Microarray Technology and Cancer Gene Profiling: 593 (Advances in Experimental Medicine and Biology)

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Instead, the interpretation of a common clustering may be crucial. However, sometimes it seems that authors feel the urge to present their microarray results in the form of a cluster diagram, although such clusters are not always meaningful. Because the conventional diagnosis of cancer is based on the morphological appearance of stained tissues whose analysis requires highly trained pathologists, the introduction of microarrays offered the hope that classification and prediction of cancer by means of their gene expression profiles could be more objective and accurate.

Nevertheless, the problem of classification by microarrays is not simple; the genes that make it possible to predict the classes have to be identified from a large number of genes in a relatively small number of samples. Moreover, it is important to identify which genes contribute most to the classification. For this purpose, several new computational methods have been developed. In addition to algorithms suggested by Golub et al. At the moment, the reference method for classification problems in microarray studies are support vector machine SVM algorithms, and new promising supervised gene selection algorithms based on the SVM technique 25 [such as recursive feature elimination 26 and RFR 18 , 19 ] have been introduced.

An increasing number of studies have used microarrays in the field of thyroidology. Both platforms, spotted cDNA and oligonucleotide microarrays with a two-color or radioactive detection, and in situ photolithographically synthesized oligonucleotide arrays with a one-color detection, have been used to analyze the gene expression profiles of malignant thyroid tumors [ i.

Contributions of microarray studies to unravel the molecular etiology or patho physiology of thyroid tumors. The first array investigation of PTC was performed by Huang et al. In their study of eight PTC tumors, which were compared with normal surrounding tissue from the same eight individuals, the authors specified 50 genes with the most distinct gene expression changes. They confirmed a decreased expression of several thyroid-specific genes [ e. Moreover, they identified a number of additional PTC-specific genes, many of them associated with cell cycle or mitogenic control [ e.

In subsequent studies, many of the genes specified in this study e. Also, the findings of Jarzab et al. Thus, the involvement of adhesion-related genes appears a characteristic feature of PTC, which is not so distinct in many other cancers. For the adhesion-controlling PTC genes, it is well known that they participate in both invasion and metastasis and are involved in inflammation. Borrello et al. This conclusion remains in some conflict with clinical data showing better outcome in PTC cases with concomitant lymphocytic infiltration In the PTC gene expression profile, a very distinct expression pattern of immune response genes was observed, which follows in terms of intensity the pattern that is characteristic for the difference between tumor and normal tissue The molecular background of PTC was comprehensively investigated in a recent study of Melillo et al.

Using a broad methodology, they showed that the oncogenic proteins i. This finding is supported by the gene expression study of Frattini et al. However, in a recent reanalysis of the data sets of Huang et al. Although gene expression profiles of PTC with different genotypes i. Melillo et al. Therefore, the three oncoproteins, which are part of a single signaling pathway, are each able to trigger specific signals in addition to the common ones. This conclusion is supported and widened by the gene expression profiling of Giordano et al.

Distinct genes are from Ref. If this holds true, these observations would also be in line with the observation of a less favorable prognosis of BRAF mutant PTC 64 , 69 , Differences in the gene expression profile of PTC variants were indicated by Giordano et al. The difference between classic and tall cell variant was also described by Wreesmann et al. On the other hand, the differences between the classic and follicular PTC variant are less visible 40 , although some authors published lists of genes differentiating both variants This issue will be addressed below.

Gene expression profiles of FTC were analyzed in several microarray studies, with the aim to improve the molecular differentiation between FTC and follicular adenomas and to elucidate the molecular etiology of FTC further 27 — In an early investigation, Barden et al. An alternative approach of microarray analysis that aims at identifying the minimal number of discriminating genes appears more promising for diagnostic purposes and could also lead to further elucidation of the molecular etiology. Weber et al. Cerutti et al. Although these genes were primarily selected for diagnostic purposes with the aim to differentiate between follicular adenomas and carcinomas , they should also be considered for their significance regarding the biology of FTC.

By a combination of microarray data and previously published loss of heterozygosity data of FTC and normal thyroid samples, Aldred et al. Although both caveolins are down-regulated in FTC, their molecular mechanisms of down-regulation remain unclear. Loss of heterozygosity was found in a subgroup of tumors, but the authors could not identify mutations in either gene, and furthermore, the methylation status of the caveolin-1 promoter did not correlate with the expression pattern Therefore, these genes most likely do not make it possible to distinguish between benign and malignant thyroid tumors.

In their study, Lui et al. Moreover, this conclusion is in line with other findings. French et al. Nikiforova et al.

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A gene expression profiling study of Lacroix et al. Recently, Giordano et al. Interestingly, in their study Giordano et al. Kroll et al. In a recent study, we confirmed these findings. It has been suggested that a down-regulation of lymphocyte and macrophage-specific genes 52 , 53 might reflect a different cellular composition of the AFTNs with a lack of lymphocytes and discussed by Wattel et al. Interestingly, two independent studies using different microarray platforms i. The stringent and prominent SIAT1 expression pattern that we found in our microarray study of AFTNs 52 prompted us to investigate one of its possible functional relevances further.

Subsequent studies characterized a new aspect of posttranslational modification of the TSHR. In cell culture experiments, we demonstrated for the first time that the transfer of sialic acid directly affects TSHR signaling because it improves and prolongs the cell-surface expression of the TSHR Furthermore, microarray investigations of AFTNs and TSH-stimulated primary thyroid cells illustrate a remarkable induction or activation of negative feedback mechanisms such as an up-regulation of phosphodiesterases 11 , 51 , 53 , which is in line with previous findings of Persani et al.

Interestingly, whereas microarray studies of TSH-stimulated primary thyroid cell cultures reveal an increased expression of the regulator of G protein signaling RGS 2 11 , 51 , which has been shown to reduce the TSHR signaling via inositolphosphate 87 , RGS2 is characterized by a decreased expression in AFTNs 11 , 51 , Such a difference can most likely be explained by defects in the RGS regulation pathway or by additional counter-regulatory mechanisms that occur only in the chronically proliferating AFTNs. These findings demonstrate that the additional investigation of a compatible cell model e.

However, it is obvious that the gene expression profile of the cell model itself can only depict a part of the complex situation present in the tissue samples. Several microarray studies, which were performed with the aim to identify novel diagnostic and clinical markers for differentiated thyroid tumors, used benign thyroid nodules, histologically classified as follicular adenoma or hyperplastic nodules, for comparison with PTC and FTC 29 , 30 , 32 , 33 , 35 , 37 , 40 , 42 , 49 , However, with respect to their function, histologically benign thyroid nodules can be further distinguished as AFTNs or CTNs or less often as so-called warm nodules, which do not show detectable differences to the surrounding thyroid tissue by scintigraphy.

C , the current knowledge concerning their molecular etiology is very limited. Nevertheless, there is only one study that investigated the array-based gene expression profiles of CTNs classified by scintigraphy In an early study we could show a down-regulation of several signal transducing components both in AFTNs and CTNs, which seemed to reflect a disturbed signaling system 8. These similarities in the gene expression patterns of AFTNs and CTNs might be attributable to a common property of both benign tumor entities, e. To gain a higher resolution that might help to identify specific signaling cascades involved in nodular development, we subsequently compared gene expression profiles of 22 CTNs to their normal surrounding tissue using the U95A Affymetrix GeneChip On the basis of the high number of investigated genes approximately 10, full-length genes and an improved statistical analysis that made it possible to analyze the significance of differential gene expression within gene sets e.

Furthermore, these expression data also revealed that contrary to PTCs, altered expression of components belonging to the RAS-MAPK cascade is of minor importance for the development of CTNs because gene sets representing this pathway did not show differential expression in comparison to the surrounding normal tissue.

This is in line with findings of Esapa et al. Moreover, these results are supported by findings of Krohn et al. This expression pattern is especially interesting because it has been shown that thyroid cells undergoing a long-term PKC stimulation are characterized by a general loss of thyroid-specific functions e. The up-regulation of cell cycle genes in the gene expression profile of CTNs 54 is so distinct that it may be used for the differential molecular diagnosis of these tumors, as shown by RFR algorithm Mainly genes related to proliferation and growth processes were included into a gene molecular classifier e.

Furthermore, the CTN classifier contains genes considered as cancer specific, such as the fibroblast growth factor receptor 1 FGFR1 found down-regulated by Chevillard et al. In contrast to AFTNs, the CTN multigene signature was also found in some PTC, which could be explained by the partly dedifferentiated and proliferating phenotype that is common to both entities.

Several studies provide evidence that differentiated functions of thyrocytes and of iodide metabolism can be reinduced by retinoic acid — CRABP1 encodes a high-affinity cellular retinoic acid binding protein that regulates the availability of retinoic acid for its nuclear receptors and is also involved in retinoic acid catabolism Our recent findings showing an increased expression of CRABP1 in AFTNs in comparison to their normal surrounding tissue support this assumption unpublished observation. Overall, in addition to the interesting finding of decreased CRABP1 expression, gene expression profiling of CTNs allowed identification of the molecular pattern of their increased proliferation that is characterized by a differential expression of several cell cycle-associated genes.

Despite the fact that microarray studies revealed very distinct changes in the expression of certain genes, none of the several genes identified by array studies as differentially regulated was proven to be an ideal single marker of PTC — For example, DPP4 dipeptidyl-peptidase 4 , which was indicated by Huang et al. Also, oncofibronectin, galectin 3, and other proposed markers did not work properly in a single gene context — Moreover, the different multigene classifiers proposed by various authors showed only a minor overlap of the respective markers included.

In fact, such a comparison requires a systematic bioinformatic approach. Such analysis has been performed in some types of cancer but is not available for thyroid cancer. The discriminating gene set contained both known cancer-associated genes e. However, such large gene lists are not applicable for diagnostic purposes. Thus, approaches to limit the number of genes in an identifier have been undertaken. The aim of the study of Jarzab et al. This gene set was selected not by univariate approaches but by complementation of the genes, by a RFR algorithm.

Interestingly, the classifier does not contain many known genes found in other approaches like FN1 or TIMP , whereas some other genes previously known for their up-regulation in PTC e. Within the classifier there were some new genes, previously not described in PTC [ e. The goal of this approach was not to obtain 20 genes showing a correlated change in expression between tumor and benign tissue but to generate complementary information from a selected set of genes.

The idea of using gene interactions to improve classification accuracy can be explained with the simplest example of a two-gene linear interaction Fig. However, two genes cannot classify all samples, and it is necessary to base the classification on a larger number of genes, usually five to The gene PTC classifier obtained by RFR [the name RFR refers to the number of genes included in the classifier 44 ] performs very well to differentiate between tumors red bar and normal tissues green bar.

The intensity of the color refers to the intensity of gene expression; red refers to the up-regulation, and green refers to the down-regulation. However, only some genes e. Other included genes e. Taken together, these two markers make it possible to classify both samples properly.


Taking into account the known heterogeneity of malignant tumors, RFR algorithm helped to obtain a robust molecular classifier able to recognize a wide range of PTC tumors. Fujarewicz, M. Jarzab, M. Eszlinger, K. Krohn, R. Paschke, M. Oczko-Wojciechowska, M. Wiench, A.

Kukulska, B. Jarzab, and A. Swierniak, manuscript in preparation. In this context, it is advisable to proceed with molecular classifiers composed of at least six genes. It is more than a simple coincidence that Mazzanti et al. Tumors and tissues often consist of more than one type of cells that might contribute differently to the measured expression of a given gene The resulting problem of data interpretation in such a complex situation can be solved in different ways.

Before the microarray experiment is performed, specific cell types can be isolated by microdissection. At the moment, there are some related bioinformatic research reports of GSE in glioma. Some studies have shown that different enrichment pathway analyses of DEGs can be classified according to their degrees of differential expression during tumor progression in order to explore the deterioration of low into high grade glioma [ 34 ]. Some research finds that the DEGs are regulated by transcription factors in glioblastoma [ 35 ] and microarray technology has been used to identify the DEGs and their functions in the development of three types of glioma astrocytoma, glioblastoma, and oligodendroglioma [ 36 ].

Different from these, our study selects the node of the highest score from each module as hub genes in MCODE after comparing nontumor samples with glioma samples. These hub nodes are the key genes of interaction, in the PPI network, that may play important roles in the occurrence and development of glioma. Moreover, hub gene identification is more persuasive, since we validate the association of hub genes and glioma by using survival analysis in two independent datasets to identify four genes that may be cancer biomarkers for glioma. Though not all hub genes associated with the survival of glioma patients, but some hub genes play important roles in immune or inflammation.

For example, WNT10B plays an important role in regulating asthmatic airway inflammation through modification of the T cell response [ 37 ]. In conclusion, we presumed these key genes identified by a series of bioinformatics analyses on DEGs between tumor samples and normal samples, probably related to the development of glioma. These hub genes could also affect the survival time of glioma patients as validated from two independent datasets.

These identified genes and pathways provide a more detailed molecular mechanism for underlying glioma initiation and development. However, further molecular and biological experiments are required to confirm the functions of the key genes in glioma. All authors declare that they have no conflict of interests to state.

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Microarray Technology and Cancer Gene Profiling

This may be because the snippet appears in a figure legend, contains special characters or spans different sections of the article. J Immunol Res. Published online Dec 6. PMID: Corresponding author. Haixiong Xu: moc. Received Jul 30; Accepted Oct This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article has been cited by other articles in PMC. Abstract Glioma is the most common malignant tumor in the central nervous system. Introduction Among the various histological subtypes of brain tumor, glioma is the most common malignant tumor in the central nervous system [ 1 ]. Materials and Methods 2. Results 3. Open in a separate window. Figure 1. Table 1 Gene ontology analysis of downregulated genes associated with glioma. Table 2 KEGG pathway analysis of downregulation genes associated with glioma.

Figure 2. Table 3 The enriched pathways for genes in the highest module. Identification of Biomarkers In order to identify biomarkers, we calculated the survival rate for two groups of 12 hub genes in the generation dataset GSE and compared the result with the validation dataset GSE through Kaplan-Meier analysis and log-rank test.

Microarray technology and cancer gene profiling

Figure 3. Figure 4. Table 4 Cox multivariate analyses of biomarkers associated with OS in the generation and validation datasets. Discussion In the present study, we identified DEGs between glioma and normal samples and used a series of bioinformatics analyses to screen key gene and pathways associated with cancer. Conflicts of Interest All authors declare that they have no conflict of interests to state.

References 1. Ostrom Q. Louis D. The World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica. Stupp R. The Lancet Oncology. Freije W. Gene expression profiling of gliomas strongly predicts survival. Cancer Research. De Preter K. Accurate outcome prediction in neuroblastoma across independent data sets using a multigene signature.

Clinical Cancer Research. Kim Y. Identification of novel synergistic targets for rational drug combinations with PI3 kinase inhibitors using siRNA synthetic lethality screening against GBM. Cheng W. Bioinformatic profiling identifies an immune-related risk signature for glioblastoma. Ward A. Re-expression of microRNA reverses both tamoxifen resistance and accompanying EMT-like properties in breast cancer.

Saura M. Gene Ontology Consortium. The gene ontology GO project in Nucleic Acids Research. Kanehisa M. Cell Biochem Biophys. Will the new cytogenetics replace the old cytogenetics? Clin Genet. Genome structural variation discovery and genotyping. Park H et al. Li W, Olivier M. Current analysis platforms and methods for detecting copy number variation. Physiol Genomics. FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate.

Nucleic Acids Res. Whole genome DNA copy number changes identified by high density oligonucleotide arrays. Hum Genomics. An integrated view of copy number and allelic alterations in the cancer genome using single nucleotide polymorphism arrays. A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays.

Estimation and assessment of raw copy numbers at the single locus level. CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics. BMC Bioinformatics. Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data.

Software comparison for evaluating genomic copy number variation for Affymetrix 6. Causes and consequences of aneuploidy in cancer. Carling D. The AMP-activated protein kinase cascade—a unifying system for energy control. Trends Biochem Sci. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. J Natl Compr Canc Netw. Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer.

N Engl J Med. Strongly enhanced antitumor activity of trastuzumab and pertuzumab combination treatment on HER2-positive human xenograft tumor models. Bird AP. CpG-rich islands and the function of DNA methylation. Amount and distribution of 5-methylcytosine in human DNA from different types of tissues of cells.

Histone onco-modifications. The epigenomics of cancer. Epigenetic regulation as a new target for breast cancer therapy. Cancer Invest. Human DNA methylomes at base resolution show widespread epigenomic differences. Quantitative multiplex methylation-specific PCR assay for the detection of promoter hypermethylation in multiple genes in breast cancer. Quantitative hypermethylation of a small panel of genes augments the diagnostic accuracy in fine-needle aspirate washings of breast lesions.

DNA hypermethylation of PITX2 is a marker of poor prognosis in untreated lymph node-negative hormone receptor-positive breast cancer patients. Association of breast cancer DNA methylation profiles with hormone receptor status and response to tamoxifen. Estrogen and progesterone receptor status affect genome-wide DNA methylation profile in breast cancer.

Breast cancer methylomes establish an epigenomic foundation for metastasis. Sci Transl Med. Free DNA in the serum of cancer patients and the effect of therapy. Clin Biochem. SOX17 promoter methylation in circulating tumor cells and matched cell-free DNA isolated from plasma of patients with breast cancer. Clin Chem. Differential promoter methylation of kinesin family member 1a in plasma is associated with breast cancer and DNA repair capacity. Oncol Rep. Int J Cancer.

Global histone modifications in breast cancer correlate with tumor phenotypes, prognostic factors, and patient outcome. Loss of histone H4K20 trimethylation predicts poor prognosis in breast cancer and is associated with invasive activity. Curr Pharm Anal. Szyf M. DNA methylation signatures for breast cancer classification and prognosis. Genome Med. Methylation-specific oligonucleotide microarray: a new potential for high-throughput methylation analysis.

Genome Res. The behaviour of 5-hydroxymethylcytosine in bisulfite sequencing. Methylation profiling of CpG islands in human breast cancer cells. J Nutr. Microarray-based DNA methylation profiling: technology and applications. Methods Mol Biol. Zhang M, Smith A. Challenges in understanding genome-wide DNA methylation. J Comput Sci Technol. FEBS Lett. Predicting DNA methylation status using word composition. J Biomedical Science and Engineering. Ali I, Seker H. Detailed methylation prediction of CpG islands on human chromosome In: Biology and Chemistry.

Histone methylation marks play important roles in predicting the methylation status of CpG islands. Biochem Biophys Res Commun. Profile analysis and prediction of tissue-specific CpG island methylation classes. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. DNA methylation profiling of human chromosomes 6, 20 and Du P, Bourgon R. R package version 1. Nat Methods. Additional annotation enhances potential for biologically-relevant analysis of the illumina infinium humanmethylation beadchip array.

Epigenetics Chromatin. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol. Differential genome-wide array-based methylation profiles in prognostic subsets of chronic lymphocytic leukemia. Wessely F, Emes RD. Identification of DNA methylation biomarkers from Infinium arrays.

Front Genet. Review of processing and analysis methods for DNA methylation array data. Br J Cancer. Phipson B, Oshlack A. DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging. Analyzing gene expression data in terms of gene sets: methodological issues.

Global analysis of methylation profiles from high resolution CpG data. Genet Epidemiol. New York: Wiley-Interscience; Empirical bayes model comparisons for differential methylation analysis. Comp Funct Genomics. An evaluation of statistical methods for DNA methylation microarray data analysis. DNA methyltransferases: a novel target for prevention and therapy. Front Oncol. Phase I and pharmacodynamic trial of the DNA methyltransferase inhibitor decitabine and carboplatin in solid tumors. Anticancer Res. DNA methyltransferase inhibitor, zebularine, delays tumor growth and induces apoptosis in a genetically engineered mouse model of breast cancer.

Mol Cancer Ther. Effects of a novel DNA methyltransferase inhibitor zebularine on human breast cancer cells. Decitabine, a new star in epigenetic therapy: the clinical application and biological mechanism in solid tumors. Cancer Lett. Marson CM. Histone deacetylase inhibitors: design, structure-activity relationships and therapeutic implications for cancer. Anticancer Agents Med Chem. A phase II study of the histone deacetylase inhibitor vorinostat combined with tamoxifen for the treatment of patients with hormone therapy-resistant breast cancer. Randomized phase II, double-blind, placebo-controlled study of exemestane with or without entinostat in postmenopausal women with locally recurrent or metastatic estrogen receptor-positive breast cancer progressing on treatment with a nonsteroidal aromatase inhibitor.

Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. MicroRNAs: a developing story. Curr Opin Genet Dev. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype.

MicroRNA gene expression deregulation in human breast cancer. Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies. Mol Cancer. MicroRNAs as regulators of epithelial—mesenchymal transition. Cell Cycle. Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer. MicroRNAb confers the resistance of breast cancer cells to paclitaxel through suppression of pro-apoptotic Bcl-2 antagonist killer 1 Bak1 expression.

J Biol Chem. TGF-beta upregulates miRa expression to promote breast cancer metastasis. The miRb cluster targets Smad7, activates TGF-beta signaling, and induces EMT and tumor initiating cell characteristics downstream of Six1 in human breast cancer. Epigenetically deregulated microRNA is involved in a positive feedback loop with estrogen receptor alpha in breast cancer cells. MicroRNA-7, a homeobox D10 target, inhibits pactivated kinase 1 and regulates its functions.

Regulation of epidermal growth factor receptor signaling in human cancer cells by microRNA J Cell Biol. Pandey DP, Picard D. MicroRNA- mediated regulation of Ubc9 expression in cancer cells. Mir reduction maintains self-renewal and inhibits apoptosis in breast tumor-initiating cells. A pleiotropically acting microRNA, miR, inhibits breast cancer metastasis. Concurrent suppression of integrin alpha5, radixin, and RhoA phenocopies the effects of miR on metastasis. Concomitant suppression of three target genes can explain the impact of a microRNA on metastasis.

MicroRNA regulates endothelial expression of vascular cell adhesion molecule 1. Breast cancer metastasis suppressor 1 up-regulates miR, which suppresses breast cancer metastasis. Downregulation of miRb contributes to enhance urokinase-type plasminogen activator uPA expression and tumor progression and invasion in human breast cancer. Suppression of cell growth and invasion by miR in breast cancer. Cell Res. MicroRNA targets notch3, activates apoptosis, and inhibits tumor cell migration and focus formation.

Breast cancer metastasis suppressor 1 coordinately regulates metastasis-associated microRNA expression. Cell Death Differ. Circulating microRNAs as stable blood-based markers for cancer detection. MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol Oncol. Exosomes from human saliva as a source of microRNA biomarkers. Oral Dis.

Salivary microRNA: discovery, characterization, and clinical utility for oral cancer detection. Altered miRNA expression in sputum for diagnosis of nonsmall cell lung cancer. Lung Cancer. Early detection of lung adenocarcinoma in sputum by a panel of microRNA markers. Detection of cancer with serum miRNAs on an oligonucleotide microarray. Cancer diagnosis and prognosis decoded by blood-based circulating microRNA signatures. Serum circulating microRNA profiling for identification of potential breast cancer biomarkers. Dis Markers.

Down-regulation of miRNAa in human plasma is a novel marker for breast cancer. Med Oncol. Circulating miRNAs as surrogate markers for circulating tumor cells and prognostic markers in metastatic breast cancer. The level of circulating miRNAb and miRNA in detecting lymph node metastasis of breast cancer: potential biomarkers.

DNA Microarrays - Current Technology and Clinical Applications

Tumour Biol. Circulating MiRb as a marker predicting chemoresistance in breast cancer. Serum microRNA as a potential biomarker to track disease in breast cancer. MicroRNA sequence and expression analysis in breast tumors by deep sequencing. Next-generation sequencing of microRNAs for breast cancer detection. J Biomed Biotechnol. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. Hunting for robust gene signature from cancer profiling data: sources of variability, different interpretations, and recent methodological developments.

Analysis of miRNA-gene expression-genomic profiles reveals complex mechanisms of microRNA deregulation in osteosarcoma. Cancer Genetics. Funct Integr Genomics. Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis. BMC Neurosci. Computational approaches for microRNA studies: a review. Mamm Genome. Computational and experimental identification of C.

The microRNAs of Caenorhabditis elegans. Computational identification of Drosophila microRNA genes. Lee RC, Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Phylogenetic shadowing and computational identification of human microRNA genes. Identification of hundreds of conserved and nonconserved human microRNAs. Fast folding and comparison of RNA secondary structures. Lindow M, Gorodkin J. Principles and limitations of computational microRNA gene and target finding.

DNA Cell Biol. Allmer J, Yousef M. Computational methods for ab initio detection of microRNAs. Learning from positive examples when the negative class is undetermined — microRNA gene identification. Algorithms Mol Biol. Hertel J, Stadler PF. Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data. MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans. Human microRNA prediction through a probabilistic co-learning model of sequence and structure.

Prediction of novel microRNA genes in cancer-associated genomic regions — a combined computational and experimental approach. Bentwich I. Prediction and validation of microRNAs and their targets. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Identification of clustered microRNAs using an ab initio prediction method. Controlled delivery of antisense oligonucleotides: a brief review of current strategies.

Expert Opin Drug Deliv. Dias N, Stein CA. Antisense oligonucleotides: basic concepts and mechanisms. Genomic and epigenomic cross-talks in the regulatory landscape of miRNAs in breast cancer. Mol Cancer Res. Current molecular diagnostics of breast cancer and the potential incorporation of microRNA. Expert Rev Mol Diagn. Gene expression profiling predicts clinical outcome of breast cancer. A gene-expression signature as a predictor of survival in breast cancer.

Validation and clinical utility of a gene prognostic signature for women with node-negative breast cancer. Use of gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study RASTER. Lancet Oncol. Validation of gene prognosis signature in node-negative breast cancer. Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.

Supervised risk predictor of breast cancer based on intrinsic subtypes. The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1—98 trial. BMC Med Genomics. Mammostrat as a tool to stratify breast cancer patients at risk of recurrence during endocrine therapy. Novel prognostic immunohistochemical biomarker panel for estrogen receptor-positive breast cancer. Northern blot analysis for detection and quantification of RNA in pancreatic cancer cells and tissues.

Nat Protoc. Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. The power of real-time PCR. Adv Physiol Educ. Trans Lung Cancer Res. Quantitative assessment of DNA microarrays—comparison with Northern blot analyses. Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Pollock JD. Gene expression profiling: methodological challenges, results, and prospects for addiction research. Chem Phys Lipids.

RNA-Seq: a revolutionary tool for transcriptomics. Serial analysis of gene expression. CAGE: cap analysis of gene expression. Gene expression analysis by massively parallel signature sequencing MPSS on microbead arrays. Nat Biotechnol. Computational methods for discovering structural variation with next-generation sequencing. Preprocessing and downstream analysis of microarray DNA copy number profiles. Brief Bioinform. A review of feature selection techniques in bioinformatics. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

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A review of microarray datasets and applied feature selection methods. Inf Sci. Kumar AP, Valsala P. Methods Inf Med. Jafari P, Azuaje F. An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. Battiti R. Using mutual information for selecting features in supervised neural net learning. An entropy-based gene selection method for cancer classification using microarray data. Significance analysis of microarrays applied to the ionizing radiation response. Hybrid genetic algorithms for feature selection. Improved binary PSO for feature selection using gene expression data.

Comput Biol Chem. A top-r feature selection algorithm for microarray gene expression data. Ga-kde-bayes: an evolutionary wrapper method based on non-parametric density estimation applied to bioinformatics problems. Selection bias in gene extraction on the basis of microarray gene-expression data. Regulatable gene expression systems for gene therapy applications: progress and future challenges. Mol Ther. Spatial and temporal control of gene therapy using ionizing radiation. Nat Med. Direct retroviral delivery of human cytochrome P 2B6 for gene-directed enzyme prodrug therapy of cancer.

Cancer Gene Ther. Novel jet-injection technology for nonviral intratumoral gene transfer in patients with melanoma and breast cancer. Segmental copy number variation shapes tissue transcriptomes. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Futcher B, Carbon J.

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Toxic effects of excess cloned centromeres. Veitia RA. Exploring the etiology of haploinsufficiency. Microarray analysis reveals a major direct role of dna copy number alteration in the transcriptional program of human breast tumors. Lessons from a decade of integrating cancer copy number alterations with gene expression profiles. The consequences of chromosomal aneuploidy on gene expression profiles in a cell line model for prostate carcinogenesis. High-resolution analysis of gene copy number alterations in human prostate cancer using CGH on cDNA microarrays: impact of copy number on gene expression.

Gene expression alterations over large chromosomal regions in cancers include multiple genes unrelated to malignant progression. National Cancer Institute. The Cancer Genome Atlas Homepage. Combined analysis of chromosomal instabilities and gene expression for colon cancer progression inference. J Clinical Bioinformatics. Combination of gene expression and genome copy number alteration has a prognostic value for breast cancer.

Integrative analysis reveals the direct and indirect interactions between DNA copy number aberrations and gene expression changes. Comprehensive copy number and gene expression profiling of the 17q23 amplicon in human breast cancer. Targets of genome copy number reduction in primary breast cancers identified by integrative genomics. Genes Chromosom Cancer. Copy number alterations that predict metastatic capability of human breast cancer.

Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array. Impact of DNA amplification on gene expression patterns in breast cancer. Genetic profiling of chromosome 1 in breast cancer: mapping of regions of gains and losses and identification of candidate genes on 1q.

Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell. High-resolution array-CGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer.

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Briefings in bioinformatics. Integrated analysis of DNA copy number and gene expression microarray analysis using gene sets. Linear and non-linear dependencies between copy number aberrations and mRNA expression reveal distinct molecular pathways in breast cancer. Exploratory analysis of multiple omics datasets using the adjusted RV coefficient. Stat Appl Genet Mol Biol.

Relationship of gene expression and chromosomal abnormalities in colorectal cancer. Segmentation of genomic and transcriptomic microarrays data reveals major correlation between DNA copy number aberrations and gene-loci expression. Integrated analysis of copy number alterations and gene expression: a bivariate assessment of equally directed abnormalities. Joint analysis of DNA copy numbers and gene expression levels. In: Jonassen I, Kim J, editors.

Germany: Springer; Integrative analysis of gene expression and copy number alterations using canonical correlation analysis. Highlighting relationships between heterogeneous biological data through graphical displays based on regularized canonical correlation analysis. J Biol Syst. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Magellan: a web based system for the integrated analysis of heterogeneous biological data and annotations; application to DNA copy number and expression data in ovarian cancer.

Cancer Inform. A computational procedure to identify significant overlap of differentially expressed and genomic imbalanced regions in cancer datasets. Louhimo R, Hautaniemi S. CNAmet: an R package for integrating copy number, methylation and expression data. An integrated approach to uncover drivers of cancer. Epigenetic regulation of estrogen signaling in breast cancer. Demethylation and reexpression of epigenetically silenced tumor suppressor genes: sensitization of cancer cells by combination therapy.