Microarrays for Cancer Diagnosis and Classification

Computational Approach to Identify the Key Genes for Invasive Lobular Carcinoma (ILC) Diagnosis and Therapies

Invasive Lobular Carcinoma (ILC) is a common form of breast cancer that begins in milk-producing glands lobules and spreads to other parts of the breast. According to the American Cancer Society, about 10–15% of breast cancer cases are ILC. ILC risk rises with age. The number of deaths caused by this cancer each year can be decreased through early diagnosis and if accurate therapy is given. However, diagnosis of ILC is difficult due to its development pattern as it grows as single file strands and not as lumps. Treatments of ILC involve chemotherapy, hormonal therapy and radiation therapy. Drugs that are being used for ILC, are commonly used to treat all types of breast cancer and there are no specific drugs that target receptors of ILC are available. Microarray technology’s emergence helps in finding the differentially expressed genes (DEGs) in malignant cells. From the DEGs, highly interacting genes were identified using the online tool, string. Seven key genes were identified based on the interaction and they are FN1, CDKN2A, COL1A1, COL3A1, COL11A1, LEF1 and IL1B. Thus, the drugs targeting these biomarkers were identified by doing molecular docking using the tool Autodock and molecular dynamic (MD) simulation using the tool iMODs. The response of the identified drugs to the ILC cell line was compared with the control drugs by in silico pharmacogenomic analysis and it was found that the identified drugs have a good response to the ILC cell line.

An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data

Cancer can be considered as one of the leading causes of death widely. One of the most effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using expression profiling technique which is based on microarray gene. For each data point (sample), gene data expression usually receives tens of thousands of genes. As a result, this data is large-scale, high-dimensional, and highly redundant. The classification of gene expression profiles is considered to be a (NP)-Hard problem. Feature (gene) selection is one of the most effective methods to handle this problem. A hybrid cancer classification approach is presented in this paper, and several machine learning techniques were used in the hybrid model: Pearson's correlation coefficient as a correlation-based feature selector and reducer, a Decision Tree classifier that is easy to interpret and does not require a parameter, and Grid Search CV (cross-validation) to optimize the maximum depth hyperparameter. Seven standard microarray cancer datasets are used to evaluate our model. To identify which features are the most informative and relative using the proposed model, various performance measurements are employed, including classification accuracy, specificity, sensitivity, F1-score, and AUC. The suggested strategy greatly decreases the number of genes required for classification, selects the most informative features, and increases classification accuracy, according to the results.

The Role of Surface Chemistry in the Efficacy of Protein and DNA Microarrays for Label-Free Detection: An Overview

The importance of microarrays in diagnostics and medicine has drastically increased in the last few years. Nevertheless, the efficiency of a microarray-based assay intrinsically depends on the density and functionality of the biorecognition elements immobilized onto each sensor spot. Recently, researchers have put effort into developing new functionalization strategies and technologies which provide efficient immobilization and stability of any sort of molecule. Here, we present an overview of the most widely used methods of surface functionalization of microarray substrates, as well as the most recent advances in the field, and compare their performance in terms of optimal immobilization of the bioreceptor molecules. We focus on label-free microarrays and, in particular, we aim to describe the impact of surface chemistry on two types of microarray-based sensors: microarrays for single particle imaging and for label-free measurements of binding kinetics. Both protein and DNA microarrays are taken into consideration, and the effect of different polymeric coatings on the molecules' functionalities is critically analyzed.

Prediction of Lymph-Node Metastasis in Cancers Using Differentially Expressed mRNA and Non-coding RNA Signatures

Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96-92.19%), 81.97% (70.83-95.24%), and 80.78% (69.61-90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.

Identification of a Metastasis-Associated Gene Signature of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most frequent pathological subtypes of kidney cancer, accounting for ~70-75%, and the major cause of mortality is metastatic disease. The difference in gene expression profiles between primary ccRCC tumors and metastatic tumors has not been determined. Thus, we report integrated genomic and transcriptomic analysis for identifying differentially expressed genes (DEGs) between primary and metastatic ccRCC tumors to understand the molecular mechanisms underlying the development of metastases. The microarray datasets GSE105261 and GSE85258 were obtained from the Gene Expression Omnibus (GEO) database, and the R package limma was used for DEG analyses. In summary, the results described herein provide important molecular evidence that metastatic ccRCC tumors are different from primary tumors. Enrichment analysis indicated that the DEGs were mainly enriched in ECM-receptor interaction, platelet activation, protein digestion, absorption, focal adhesion, and the PI3K-Akt signaling pathway. Moreover, we found that DEGs associated with a higher level of tumor immune infiltrates and tumor mutation burden were more susceptible to poor prognosis of ccRCC. Specifically, our study indicates that seven core genes, namely the collagen family (COL1A2, COL1A1, COL6A3, and COL5A1), DCN, FBLN1, and POSTN, were significantly upregulated in metastatic tumors compared with those in primary tumors and, thus, potentially offer insight into novel therapeutic and early diagnostic biomarkers of ccRCC.

Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy

Breast Cancer is the most predominant female cancer in developed as well as developing countries. The treatment strategies of breast cancers depends on an array of factors like age at diagnosis, menstrual status, dietary pattern, immunological response, genetic variations of the cancer cells etc. Recent technological advancements in cancer diagnosis lead to the emergence of gene expression pattern for better understanding of the tumor behavior. It has not only bolstered the prognosis, but also the early diagnosis and therapy. The accuracy in disease prognosis can be boosted when gene expression signatures are combined with traditional clinicopathological features. This review explains how the evolution of gene expression signatures for breast cancers, its advantages and future prospects. In addition, an overview of currently available gene expression signature analysis tools and consolidated information on their current status and specific benefits, that can be availed for breast cancer diagnosis are also discussed.

Advances in Diagnostic Procedures and Their Applications in the Era of Cancer Immunotherapy

Diagnostic procedures play critical roles in cancer immunotherapy. In this chapter, we briefly discuss three major diagnostic procedures widely used in immunotherapy: immunohistochemistry, next-generation sequencing, and flow cytometry. We also describe the uses of other diagnostic procedures and preclinical animal models in cancer immunotherapy translational research.

Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk

Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.

Simultaneous isolation and detection of single breast cancer cells using surface-enhanced Raman spectroscopy

Nowadays, cancer is one of the most dangerous and deadly disease all around the world. Cancer that is diagnosed at early stages is more likely to be treated successfully. Treatment of progressed cancer is very difficult, and generally surviving rates are much lower. Therefore, much research has been focused on developing non-invasive methods for detection of cancer and monitoring of its progress. Within this contribution, we present a novel strategy for selective isolation and detection of breast cancer cell lines (MCF-7 and BT-20) based on surface enhanced Raman scattering (SERS). A simplified protocol based on cell-aptamer interaction has been developed in which core-shell (Au@Fe₃O₄) nanoparticles (CSNs) were functionalized with a mucin 1 (MUC1) specific aptamer (Apt1) to capture cells through the interaction between Apt1 and overexpressed protein (MUC1) on the surface of the tumor cells. Meanwhile, a SERS nano-tag, synthesized by the conjugation of Apt1 to the surface of BSA coated and with 4-mercaptopyridine (4-Mpy) functionalized gold nanoparticles, was used to detect the isolated cells. As a conclusion, the proposed strategy can be extended to isolate and detect cells more precisely based on the detection of different kinds of biomarkers on the surface of cancer cells, simultaneously.

Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g., mammals and microbes) using diverse types of data.

CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples.

AVAILABILITY

CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper.

Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups

Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.

Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers.

Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case

Precise cancer classification is a central challenge in clinical cancer research such as diagnosis, prognosis and metastasis prediction. Most existing cancer classification methods based on gene or metabolite biomarkers were limited to single genomics or metabolomics, and lacked integration and utilization of multiple ‘omics’ data. The accuracy and robustness of these methods when applied to independent cohorts of patients must be improved. In this study, we propose a directed random walk-based method to evaluate the topological importance of each gene in a reconstructed gene–metabolite graph by integrating information from matched gene expression profiles and metabolomic profiles. The joint use of gene and metabolite information contributes to accurate evaluation of the topological importance of genes and reproducible pathway activities. We constructed classifiers using reproducible pathway activities for precise cancer classification and risk metabolic pathway identification. We applied the proposed method to the classification of prostate cancer. Within-dataset experiments and cross-dataset experiments on three independent datasets demonstrated that the proposed method achieved a more accurate and robust overall performance compared to several existing classification methods. The resulting risk pathways and topologically important differential genes and metabolites provide biologically informative models for prostate cancer prognosis and therapeutic strategies development.

Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform these data into biological knowledge, for example, how to use these data to answer questions such as: Which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction, and network interrogation. Here we provide an overview of network analysis including a step-by-step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.

Downregulated microRNA-510-5p acts as a tumor suppressor in renal cell carcinoma

MicroRNA (miR)-510-5p has been demonstrated to be involved in a number of types of malignancy; however, the function of miR-510-5p in renal cancer remains unclear. The present study aimed to determine the expression of miR-510-5p in renal cell carcinoma (RCC) specimens and analyzed the impact of miR-510-5p on renal cancer by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, wound scratch and apoptosis assays. The results showed that miR-510-5p was significantly downregulated in RCC specimens compared with normal renal specimens. Overexpression of miR-510-5p by synthetic mature mimics reduced cell proliferation and migration and induced an increase in cell apoptosis, indicating that miR-510-5p may act as a tumor suppressor in RCC. The present study firstly revealed that downregulated miR-510-5p functioned as a tumor suppressor by reducing cellular proliferation and migration, and inducing apoptosis in RCC. Further research is required to define target genes of miR-510-5p to determine the cellular mechanism of miR-510-5p in the carcinogenesis of RCC.

Pathological bases for a robust application of cancer molecular classification

Any robust classification system depends on its purpose and must refer to accepted standards, its strength relying on predictive values and a careful consideration of known factors that can affect its reliability. In this context, a molecular classification of human cancer must refer to the current gold standard (histological classification) and try to improve it with key prognosticators for metastatic potential, staging and grading. Although organ-specific examples have been published based on proteomics, transcriptomics and genomics evaluations, the most popular approach uses gene expression analysis as a direct correlate of cellular differentiation, which represents the key feature of the histological classification. RNA is a labile molecule that varies significantly according with the preservation protocol, its transcription reflect the adaptation of the tumor cells to the microenvironment, it can be passed through mechanisms of intercellular transference of genetic information (exosomes), and it is exposed to epigenetic modifications. More robust classifications should be based on stable molecules, at the genetic level represented by DNA to improve reliability, and its analysis must deal with the concept of intratumoral heterogeneity, which is at the origin of tumor progression and is the byproduct of the selection process during the clonal expansion and progression of neoplasms. The simultaneous analysis of multiple DNA targets and next generation sequencing offer the best practical approach for an analytical genomic classification of tumors.

Ménage à trois: an evolutionary interplay between human papillomavirus, a tumor, and a woman

Cervical cancer is the third most common cancer in women with human papillomavirus (HPV) being a key etiologic factor of this devastating disease. In this article, we describe modern advances in the genomics and transcriptomics of cervical cancer that led to uncovering the key gene drivers. We also introduce, herein, a model of cervical carcinogenesis that explains how the interplay between virus, tumor, and woman results in the selection of clones that simultaneously harbor genomic amplifications for genes that drive cell cycle, antiviral response, and inhibit cell differentiation. The new model may help researchers understand the controversies in antiviral therapy and immunogenetics of this cancer and may provide a basis for future research directions in early diagnostics and personalization of therapy.

VEZT, a novel putative tumor suppressor, suppresses the growth and tumorigenicity of gastric cancer

Vezatin (VEZT), an adherens junctions transmembrane protein, was identified as a putative tumor suppressor in our previous study. However, the role of VEZT in tumorigenesis remains elusive. We aimed to clarify its epigenetic regulation and biological functions in gastric cancer. In this study, we show that the expression level of VEZT is involved in lymphatic metastasis, depth of cancer invasion and TNM stage in 104 gastric cancer patients. Bisulfate sequencing polymerase chain reaction (BSP) methods showed that VEZT was hypermethylated in tissues and corresponding blood of gastric cancer patients compared with healthy controls. Helicobacter pylori (H. pylori) infection induces the methylation and silencing of VEZT in GES-1 cells. Restoring VEZT expression in MKN-45 and NCI-N87 gastric cancer cells inhibited growth, invasion and tumorigenesis in vitro and in vivo. Global microarray analysis was applied to analyze the molecular basis of the biological functions of VEZT after VEZT transfection combined with real-time PCR and chromatin immunoprecipitation assay. G protein-coupled receptor 56(GPR56), cell growth, cell division cycle 42(CDC42), migration/invasion and transcription factor 19(TCF19), cell cycle progression, were identified as direct VEZT target genes. TCF19, a novel target of VEZT, was functionally validated. Overexpression of TCF19 in MKN-45 cells increased cell cycle progress and growth ability. This study provides novel insight into the regulation of the VEZT gene, which could represent a potential target for therapeutic anti-cancer strategies.

Topologically inferring risk-active pathways toward precise cancer classification by directed random walk

MOTIVATION

The accurate prediction of disease status is a central challenge in clinical cancer research. Microarray-based gene biomarkers have been identified to predict outcome and outperform traditional clinical parameters. However, the robustness of the individual gene biomarkers is questioned because of their little reproducibility between different cohorts of patients. Substantial progress in treatment requires advances in methods to identify robust biomarkers. Several methods incorporating pathway information have been proposed to identify robust pathway markers and build classifiers at the level of functional categories rather than of individual genes. However, current methods consider the pathways as simple gene sets but ignore the pathway topological information, which is essential to infer a more robust pathway activity.

RESULTS

Here, we propose a directed random walk (DRW)-based method to infer the pathway activity. DRW evaluates the topological importance of each gene by capturing the structure information embedded in the directed pathway network. The strategy of weighting genes by their topological importance greatly improved the reproducibility of pathway activities. Experiments on 18 cancer datasets showed that the proposed method yielded a more accurate and robust overall performance compared with several existing gene-based and pathway-based classification methods. The resulting risk-active pathways are more reliable in guiding therapeutic selection and the development of pathway-specific therapeutic strategies.

AVAILABILITY

DRW is freely available at http://210.46.85.180:8080/DRWPClass/

ToP: a trend-of-disease-progression procedure works well for identifying cancer genes from multi-state cohort gene expression data for human colorectal cancer

Significantly expressed genes extracted from microarray gene expression data have proved very useful for identifying genetic biomarkers of diseases, including cancer. However, deriving a disease related inference from a list of differentially expressed genes has proven less than straightforward. In a systems disease such as cancer, how genes interact with each other should matter just as much as the level of gene expression. Here, in a novel approach, we used the network and disease progression properties of individual genes in state-specific gene-gene interaction networks (GGINs) to select cancer genes for human colorectal cancer (CRC) and obtain a much higher hit rate of known cancer genes when compared with methods not based on network theory. We constructed GGINs by integrating gene expression microarray data from multiple states--healthy control (Nor), adenoma (Ade), inflammatory bowel disease (IBD) and CRC--with protein-protein interaction database and Gene Ontology. We tracked changes in the network degrees and clustering coefficients of individual genes in the GGINs as the disease state changed from one to another. From these we inferred the state sequences Nor-Ade-CRC and Nor-IBD-CRC both exhibited a trend of (disease) progression (ToP) toward CRC, and devised a ToP procedure for selecting cancer genes for CRC. Of the 141 candidates selected using ToP, ∼50% had literature support as cancer genes, compared to hit rates of 20% to 30% for standard methods using only gene expression data. Among the 16 candidate cancer genes that encoded transcription factors, 13 were known to be tumorigenic and three were novel: CDK1, SNRPF, and ILF2. We identified 13 of the 141 predicted cancer genes as candidate markers for early detection of CRC, 11 and 2 at the Ade and IBD states, respectively.

Data Mining and Meta-Analysis on DNA Microarray Data

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

Comprehensive literature review and statistical considerations for microarray meta-analysis

With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

Microarray meta-analysis defines global angiogenesis-related gene expression signatures in human carcinomas

Angiogenesis is a prerequisite for progression of cancers. The number of genes linked to angiogenesis suggests the existence of complex gene-networks, which remain to be elucidated. To identify angiogenesis genes deregulated in carcinomas, we performed a meta-profiling analysis of published gene expression microarray studies. Own microarray and quantitative RT-PCR data were obtained from a colorectal carcinoma cohort. Applying highly stringent inclusion criteria, 15 cancer array studies were suitable for our analysis. These studies provided 789 tumor specimens and 190 samples of healthy tissues yielding a total of approx. 1,000,000 gene expression measurements. Meta-analysis on the expression of 480 angiogenesis-related genes in 10 cancer types identified a characteristic, entity-independent "global" cancer expression signature of 25 angiogenesis-related genes showing high frequency down-regulation when compared to corresponding healthy tissues. Furthermore, we characterized 25 genes displaying frequent up-regulation, yet less often than the 25 down-regulated genes. Comparative inter-study cross-validation revealed that both signatures discriminate cancers from healthy tissues with high accuracy in independent test sets. Moreover, own microarray data of colorectal carcinomas confirmed the specific and sensitive discriminating potential of both signatures. These results were validated by quantitative RT-PCR for eight genes displaying the highest differences in the microarray analysis. Our study for the first time defines global gene expression signatures linked to angiogenesis in carcinomas. Our findings suggest that gene down-regulation may represent a central aspect of tumor angiogenesis.

Homeobox gene IRX1 is a tumor suppressor gene in gastric carcinoma

The IRX1 tumor suppressor gene is located on 5p15.33, a cancer susceptibility locus. Loss of heterozygosity of 5p15.33 in gastric cancer was identified in our previous work. In this study, we analyzed the molecular features and function of IRX1. We found that IRX1 expression was lost or reduced in gastric cancer. However, no mutations were identified in IRX1-encoding regions. IRX1 transcription was suppressed by hypermethylation, and the expression of IRX1 mRNA was partially restored in gastric cancer cells after 5-Aza-dC treatment. Restoring IRX1 expression in SGC-7901 and NCI-N87 gastric cancer cells inhibited growth, invasion and tumorigenesis in vitro and in vivo. We identified a number of target genes by global microarray analysis after IRX1 transfection combined with real-time PCR and chromatin immunoprecipitation assay. BDKRB2, an angiogenesis-related gene, HIST2H2BE and FGF7, cell proliferation and invasion-related genes, were identified as direct IRX1 target genes. The hypermethylation of IRX1 was not only detected in primary gastric cancer tissues but also in the peripheral blood of gastric cancer patients, suggesting IRX1 could potentially serve as a biomarker for gastric cancer.

Global changes in processing of mRNA 3' untranslated regions characterize clinically distinct cancer subtypes

Molecular cancer diagnostics are an important clinical advance in cancer management, but new methods are still needed. In this context, gene expression signatures obtained by microarray represent a useful molecular diagnostic. Here, we describe novel probe-level microarray analyses that reveal connections between mRNA processing and neoplasia in multiple tumor types, with diagnostic potential. We now show that characteristic differences in mRNA processing, primarily in the 3'-untranslated region, define molecular signatures that can distinguish similar tumor subtypes with different survival characteristics, with at least 74% accuracy. Using a mouse model of B-cell leukemia/lymphoma, we find that differences in transcript isoform abundance are likely due to both alternative polyadenylation (APA) and differential degradation. While truncation of the 3'-UTR is the most common observed pattern, genes with elongated transcripts were also observed, and distinct groups of affected genes are found in related but distinct tumor types. Genes with elongated transcripts are overrepresented in ontology categories related to cell-cell adhesion and morphology. Analysis of microarray data from human primary tumor samples revealed similar phenomena. Western blot analysis of selected proteins confirms that changes in the 3'-UTR can correlate with changes in protein expression. Our work suggests that alternative mRNA processing, particularly APA, can be a powerful molecular biomarker with prognostic potential. Finally, these findings provide insights into the molecular mechanisms of gene deregulation in tumorigenesis.

Accurate and reliable cancer classification based on probabilistic inference of pathway activity

With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers, for building the classifier. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes, and use the pathway activities for classification. It has been shown that pathway-based classifiers typically yield more reliable results compared to traditional gene-based classifiers. In this paper, we propose a new classification method based on probabilistic inference of pathway activities. For a given sample, we compute the log-likelihood ratio between different disease phenotypes based on the expression level of each gene. The activity of a given pathway is then inferred by combining the log-likelihood ratios of the constituent genes. We apply the proposed method to the classification of breast cancer metastasis, and show that it achieves higher accuracy and identifies more reproducible pathway markers compared to several existing pathway activity inference methods.

Diagnostic microarrays in hematologic oncology: applications of high- and low-density arrays

Microarrays have become important tools for high-throughput analysis of gene expression, chromosome aberrations, and gene mutations in cancer cells. In addition to high-density experimental microarrays, low-density, gel-based biochip technology represents a versatile platform for translation of research into clinical practice. Gel-based microarrays (biochips) consist of nanoliter gel drops on a hydrophobic surface with different immobilized biopolymers (primarily nucleic acids and proteins). Because of the high immobilization capacity of the gel, such biochips have a high probe concentration and high levels of fluorescence signals after hybridization, which allow the use of simple, portable detection systems. The notable accuracy of the analysis is reached as a result of the high level of discrimination between positive and negative gel-bound probes. Different applications of biochips in the field of hematologic oncology include analysis of chromosomal translocations in leukemias, diagnostics of T-cell lymphomas, and pharmacogenetics.

A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data

Motivation: Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted to simulate the gene–endophenotype–disease relationship in which endophenotypes were represented by hidden nodes.

Results: We successfully constructed a three-endophenotype model for Taiwanese hypertensive males with high identification accuracy. Of the three endophenotypes, one is strongly protective, another is weakly protective and the third is highly correlated with developing young-onset male hypertension. Sixteen of the involved 101 genes were highly and consistently influential to the endophenotypes. Identification of SLC4A5, SLC5A10 and LDOC1 indicated that sodium/bicarbonate transport, sodium/glucose transport and cell-proliferation regulation may play important upstream roles and identification of BNIP1, APOBEC3F and LDOC1 suggested that apoptosis, innate immune response and cell-proliferation regulation may play important downstream roles in hypertension. The involved genes not only provide insights into the mechanism of hypertension but should also be considered in future gene mapping endeavors.

Availability: Microarray data and test program are available at http://ms.iis.sinica.edu.tw/microarray/index.htm

Contact:pan@ibms.sinica.edu.tw or hsu@iis.sinica.edu.tw

Supplementary information:Supplementary data are available at Bioinformatics online.

Introduction to microarray technology

DNA microarrays can be used for large number of application where high-throughput is needed. The ability to probe a sample for hundred to million different molecules at once has made DNA microarray one of the fastest growing techniques since its introduction about 15 years ago. Microarray technology can be used for large scale genotyping, gene expression profiling, comparative genomic hybridization and resequencing among other applications. Microarray technology is a complex mixture of numerous technology and research fields such as mechanics, microfabrication, chemistry, DNA behaviour, microfluidics, enzymology, optics and bioinformatics. This chapter will give an introduction to each five basic steps in microarray technology that includes fabrication, target preparation, hybridization, detection and data analysis. Basic concepts and nomenclature used in the field of microarray technology and their relationships will also be explained.

Growth differentiation factor 15 production is necessary for normal erythroid differentiation and is increased in refractory anaemia with ring-sideroblasts

The disturbed erythropoiesis in patients with refractory anaemia with ring-sideroblasts (RARS) is characterized by intramedullary apoptosis of erythroid precursors and increased iron accumulation in mitochondria. To gain insight into these pathophysiological mechanisms we compared the gene expression profile (GEP) of erythroid precursors from RARS patients to the GEP of normal erythroid precursors. Three hundred sixty four probe sets were up-, and 253 probe sets downregulated in RARS cells. Interestingly, Growth Differentiation factor 15 (GDF15), a cytokine from the TGFbeta family, was dramatically upregulated in all RARS patients. Measurement of GDF15 in the sera from twenty RARS patients confirmed this finding by showing significantly, 7.2-fold, increased protein levels (3254 +/- 1400 ng/ml vs. 451 +/- 87 ng/ml in normals). In vitro studies demonstrated erythroid-specific production of GDF15 and dependence on erythropoietin. Induction of apoptosis by arsenic trioxide, a drug which acts via reduction of the mitochondrial membrane potential, also stimulated GDF15 production. Downregulation of endogenous GDF15 production in erythoblasts by specific siRNA led to diminished erythroid differentiation. Taken together, our findings demonstrate a new role for GDF15 in normal erythropoiesis as well as in the ineffective erythropoiesis of RARS patients.

Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders

BACKGROUND

The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot.

METHODOLOGY/PRINCIPAL FINDINGS

The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%.

CONCLUSION

Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.