary for the production and detection of cAMP. A morphological analysis of keaA deficient cells during multicellular development indicated that, although the mutant is not absolutely deficient in aggregation, cells do not efficiently participate in the process. Gene expression analysis using cDNA microarrays of wild-type and keaA deficient cells indicated a role for KeaA in the regulation of the cell cycle and pre-starvation responses. Conclusions: KeaA is required for cAMP signaling following stress. Our studies indicate a role for kelch proteins in the signaling that regulates the cell cycle and development in response to changes in the environmental conditions.