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PubMed2004

Gene expression profiling of oral squamous cell carcinoma using laser microdissection and cDNA microarray.

Cancer diagnosis and therapy are performed on the basis of clinical stage and clinicopathological findings; however, sensitivity to therapy and prognosis may not always be the same even when considering similar cancers because it is difficult to recognize adequate biological characteristics of a cancer when determining cancer therapy. To enable personalized medicine for cancer diagnosis and therapy, which may solve this problem, we used laser microdissection and cDNA microarrays to study the gene expression profile of oral squamous cell carcinoma. Moreover, to establish an objective evaluation with this system, we examined which type of gene expression profile corresponded to the biological characteristics of this cancer. We identified several genes that were up- or downregulated in the majority of cases and clarified genes sharing common behavioral profiles between metastasis-positive and metastasis-negative cases. It was suspected that the genes that were commonly up- or downregulated in the majority of cases were important for histogenesis and acquisition of invasion and proliferation capability and that the genes sharing common behavioral profiles between metastasis-positive and metastasis-negative cases played a large role in cancer metastasis. Using the expression profile of these genes, it may be possible to evaluate cellular state and metastatic potential and use them as tumor markers. Alternately, we showed averaged gene expression profiles in cases with or without metastasis; this may reveal a profile that could evaluate metastatic potential, which is an important element in the biological characteristics of cancer. In conclusion, our system using laser microdissection and cDNA microarray may contribute to cancer diagnosis and therapy and improvement in the quality of life of cancer patients.

Adult↗

PubMed2005

Adding semantics to gene expression profiles: new tools for drug discovery.

Gene expression profiles are unveiling a wealth of new potential drug targets for a wide range of diseases, offering new opportunities for drug discoveries. The emerging challenge, however, is the effective selection of the myriad of targets to identify those with the most therapeutic utility. Numerical clustering has became a commonly used method to investigate and interpret gene expression data sets but it is often inadequate to infer the genes' and proteins' role and point to candidate genes for drug development. This review illustrates how clustering methods based on semantic characteristics, such as gene ontologies, could be used to extract more knowledge from genomic data and improve drug target and discovery processes.

Animals↗

PubMed2002

Development of cDNA microarray for expression profiling of estrogen-responsive genes.

Estrogen plays an important role in many physiological events including carcinogenesis and the development of human breast cancer. However, the molecular mechanisms of estrogen signaling in cancers have not been clarified hitherto and accurate therapeutic prediction of breast cancer is earnestly desired. We first carried out estrogen-responsive expression profiling of approximately 9000 genes in estrogen receptor-positive human MCF-7 breast cancer cells. Based on the results, estrogen-responsive genes were selected for production of a custom-made cDNA microarray. Using a microarray consisting of the narrowed-down gene subset, we first analyzed the time course of the estrogen-responsive gene expression profiles in MCF-7 cells, resulting in subdivision of the genes up-regulated by estrogen into early-responsive and late-responsive genes. The expression patterns of several genes were confirmed by Northern blot analysis. We also analyzed the effects of the estrogen antagonists ICI 182780 and 4-hydroxytamoxifen (OHT) on the estrogen-responsive gene expression profiles in MCF-7 cells. While the regulation of most of the genes by estrogen was completely abolished by ICI 182780, some genes were partially regulated by estrogen even in the presence of OHT. Furthermore, the estrogen-responsive gene expression profiles of twelve cancer cell lines derived from the breast, ovary, stomach and other tissues were obtained and analyzed by hierarchical clustering including the profiles in MCF-7 cells. Several genes also showed up-regulation or down-regulation by estrogen in cell lines other than MCF-7 cells. The significance of the estrogen-responsive genes identified in these analyses concerning the nature of cancer is discussed.

Blotting, Northern↗

PubMed2006

Algorithm to find gene expression profiles of deregulation and identify families of disease-altered genes.

MOTIVATION: Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better. RESULTS: We have developed an algorithm that searches for the genes which present a significant alteration in the variability of their expression profiles, by comparing an altered state with a control state. The algorithm provides groups of genes and assigns a statistical measure of significance to each group of genes selected. The method also includes a prefilter tool to select genes with a threshold of differential expression that can be set by the user ad casum. The method is evaluated using an experimental set of microarrays of human control and cancer samples from patients with acute promyelocytic leukemia.

Algorithms↗

PubMed2002

Gene expression profiling by cDNA array in human hepatoma cell line in response to cisplatin treatment.

Gene expression profiling with cDNA array allows simultaneous analysis of the gene expression pattern of a large number of genes and may enhance the investigation of the molecular mechanisms involved in the treatment of hepatocellular carcinoma with cisplatin. We used cDNA array technology to assess the gene expression profiles related to cell cycle regulation and apoptosis in human hepatoma Hep3B cells in response to cisplatin treatment. In Hep3B cells, apoptosis induced by cisplatin was p53-independent, and was associated with up-regulation of cell cycle regulators, pro-apoptotic genes, growth receptors, and genes involved in signal transduction. These included p33ING1, c-Abl, Bax, insulin-like growth factor binding protein 3, Siva, cyclin D1, RhoA, and Raf-1. Down-regulation of cell cycle regulator CDC2 was observed. Semi-quantitative reverse transcription-polymerase chain reaction and/or Western blot analysis performed on seven of these genes confirmed their upregulation of gene expression. Such global analysis of the cytotoxic response to chemotherapeutic drugs may yield insight into the mechanisms of drug action and allow rational design of more effective treatment strategies.

Antineoplastic Agents↗

PubMed2004

Large-scale analysis of gene expression profiles during early stages of root nodule formation in a model legume, Lotus japonicus.

Gene expression profiles during early stages of formation of symbiotic nitrogen-fixing nodules in a model legume Lotus japonicus were analyzed by means of a cDNA array of 18,144 non-redundant expressed sequence tags (ESTs) isolated from L. japonicus. Expression of a total of 1,076 genes was significantly accelerated during the successive stages that represent infection of Mesorhizobium loti, nodule primordium initiation, nodule organogenesis, and the onset of nitrogen fixation. These include 32 nodulin and nodulinhomolog genes as well as a number of genes involved in the catabolism of photosynthates and assimilation of fixed nitrogen that were previously known to be abundantly expressed in root nodules of many legumes. We also identified a large number of novel nodule-specific or enhanced genes, which include genes involved in many cellular processes such as membrane transport, defense responses, phytohormone synthesis and responses, signal transduction, cell wall synthesis, and transcriptional regulation. Notably, our data indicate that the gene expression profile in early steps of Rhizobium-legume interactions is considerably different from that in subsequent stages of nodule development. A number of genes involved in the defense responses to pathogens and other stresses were induced abundantly in the infection process, but their expression was suppressed during subsequent nodule formation. The results provide a comprehensive data source for investigation of molecular mechanisms underlying nodulation and symbiotic nitrogen fixation.

Blotting, Northern↗

PubMed2004

Discrimination of genotoxic from non-genotoxic carcinogens by gene expression profiling.

Two general mechanisms are implicated in chemical carcinogenesis. The first involves direct damage to DNA, referred to as genotoxic (GTX), to which the cell responds by repair of the damages, arrest of the cell cycle or induction of apoptosis. The second is non-DNA damaging, non-genotoxic (NGTX), in which a wide variety of cellular processes may be involved. Therefore, it can be hypothesized that modulation of the underlying gene expression patterns is profoundly distinct between GTX and NGTX carcinogens, and thus that expression profiling is applicable for classification of chemical carcinogens as GTX or NGTX. We investigated this hypothesis by analysing modulation of gene expression profiles induced by 20 chemical carcinogens in HepG2 cells with application of cDNA microarrays that contain 597 toxicologically relevant genes. In total, 22 treatments were included, divided in two sets. The training set consisted of 16 treatments (nine genotoxins and seven non-genotoxins) and the validation set of six treatments (three and three). Class discrimination models based on Pearson correlation analyses for the 20 most discriminating genes were developed with data from the training set, where after the models were tested with all data. Using all data, the correctness for classification of the carcinogens from the training set was clearly better than that for the validation set, namely 81 and 33%, respectively. Exclusion of the treatments that had only marginal effects on the expression profiles, improved the discrimination for the training and validation sets to 92 and 100% correctness, respectively. Exclusion of the gene expression signals that were hardly altered also improved classification, namely to 94 and 80%. Therefore, our study proves the principle that gene expression profiling can discriminate carcinogens with major differences in their mode of actions, namely genotoxins versus non-genotoxins.

Carcinogens↗

PubMed2004

Prediction of chemotherapeutic response in ovarian cancer with DNA microarray expression profiling.

Ovarian carcinoma is a leading cause of gynecologic cancer death in women. Despite treatment, a large number of women with ovarian cancer eventually relapse and die of the disease. Hence, recurrent ovarian cancer continues to be a therapeutic dilemma, possibly a result of the emergence of drug resistance during relapse. Recent advances in expression genomics enable global transcript analysis that leads to molecular classification of cancers and prediction of outcome and treatment response. We did a cDNA microarray examination of the expression profiles of eight primary ovarian cancers stratified into two groups based on their chemotherapeutic response. We applied a voice-speech-pattern recognition algorithm for microarray data analysis and were able to model and predict the response of these patients to chemotherapy from their expression profiles. Hence, gene expression profiling by means of DNA microarray may be applied diagnostically for predicting treatment response in ovarian cancer.

Adult↗

PubMed2006

Prognostic significance of gene expression profiles of metastatic neuroblastomas lacking MYCN gene amplification.

BACKGROUND: The aggressiveness of metastatic neuroblastomas that lack MYCN gene amplification varies with age--they are least aggressive when diagnosed in patients younger than 12 months and most aggressive when diagnosed in patients older than 24 months. However, age at diagnosis is not always associated with patient survival. We examined whether molecular classification of metastatic neuroblastomas without MYCN gene amplification at diagnosis using gene expression profiling could improve the prediction of risk of disease progression. METHODS: We used Affymetrix microarrays to determine the gene expression profiles of 102 untreated primary neuroblastomas without MYCN gene amplification obtained from children whose ages at diagnosis ranged from 0.1 to 151 months. A supervised method using diagonal linear discriminant analysis was devised to build a multigene model for predicting risk of disease progression. The accuracy of the model was evaluated using nested cross-validations, permutation analyses, and gene expression data from 15 additional tumors obtained at disease progression. RESULTS: An expression profile model using 55 genes defined a tumor signature that distinguished two groups of patients from among those older than 12 months at diagnosis and clinically classified as having high-risk disease, those with a progression-free survival (PFS) rate of 16% (95% confidence interval [CI] = 8% to 28%), and those with a PFS rate of 79% (95% CI = 57% to 91%) (P<.01). These tumor signatures also identified two groups of patients with PFS of 15% (95% CI = 7% to 27%) and 69% (95% CI = 40% to 86%) (P<.01) from among patients who were older than 18 months at diagnosis. The gene expression signature of untreated molecular high-risk tumors was also present in progressively growing tumors. CONCLUSION: Gene expression signatures of tumors obtained at diagnosis from patients with clinically indistinguishable high-risk, metastatic neuroblastomas identify subgroups with different outcomes. Accurate identification of these subgroups with gene expression profiles may facilitate development, implementation, and analysis of clinical trials aimed at improving outcome.

Adolescent↗

PubMed2006

Gene expression profiles distinguish the carcinogenic effects of aristolochic acid in target (kidney) and non-target (liver) tissues in rats.

BACKGROUND: Aristolochic acid (AA) is the active component of herbal drugs derived from Aristolochia species that have been used for medicinal purposes since antiquity. AA, however, induced nephropathy and urothelial cancer in people and malignant tumors in the kidney and urinary tract of rodents. Although AA is bioactivated in both kidney and liver, it only induces tumors in kidney. To evaluate whether microarray analysis can be used for distinguishing the tissue-specific carcinogenicity of AA, we examined gene expression profiles in kidney and liver of rats treated with carcinogenic doses of AA. RESULTS: Microarray analysis was performed using the Rat Genome Survey Microarray and data analysis was carried out within ArrayTrack software. Principal components analysis and hierarchical cluster analysis of the expression profiles showed that samples were grouped together according to the tissues and treatments. The gene expression profiles were significantly altered by AA treatment in both kidney and liver (p < 0.01; fold change > 1.5). Functional analysis with Ingenuity Pathways Analysis showed that there were many more significantly altered genes involved in cancer-related pathways in kidney than in liver. Also, analysis with Gene Ontology for Functional Analysis (GOFFA) software indicated that the biological processes related to defense response apoptosis and immune response were significantly altered by AA exposure in kidney, but not in liver. CONCLUSION: Our results suggest that microarray analysis is a useful tool for detecting AA exposure; that analysis of the gene expression profiles can define the differential responses to toxicity and carcinogenicity of AA from kidney and liver; and that significant alteration of genes associated with defense response, apoptosis and immune response in kidney, but not in liver, may be responsible for the tissue-specific toxicity and carcinogenicity of AA.

Animals↗

PubMed2005

A systematic proteomic study of seed filling in soybean. Establishment of high-resolution two-dimensional reference maps, expression profiles, and an interactive proteome database.

A high-throughput proteomic approach was employed to determine the expression profile and identity of hundreds of proteins during seed filling in soybean (Glycine max) cv Maverick. Soybean seed proteins were analyzed at 2, 3, 4, 5, and 6 weeks after flowering using two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. This led to the establishment of high-resolution proteome reference maps, expression profiles of 679 spots, and corresponding matrix-assisted laser desorption ionization time-of-flight mass spectrometry spectra for each spot. Database searching with these spectra resulted in the identification of 422 proteins representing 216 nonredundant proteins. These proteins were classified into 14 major functional categories. Proteins involved in metabolism, protein destination and storage, metabolite transport, and disease/defense were the most abundant. For each functional category, a composite expression profile is presented to gain insight into legume seed physiology and the general regulation of proteins associated with each functional class. Using this approach, an overall decrease in metabolism-related proteins versus an increase in proteins associated with destination and storage was observed during seed filling. The accumulation of unknown proteins, sucrose transport and cleavage enzymes, cysteine and methionine biosynthesis enzymes, 14-3-3-like proteins, lipoxygenases, storage proteins, and allergenic proteins during seed filling is also discussed. A user-intuitive database (http://oilseedproteomics.missouri.edu) was developed to access these data for soybean and other oilseeds currently being investigated.

Databases, Protein↗

PubMed2006

Functional hierarchical models for identifying genes with different time-course expression profiles.

Time-course studies of gene expression are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion. We introduce a functional hierarchical model for detecting temporally differentially expressed (TDE) genes between two experimental conditions for cross-sectional designs, where the gene expression profiles are treated as functional data and modeled by basis function expansions. A Monte Carlo EM algorithm was developed for estimating both the gene-specific parameters and the hyperparameters in the second level of modeling. We use a direct posterior probability approach to bound the rate of false discovery at a pre-specified level and evaluate the methods by simulations and application to microarray time-course gene expression data on Caenorhabditis elegans developmental processes. Simulation results suggested that the procedure performs better than the two-way ANOVA in identifying TDE genes, resulting in both higher sensitivity and specificity. Genes identified from the C. elegans developmental data set show clear patterns of changes between the two experimental conditions.

Algorithms↗

PubMed2005

Gene expression profiles in prostate cancer: association with patient subgroups and tumour differentiation.

Prostate carcinoma is the most common cancer of western men and is a markedly heterogeneous disease. The aim of this study was to identify signatures of differentially expressed genes in prostate cancer using DNA microarray technology, evaluating expression profiles in matched pairs of benign and malignant tissue. Samples were collected from 33 radical prostatectomies, and 52 specimens were included, representing 29 histologically verified primary tumours, 19 paired samples of malignant and benign tissue, and 4 non-paired benign tissue samples. Microarray analysis was performed using an expanded sequence verified set of 40,000 human cDNA clones, revealing several genes with significant differences between malignant and benign tissue, including recently reported genes like alpha-methylacyl-CoA racemase (AMACR) and hepsin, as well as genes relevant for tumour development and progression. Leave out cross validation (LOCV) test correctly predicted tumour or benign tissue in 47 (90.3%) out of 52 cases, significantly better than cross validation tests using randomly permuted tissue labels. Unsupervised clustering analysis revealed 3 distinct patient clusters significantly associated with Gleason score, and high grade tumours (Gleason score >/=7) accumulated in cluster 1 (C1). Gene expression profiles correctly predicted 100% of tumour samples segregating to C1, as also validated by LOCV. Gene expression profiles were analysed in filtered and floored datasets with similar results, and a pair-wise design was also tested. Gene expression profiles provided tumour clusters linked to differentiation, and revealed novel markers relevant for molecular classification, grading and therapy of prostate cancer.

Cluster Analysis↗

PubMed2005

Optimization of reporter cells for expression profiling in a microfluidic device.

The emergence of green fluorescence protein (GFP) technologies has enabled non-invasive monitoring of cell function and gene expression. GFP-based expression studies are typically performed in traditional single-dish or multi-well formats to monitor a small number of genes or conditions that do not lend well to scaling, high-throughput analysis, or single-cell measurements. We have recently developed a microfluidic device, the Living Cell Array (LCA), for monitoring GFP-based gene expression in a high-throughput manner. Here, we report the optimization of GFP reporter cell characteristics in this microfluidic device for gene expression profiling. A reporter cell line for the transcription factor NF-kappa B was generated and used as the model cell line. Reporter cells were seeded in the LCA and NF-kappa B activated by addition of the cytokine TNF-alpha . Our studies show that the fluorescence kinetics from the reporter cell line in response to both single and repeated TNF-alpha stimulation in the LCA is similar to that observed in standard tissue culture. In addition, our data also indicate that multiple expression waves can be reliably monitored from a small population of reporter cells. Using reporter cell line subcloning and cell cycle synchronization, we demonstrate that the kinetics and magnitude of induced fluorescence in the reporter cell lines can be further improved to maximize the fluorescence readout from reporter cell lines, thereby improving their applicability to live cell expression profiling. Our studies establish some of the important criteria to be considered when using reporter cell lines for dynamic expression profiling in microfluidic devices.

Cell Culture Techniques↗

PubMed2006

Localization of cystathionine beta synthase in mice ovaries and its expression profile during follicular development.

BACKGROUND: In vitro fertilization (IVF) researches have suggested that cystathionine beta synthase (CBS) is involved in oocyte development. However, little is known about the regional and cellular expression patterns of CBS in the ovary. The purpose of this study was to analyze the localization of CBS in mice ovaries and to investigate the expression profile during follicular development. METHODS: We used in situ hybridization and immunohistochemical analysis to determine CBS expression in the ovaries of female Balb/c mice. Then the follicles were collected from F1 (C57BL x Balb/c) mice and cultured in vitro. With the method of semi-quantitative RT-PCR, we also investigated the expression profile of CBS during follicular development. RESULTS: CBS was absent in the oocytes, although it was ubiquitously expressed in the ovary with the strongest expression in follicular cells at all stages. In late antral follicles, CBS expression was markedly higher in granulosa cells located close to the antrum and in cumulus cells around the oocyte. The semi-quantitative RT-PCR showed that CBS mRNA was detected in follicles at all stages in vitro. In cumulus-oocyte complexes superovulated, CBS expression also increased rapidly. CONCLUSIONS: CBS was located mainly in the follicular cells in the ovaries. The level of CBS expression is high in follicles during folliculogenesis in mice. Differences in the CBS expression profile between oocyte and follicular cells suggest a role for CBS as a mediator in interactions between oocyte and granulosa cells.

Animals↗

PubMed2006

Clustering of Pseudomonas aeruginosa transcriptomes from planktonic cultures, developing and mature biofilms reveals distinct expression profiles.

BACKGROUND: Pseudomonas aeruginosa is a genetically complex bacterium which can adopt and switch between a free-living or biofilm lifestyle, a versatility that enables it to thrive in many different environments and contributes to its success as a human pathogen. RESULTS: Transcriptomes derived from growth states relevant to the lifestyle of P. aeruginosa were clustered using three different methods (K-means, K-means spectral and hierarchical clustering). The culture conditions used for this study were; biofilms incubated for 8, 14, 24 and 48 hrs, and planktonic culture (logarithmic and stationary phase). This cluster analysis revealed the existence and provided a clear illustration of distinct expression profiles present in the dataset. Moreover, it gave an insight into which genes are up-regulated in planktonic, developing biofilm and confluent biofilm states. In addition, this analysis confirmed the contribution of quorum sensing (QS) and RpoS regulated genes to the biofilm mode of growth, and enabled the identification of a 60.69 Kbp region of the genome associated with stationary phase growth (stationary phase planktonic culture and confluent biofilms). CONCLUSION: This is the first study to use clustering to separate a large P. aeruginosa microarray dataset consisting of transcriptomes obtained from diverse conditions relevant to its growth, into different expression profiles. These distinct expression profiles not only reveal novel aspects of P. aeruginosa gene expression but also provide a growth specific transcriptomic reference dataset for the research community.

Animals↗

PubMed2002

Osteopontin identified as lead marker of colon cancer progression, using pooled sample expression profiling.

BACKGROUND: New tumor markers and markers of tumor progression are needed for improved staging and for better assessment of treatment of many cancers. Gene expression profiling techniques offer the opportunity to discover such markers. We investigated the feasibility of sample pooling strategy in combination with a novel analysis algorithm to identify markers. METHODS: Total RNA from human colon tumors (n = 60) of multiple stages (adenomas; cancers with modified Astler Collier stages B, C, and D; and liver metastases) were pooled within stages and compared with pooled normal mucosal specimens (n = 10) by using oligonucleotide expression arrays. Genes that showed consistent increases or decreases in their expression through tumor progression were identified. Northern blot analysis was used to validate the findings. All statistical tests were two-sided. RESULTS: More than 300 candidate tumor markers and more than 100 markers of tumor progression were identified. Northern analysis of 11 candidate tumor markers confirmed the gene expression changes. The gene for the secreted integrin-binding protein osteopontin was most consistently differentially expressed in conjunction with tumor progression. Its potential as a progression marker was validated (Spearman's rho = 0.903; P<.001) with northern blot analysis using RNA from an independent set of 10 normal and 43 tumor samples representing all stages. Moreover, a statistically significant correlation between osteopontin protein expression and advancing tumor stage was identified with the use of 303 additional specimens (human cancer = 185, adenomas = 67, and normal mucosal specimens = 51) (Spearman's rho = 0.667; P<.001). CONCLUSIONS: Sample pooling can be a powerful, cost-effective, and rapid means of identifying the most common changes in a gene expression profile. We identified osteopontin as a clinically useful marker of tumor progression by use of gene expression profiling on pooled samples.

Adenoma↗

PubMed2006

Identification of apolipoprotein A-II in cerebrospinal fluid of pediatric brain tumor patients by protein expression profiling.

BACKGROUND: Our aim was to detect differences in protein expression profiles of cerebrospinal fluid (CSF) from pediatric patients with and without brain tumors. METHODS: We used surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry and Q10 ProteinChip arrays to compare protein expression profiles of CSF from 32 pediatric brain tumor patients and 70 pediatric control patients. A protein with high discriminatory power was isolated and identified by subsequent anion-exchange and reversed-phase fractionation, gel electrophoresis, and mass spectrometry. The identity of the protein was confirmed by Western blotting and immunohistochemistry. RESULTS: Of the 247 detected protein peak clusters, 123 were differentially expressed between brain tumor and control patients with a false discovery rate of 1%. Double-loop classification analysis gave a mean prediction accuracy of 88% in discriminating brain tumor patients from control patients. From the 123 clusters, a highly overexpressed protein peak cluster in CSF from brain tumor patients was selected for further analysis and identified as apolipoprotein A-II. Apolipoprotein A-II expression in CSF was correlated with the CSF albumin concentration, suggesting that the overexpression of apolipoprotein A-II is related to a disrupted blood-brain barrier. CONCLUSIONS: SELDI-TOF mass spectrometry can be successfully used to find differentially expressed proteins in CSF of pediatric brain tumor and control patients. Apolipoprotein A-II is highly overexpressed in CSF of pediatric brain tumor patients, which most likely is related to a disrupted blood-brain barrier. Ongoing studies are aimed at finding subtype specific proteins in larger groups of pediatric brain tumor patients.

Apolipoprotein A-II↗