GeneHub-GEPIS: digital expression profiling for normal and cancer tissues based on an integrated gene database

GeneHub-GEPIS is a web application that performs digital expression analysis in human and mouse tissues based on an integrated gene database. Using aggregated expressed sequence tag (EST) library information and EST counts, the application calculates the normalized gene expression levels across a large panel of normal and tumor tissues, thus providing rapid expression profiling for a given gene. The backend GeneHub component of the application contains pre-defined gene structures derived from mRNA transcript sequences from major databases and includes extensive cross references for commonly used gene identifiers. ESTs are then linked to genes based on their precise genomic locations as determined by GMAP. This genome-based approach reduces incorrect matches between ESTs and genes, thus minimizing the noise seen with previous tools. In addition, the gene-centric design makes it possible to add several important features, including text searching capabilities, the ability to accept diverse input values, expression analysis for microRNAs, basic gene annotation, batch analysis and linking between mouse and human genes. GeneHub-GEPIS is available at http://www.cgl.ucsf.edu/Research/genentech/genehub-gepis/ or http://www.gepis.org/

Cross-species comparison of CpG density in the promoter regions of protein kinase oncogenes

In this report, we investigated CpG density occurred in promoter regions (PRs) and downstream flanking regions (DFR) of 61 human protein kinase oncogenes (PKOs), together with other three species: chimpanzee, mouse and horse. The quantified numbers of CpGs in the PRs of human PKOs were much higher than those of chimpanzee, mouse and horse, suggesting that the CpG density changes among the four species are associated with species evolution. Human PKOs with relatively high number of CpGs in the PRs showed stronger gene expression than the mouse PKOs in tumour tissues, but not in normal tissues. Furthermore, human PKOs with extremely high density of CpGs in the PRs exhibited much lower expression in tumour tissues than in normal tissues. Our data initially suggest that the occurrence and density of CpGs in the PRs of PKOs play an important role in regulating gene expression associated with the tumorigenesis. Thus, further improvement of our understanding of the density and spatial arrangement of CpGs in the PRs of PKOs and other oncogenes involved in tumorigenesis is very important for providing preventive and therapeutic strategies for human cancer

Identification of microRNAs as a class of biomarkers for the early diagnosis of prostate cancer : an in silico and molecular approach

>Magister Scientiae - MScProstate cancer (PCa) is the second most common form of cancer in men around the world. In many parts of Africa, data on prostate cancer is sparse. This is attributed to poor access to testing and diagnostics. The International Agency for Research on Cancer (GLOBOCAN) estimated that 28,000 deaths occurred as a result of PCa in Africa in 2008, 4500 of which were in South Africa. This figure (28,000) is predicated a rise to 57,000 over the next two decades. Currently, the most commonly used diagnostic tests for PCa are the DRE and PSA tests. The former is highly invasive and both have low specificity and poor sensitivity. Therefore, the need for a less invasive early detection method with the ability to overcome the lack of specificity and sensitivity is required. Biomarkers have recently been identified as a viable option for early detection of disease. Examples of biological indicators for disease are miRNAs. miRNAs are small non-coding RNA molecules which play a key role in controlling gene expression and certain biological processes. Studies have shown that aberrantly expressed miRNAs are a hallmark of several diseases like cancer. miRNA expression has been shown to be associated with tumour development, progression and response to therapy, suggesting their possible use as diagnostic, prognostic and predictive biomarkers. The study aimed to investigate the potential of miRNAs implicated in prostate cancer as putative biomarkers for the disease and evaluating these miRNAs in a panel of prostate as well as several other cancer cell lines using qRT-PCR. An in silico approach was used to identify 13 putative miRNAs implicated in prostate cancer of which 8 were further analysed in a parallel study and 5 in this study. Two publicly available target prediction software were used for target gene prediction of the 5 identified miRNAs. The target genes were subjected to functional analysis using web-based software, DAVID. Functions which were clustered with an enrichment score of 1.3 and greater were considered significant. Targets with gene ontologies linked to “transcription regulation”, “regulation of “apopotosis”, “extracellular region” and “metal ion binding” were considered for further analyses. Protein gene interaction analysis was performed to determine the pathways the target genes are involved in using STRING. Expression profile analysis of the genes in various tissues was also carried out using in silico methods through the TiGER and GeneHub-GEPIS databases. Analysis using DAVID resulted in 9 gene targets for the 5 miRNAs. It was found that miR3 seemed the most promising miRNA for biomarker validation based on the in silico analyses. Its target gene MNT was found to be abundantly expressed in prostate tissue from the TiGER results. The GeneHub-GEPIS results also indicated that the gene’s expression is up-regulated during prostate cancer. The expression levels of the miRNAs analysed using qRT-PCR indicated that miR3 is significantly over-expressed in prostate cancer cells when compared to the other cancer cell lines used in this study, corroborating the results observed from the in silico analyses. Another miRNA with interesting results was miR5. It was predicted to target two genes, YWHAZ and TNFSF13B. In TiGER, both were found to be expressed in prostate tissue. The genes were also found to be up regulated during prostate cancer in GeneHub-GEPIS. The expression level of miR5 in LNCaP was 15.32; it was significantly up-regulated in the cell line using qRT-PCR. However, miR5 was also present in HEPG2-7.06, MCF7-0.79, HT29-1.61 and H157-3.59. Thus, it was concluded it can be used as a biomarker in combination with other miRNAs. The miRNA miR2 was found to target the actin filament protein encoding gene AFAP1. The gene was predicted to be upregulated with a DEU of 33.25 in GeneHuB-GEPIS. The qRT-PCR analysis showed that the expression ratio in LNcaP was 8.79. However, miR2 expression was up-regulated in MCF7-0.85 and HT29-1.09 as well. The expression level of miR1 in BHP1 was found to be 4.85. It can be considered as an indicator for benign prostate hyperplasia. Future work would include investigating the expression of miR3 in a larger panel of cancer cells as well as in patient samples. In addition, analysis of the UTR sequences of the miRNAs targets experimentally to prove that the target genes identified using in silico methods, are indeed regulated by these miRNAs. Furthermore, performing gene “knock-out” studies on the genes that code for the miRNAs to study their roles in prostate cancer development

GBA server: EST-based digital gene expression profiling

Expressed Sequence Tag-based gene expression profiling can be used to discover functionally associated genes on a large scale. Currently available web servers and tools focus on finding differentially expressed genes in different samples or tissues rather than finding co-expressed genes. To fill this gap, we have developed a web server that implements the GBA (Guilt-by-Association) co-expression algorithm, which has been successfully used in finding disease-related genes. We have also annotated UniGene clusters with links to several important databases such as GO, KEGG, OMIM, Gene, IPI and HomoloGene. The GBA server can be accessed and downloaded at

Identification of biomarkers associated with cervical cancer: a combined in silico and molecular approach

>Magister Scientiae - MScCervical cancer is the leading cause of cancer mortality among black women in South Africa. It is estimated that this disease kills approximately 8 women in South Africa every day. Cervical cancer is caused by the human papillomavirus (HPV) with the most common screening method for cervical cancer being Papanicolaou (Pap) smear, test amongst others. However, less than 20% of South African women go for these tests. There are several reasons why women do not go for these tests but the invasiveness of the test is one of the major causes for the low rate of screening. Lateral flow devices offer medical diagnosis at the point- of-care, allowing for the quick initiation of the appropriate therapeutic response. These tests are more cost-effective for the healthcare delivery industry, and can potentially be used by patients to self-test in the privacy of their homes and allow them to make informed decisions about their health. Therefore, the aim of this study was to use computational methods to identify serum biomarkers for cervical cancer that can be used to develop a point-of-care diagnostic device for cervical cancer. An in silico approach was used to identify genes implicated in the initiation and development of cervical cancer. Several bioinformatics tools were employed to extract a list of genes from publicly available cancer repositories. Multiple gene enrichment analysis tools were employed to analyze the selected candidate genes. Through this pipeline, ~28190 genes were identified from the various databases and were further refined to only 10 genes. The 10 genes were identified as potential cervical cancer biomarkers. A subcellular compartmentalization analysis clustered the proteins encoded by these genes as cell surface, secretory granules and extracellular space/matrix proteins. The selected candidate genes were predicted to be specific for cervical cancer tissue in a cancer tissue specificity meta-analysis study. The expression levels of the candidate genes were compared relative to each other and a graph constructed using gene expression data generated by GeneHub-GEPIS and TiGER databases. Further gene enrichment analysis was performed such as protein-protein interactions, transcription factor analysis, pathway analysis and co-expression analysis, with 9 out of the10 of the candidate genes showing co-expression. A gene expression analysis done on cervical cancer cell lines, other cancer cell lines and normal fibroblast cell line revealed differential expression of the candidate genes. Three candidate genes were significantly expressed in cervical cancer, while the seven remaining genes showed over expression in other cancer types. The study serves as basis for future investigations to diagnosis of cervical cancer, as well as for cancers. Thus, they could also serve as potential drug targets for cancer therapeutics and diagnostics

The development of nanotechnology-based detection systems for the diagnosis of breast cancer

Philosophiae Doctor - PhDBreast cancer is one of the major causes of death in South Africa. About 1 in 29 South African women are at risk of developing this type of cancer in their lifetime. The global incidence of breast cancer also increases annually with over 1 million new cases diagnosed every year. Molecular diagnostic techniques such as qRT-PCR, Fluorescent In Situ Hybridization (FISH), Immunohistochemistry (IHC) and ELISA are used to diagnose breast cancer. Some of these diagnostic techniques make use organic fluorophores as fluorescent reporter molecules. The principle of all these diagnostic techniques is reliant on the detection of molecular biomarkers that are associated with the disease. In most cases these molecular biomarkers are DNA, RNA or proteins that are up-regulated in response to or as a result of the disease. The first aim of this study was therefore to identify membrane proteins that are up-regulated in cancers that can potentially be used as biomarkers for the detection of breast cancer. The second aim of this study was to investigate the application of quantum dots in the development of a molecular diagnostic test that can detect a breast cancer biomarker. The most commonly used method to identify molecular biomarkers for diseases have traditionally been gene expression analysis using technologies such as DNA microarray. These technologies have certain limitations and have therefore not been very successful in identifying useful disease biomarkers. Biomarker II discovery by proteomics can overcome some of these limitations and is potentially a more suitable method to identify molecular biomarkers for breast cancer. In this study proteomics in combination with Stable Isotope Labelling with Amino Acids in Cell Culture SILAC was used to do a comparative analysis of the expression levels of membrane proteins present in a human breast cancer cell line (MCF-7) derived from a breast cancer patient and a human breast cell line (MCF- 12A) derived from a healthy individual. This led to the identification of the transmembrane protein, GFRA1 as potential new biomarker for breast cancer. This study showed that this protein is over expressed in MCF-7 cells as compared to MCF-12A cells and that it is also highly expressed in the myoepthelial cells of the milk ducts of breast cancer patients. This study also demonstrates the use of molecular beacon technology to develop a DNA probe for the detection of cDNA encoding the CK19 gene, which is a known biomarker for breast cancer. In the development of this probe, quantum dots were used as the fluorescence reporter. This molecular beacon probe was able to demonstrate the over expression of CK19 in MCF-7 cells. This study shows that this technology can potentially be used as a diagnostic test for breast cancer and since quantum dots are used in the development of these molecular beacon probes, this diagnostic test can potentially facilitate the development of multiplex detection systems for the diagnosis of breast cancer. Molecular beacon technology can potentially also be used to detect novel biomarkers such as GFRA1

Search for potential gastric cancer markers using miRNA databases and gene expression analysis

Aim: The aim of this study was to identify genes that are differentially expressed in gastric tumors and to analyze the association of their expression level with tumor clinicopathologic features. Methods: In the present research, we used bioinformatic-driven search to identify miRNA that are down-regulated in gastric tumors and to find their potential targets. Then, the expression levels of some of the target mRNAs were investigated using reverse transcription polymerase chain reaction (RT-PCR) analysis. Results: As a result of the bioinformatics analysis, fifteen genes were found to be potentially differentially expressed between the tumors and normal gastric tissue. Five of them were chosen for the further analysis (WNT4, FGF12, EFEMP1, CTGF, and HSPG2) due to their important role in cell proliferation and differentiation. Expression levels of these genes were evaluated in our collection of frozen tissue samples of gastric tumor and paired normal stomach epithelia. Increased FGF12 expression was observed in diffuse type of gastric cancer while WNT4 mRNA was found to be down-regulated in intestinal type of gastric cancer. Besides, CTGF gene overexpression was revealed in diffuse type of stomach cancer in comparison with that in intestinal type. Up-regulation of CTGF was also associated with lymph node metastasis. Conclusions: The findings show its expedient to perform further investigations in order to clarify diagnostic and prognostic value of CTGF, FGF12, and WNT4’s in stomach cancer as well as the role of these genes in carcinogenesis

Identification of Novel Pax8 Targets in FRTL-5 Thyroid Cells by Gene Silencing and Expression Microarray Analysis

The differentiation program of thyroid follicular cells (TFCs), by far the most abundant cell population of the thyroid gland, relies on the interplay between sequence-specific transcription factors and transcriptional coregulators with the basal transcriptional machinery of the cell. However, the molecular mechanisms leading to the fully differentiated thyrocyte are still the object of intense study. The transcription factor Pax8, a member of the Paired-box gene family, has been demonstrated to be a critical regulator required for proper development and differentiation of thyroid follicular cells. Despite being Pax8 well-characterized with respect to its role in regulating genes involved in thyroid differentiation, genomics approaches aiming at the identification of additional Pax8 targets are lacking and the biological pathways controlled by this transcription factor are largely unknown.To identify unique downstream targets of Pax8, we investigated the genome-wide effect of Pax8 silencing comparing the transcriptome of silenced versus normal differentiated FRTL-5 thyroid cells. In total, 2815 genes were found modulated 72 h after Pax8 RNAi, induced or repressed. Genes previously reported to be regulated by Pax8 in FRTL-5 cells were confirmed. In addition, novel targets genes involved in functional processes such as DNA replication, anion transport, kinase activity, apoptosis and cellular processes were newly identified. Transcriptome analysis highlighted that Pax8 is a key molecule for thyroid morphogenesis and differentiation.This is the first large-scale study aimed at the identification of new genes regulated by Pax8, a master regulator of thyroid development and differentiation. The biological pathways and target genes controlled by Pax8 will have considerable importance to understand thyroid disease progression as well as to set up novel therapeutic strategies

An integrated approach for the systematic identification and characterization of heart-enriched genes with unknown functions

<p>Abstract</p> <p>Background</p> <p>High throughput techniques have generated a huge set of biological data, which are deposited in various databases. Efficient exploitation of these databases is often hampered by a lack of appropriate tools, which allow easy and reliable identification of genes that miss functional characterization but are correlated with specific biological conditions (e.g. organotypic expression).</p> <p>Results</p> <p>We have developed a simple algorithm (DGSA = Database-dependent Gene Selection and Analysis) to identify genes with unknown functions involved in organ development concentrating on the heart. Using our approach, we identified a large number of yet uncharacterized genes, which are expressed during heart development. An initial functional characterization of genes by loss-of-function analysis employing morpholino injections into zebrafish embryos disclosed severe developmental defects indicating a decisive function of selected genes for developmental processes.</p> <p>Conclusion</p> <p>We conclude that DGSA is a versatile tool for database mining allowing efficient selection of uncharacterized genes for functional analysis.</p

The use of EST expression matrices for the quality control of gene expression data and the development of improved algorithms for gene expression profiling in cancer

There are currently a few bioinformatics tools, such as dbEST, DDD and GEPIS to name a few, which have been widely used to retrieve and analyse EST data for gene expression levels. The Cancer Genome Anatomy Project (CGAP, run by NCBI) cDNA xProfiler and eDNA DGED tools can be used to examine EST to compare gene expression levels between cancer and normal tissue. However, neither COAP nor other similar tools provide an easy way to compare expression in normal and cancerous tissue with e.g. expression levels in related or proximal tissues at the same time while also presenting that data for study separately. Furthermore, the expression data are often assumed to be correct and no quality control tools are made available at eGAP, dbEST and GEPIS. In this study the CGAP tools were recreated with the aim of enabling a wider range of tissues to be searched and compared in a single search. The CGAP tools were found to contain many errors in their library and gene parsing algorithms, for which solutions were implemented in the recreated algorithms. A method was also devised for the tissue origin of EST libraries to be verified and for the uncharacterised libraries to be annotated with a likely tissue of origin using EST data alone. An initial list of tissue-specific genes was optimised to create gene expression matrices which could be used to determine the tissue origin of a library. The matrices were demonstrated to show potential for cancer staging and for the indication of the degree of normalisation of a library in addition to tissue typing, making tissue-specific expression a suitable quality control method for expression data. Together the improved expression profiling algorithm and the expression matrices provide new tools to assess the quality of EST data and their suitability for expression profiling.EThOS - Electronic Theses Online ServiceGBUnited Kingdo