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PubMed2001

Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling.

Critical aspects of the biology and molecular basis for prostate malignancy remain poorly understood. To reveal fundamental differences between benign and malignant growth of prostate cells, we performed gene expression profiling of primary human prostate cancer and benign prostatic hyperplasia (BPH) using cDNA microarrays consisting of 6500 human genes. Frozen prostate specimens were processed to facilitate extraction of RNA from regions of tissue enriched in either benign or malignant epithelial cell growth within a given specimen. Gene expression in each of the 16 prostate cancer and nine BPH specimens was compared with a common reference to generate normalized measures for each gene across all of the samples. Using an analysis of complete pairwise comparisons of expression profiles among all of the samples, we observed clearly discernable patterns of overall gene expression that differentiated prostate cancer from BPH. Further analysis of the data identified 210 genes with statistically significant differences in expression between prostate cancer and BPH. These genes include many not recognized previously as differentially expressed in prostate cancer and BPH, including hepsin, which codes for a transmembrane serine protease. This study reveals for the first time that significant and widespread differences in gene expression patterns exist between benign and malignant growth of the prostate gland. Gene expression analysis of prostate tissues should help to disclose the molecular mechanisms underlying prostate malignant growth and identify molecular markers for diagnostic, prognostic, and therapeutic use.

Gene Expression Profiling↗

PubMed2005

Gene expression profiling of minor salivary glands clearly distinguishes primary Sjögren's syndrome patients from healthy control subjects.

OBJECTIVE: To identify gene expression signatures in minor salivary glands (MSGs) from patients with primary Sjogren's syndrome (SS). METHODS: A 16K complementary DNA microarray was used to generate gene expression profiles in MSGs obtained from 10 patients with primary SS and 10 control subjects. The data were analyzed by 2 different strategies, one strict primary analysis and one subanalysis that allowed for inclusion of genes with no signal in more than 3 samples from each group. The results were validated by quantitative reverse transcriptase-polymerase chain reaction techniques. RESULTS: We found a distinct difference in gene expression levels in MSGs, enabling a simple class prediction method to correctly classify 19 of the 20 samples as either patient or control, based on the top 5 differentially expressed genes. The 50 most differentially expressed genes in the primary SS group compared with the control group were all up-regulated, and a clear pattern of genes involved in chronic inflammation was found. CXCL13 and CD3D were expressed in >/=90% of primary SS patients and in </=10% of the controls. Lymphotoxin beta, as well as a number of major histocompatibility complex genes, cytokines, and lymphocyte activation factors, manifested its role in the pathogenesis of SS. Numerous type I interferon genes related to virus infection were found among the top 200 genes, with increased expression in primary SS. Interestingly, the expression of carbonic anhydrase II, which is essential in saliva production and secretion, and the apoptosis regulator Bcl-2-like 2 were down-regulated in primary SS patients. CONCLUSION: We have identified distinct gene expression profiles in MSGs from patients with primary SS that provide new knowledge about groups of genes that are up-regulated or down-regulated during disease, constituting an excellent platform for forthcoming functional studies.

Adult↗

PubMed2003

Gene expression profile of AIDS-related Kaposi's sarcoma.

BACKGROUND: Kaposi's Sarcoma (KS) is a proliferation of aberrant vascular structures lined by spindle cells, and is caused by a gammaherpes virus (HHV8/KSHV). Its course is aggravated by co-infection with HIV-1, where the timing of infection with HIV-1 and HHV8 is important for the clinical outcome. METHODS: In order to better understand the pathogenesis of KS, we have analysed tissue from two AIDS-KS lesions, and from normal skin by serial analysis of gene expression (SAGE). Semi-quantitative RT-PCR was then used to validate the results. RESULTS: The expression profile of AIDS-related KS (AIDS-KS) reflects an active process in the skin. Transcripts of HHV8 were found to be very low, and HIV-1 mRNA was not detected by SAGE, although it could be found using RT-PCR. Comparing the expression profile of AIDS-KS tissue with publicly available SAGE libraries suggested that AIDS-KS mRNA levels are most similar to those in an artificially mixed library of endothelial cells and leukocytes, in line with the description of KS lesions as containing spindle cells with endothelial characteristics, and an inflammatory infiltrate. At least 64 transcripts were found to be significantly elevated, and 28 were statistically downregulated in AIDS-KS compared to normal skin. Five of the upregulated mRNAs, including Tie 1 and sialoadhesin/CD169, were confirmed by semi-quantitative PCR to be elevated in additional AIDS-KS biopsies. Antibodies to sialoadhesin/CD169, a known marker of activated macrophages, were shown to specifically label tumour macrophages. CONCLUSION: The expression profile of AIDS-KS showed 64 genes to be significantly upregulated, and 28 genes downregulated, compared with normal skin. One of the genes with increased expression was sialoadhesin (CD169). Antibodies to sialoadhesin/CD169 specifically labelled tumour-associated macrophages, suggesting that macrophages present in AIDS-KS lesions belong to a subset of human CD169+ macrophages.

Acquired Immunodeficiency Syndrome↗

PubMed2006

Effects of antidepressant treatment on gene expression profile in mouse brain: cell type-specific transcription profiling using laser microdissection and microarray analysis.

A gene expression study of mice treated with the tricyclic antidepressant amitriptyline was performed. To enable the detection of cell type-specific expression changes, laser-microdissected nucleus accumbens was analysed after 4 and 28 days of treatment. After 4 days of treatment no significantly regulated genes could be detected in this study. In contrast, 95 genes exhibited different expression levels in animals treated for 28 days with amitrityline compared with sham animals. This observation reflects the long-term effects and adaptation processes observed in patients treated with this drug. Among the regulated genes are receptors belonging to the dopamine-dependent signalling cascade, ion channels (mainly voltage-dependent potassium and calcium channels) potentially involved in signalling cascades and neuropeptides. The results support the hypothesis that the therapeutic effect of this antidepressant is much more complex and not confined to a reuptake inhibition of neurotransmitters. Paradigms inducing only weak expression changes, which may be limited to certain cell types within the highly complex brain structure, can therefore be reliably investigated by applying a cell type-specific expression profiling technique based on laser microdissection and subsequent RNA amplification followed by DNA microarray analysis.

Amitriptyline↗

PubMed2000

Gene expression profiling of cardiovascular disease models.

Recent development of gene expression profiling technologies has enabled the large-scale analysis of gene expression changes during disease progression. Frequently, cardiovascular diseases involve complex interactions of multiple cell types over prolonged periods of time. A better understanding of the pathology of cardiovascular diseases and the potential identification of underlying genetic defects are currently being explored by using profiling methodologies in a number of animal and tissue-culture models.

Animals↗

PubMed2003

Gene expression profile of normal lungs predicts genetic predisposition to lung cancer in mice.

Genetic susceptibility to lung tumorigenesis shows large variations among mouse strains. To test whether genetic predisposition to lung tumorigenesis is associated with a specific gene expression profile in normal lungs, we analyzed gene expression in 16 inbred strains of known susceptibility/resistance to lung tumorigenesis, using the RIKEN mouse full-length cDNA 19K microarray set. The strain-specific expression profile of 91 cDNA clones correlated with strain lung tumor susceptibility/resistance and predicted, by principal component analysis, the genetic predisposition to lung tumorigenesis in mice.

Animals↗

PubMed2006

Genome-wide gene expression profile analysis of esophageal squamous cell carcinomas.

To identify the molecules involved in esophageal carcinogenesis and those applicable as novel tumor markers and for the development of new molecular therapies, we performed gene expression profile analysis of 19 esophageal squamous cell carcinoma (ESCC) cells purified by laser microbeam microdissection (LMM). Using a cDNA microarray representing 32,256 genes, we identified 147 genes that were commonly up-regulated and 376 transcripts that were down-regulated in ESCC cells compared with non-cancerous esophageal epithelial cells. A comparison of clinicopathological data with the expression profiles of the 19 ESCCs identified 20 genes whose expression levels could most significantly separate cases with lymph node metastasis from those without. In addition, immunohistochemical analysis of candidate tumor markers on tissue microarrays demonstrated transactivation of a secretory protein, transforming growth factor alpha (TGFA) in the great majority of 228 ESCC cases and an association of their expression with the poor prognosis of patients. Our data provide valuable information for establishing novel diagnostic markers for early diagnosis and choice of therapy, and identifying therapeutic target molecules for the development of novel anti-cancer drugs and immunotherapy in esophageal cancer treatment.

Aged↗

PubMed2004

Gene expression profiling identifies platelet-derived growth factor as a diagnostic molecular marker for papillary thyroid carcinoma.

PURPOSE: Cancer diagnostics and therapeutics are often based on clinically relevant markers that are expressed specifically in a malignant tissue at levels higher than in normal tissue. We examined potential markers for papillary thyroid carcinoma (PTC) by monitoring PTC-specific gene expression using cDNA microarray. EXPERIMENTAL DESIGN: Gene expression profiles for PTC tissue, normal thyroid tissue, and healthy peripheral blood cells were compared by use of a human 4000-gene cDNA microarray. Protein expressions of the up-regulated genes in PTC were examined in thyroid tissues by immunohistochemistry. RESULTS: Sixty-four genes were overexpressed in PTC tissue relative to normal thyroid tissue and healthy peripheral blood cells. The genes that were up-regulated in PTC were involved in cell cycle regulation, DNA damage response, angiogenesis, and oncogenesis. Among these genes, basic fibroblast growth factor and platelet-derived growth factor were identified by immunochemical methods as proteins that are specifically expressed at high levels in thyroid neoplasms. Basic fibroblast growth factor, which has been identified as a biomarker for PTC, was overexpressed in 54% of PTC cases, 67% of follicular thyroid carcinomas, and 36% of benign thyroid neoplasms. Platelet-derived growth factor was overexpressed in 81% of PTC cases and 100% of follicular carcinomas, but was immunonegative in normal thyroid tissues and benign thyroid neoplasms. CONCLUSIONS: Platelet-derived growth factor may be a potential biomarker for PTC and follicular carcinoma. Expression profile analysis using a microarray followed by immunohistochemical study can be used to facilitate the development of molecular biomarkers for cancer.

Carcinoma, Papillary↗

PubMed2005

A fully Bayesian model to cluster gene-expression profiles.

MOTIVATION: With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or a common cellular origin. With most methods, e.g. k-means, the number of clusters needs to be specified in advance; results depend strongly on this choice. Even with likelihood-based methods, estimation of this number is difficult. Furthermore, missing values often cause problems and lead to the loss of data. RESULTS: We propose a fully probabilistic Bayesian model to cluster gene-expression profiles. The number of classes does not need to be specified in advance; instead it is adjusted dynamically using a Reversible Jump Markov Chain Monte Carlo sampler. Imputation of missing values is integrated into the model. With simulations, we determined the speed of convergence of the sampler as well as the accuracy of the inferred variables. Results were compared with the widely used k-means algorithm. With our method, biologically related co-expressed genes could be identified in a yeast transcriptome dataset, even when some values were missing. AVAILABILITY: The code is available at http://genome.tugraz.at/BayesianClustering/

Algorithms↗

PubMedDate not supplied

Diagnostic pattern recognition on gene-expression profile data by using one-class classification.

In this paper, we perform diagnostic pattern recognition on a gene-expression profile data set by using one-class classification. Unlike conventional multiclass classifiers, the one-class (OC) classifier is built on one class only. For optimal performance, it accepts samples coming from the class used for training and rejects all samples from other classes. We evaluate six OC classifiers: the Gaussian model, Parzen windows, support vector data description (with two types of kernels: inner product and Gaussian), nearest neighbor data description, K-means, and PCA on three gene-expression profile data sets, those being an SRBCT data set, a Colon data set, and a Leukemia data set. Providing there is a good splitting of training and test samples and feature selection, most OC classifiers can produce high quality results. Parzen windows and support vector data description are "over-strict" in most cases, while nearest neighbor data description is "over-loose". Other classifiers are intermediate between these two extremes. The main difficulty for the OC classifier is it is difficult to obtain an optimum decision threshold if there are a limited number of training samples.

Colon↗

PubMed2026

Evolutionary characterization and expression profiling of ACC and FASN genes in Chinese mitten crab Eriocheir sinensis.

Acetyl-CoA carboxylase (ACC) and fatty acid synthase (FASN) are rate-limiting enzymes in the fatty acid biosynthetic pathway, yet their evolutionary relationships, sequence features, and expression profiles remain poorly understood in crustaceans, particularly in the economically important Chinese mitten crab (Eriocheir sinensis). Here, we identified and systematically analyzed ACC and FASN genes in E. sinensis using comparative genomic analyses across 43 species. ACC was highly conserved as a single-copy gene in invertebrates, in contrast to the multiple paralogs observed in vertebrates. Similarly, FASN was generally maintained as a single-copy gene across most taxa but exhibited lineage-specific expansion in certain insect groups. Phylogenetic and structural analysis revealed strong conservation of both genes within crustaceans, supported by multiple conserved motifs and canonical functional domains. Expression profiling showed predominant expression in the hepatopancreas and midgut, suggesting their potential involvement in crustacean lipid metabolism. During the molting cycle, ACC and FASN exhibited higher expression levels during stages C and D, suggesting an increased capacity for fatty acid biosynthesis before molting. In addition, dietary lipid levels experiment revealed that ACC and FASN expression responded dynamically to dietary lipid availability, with increased expression at moderate lipid levels but reduced expression under excessive lipid supplementation, indicating a possible adaptive transcriptional response to lipid status. Collectively, this study provides insights into the evolutionary conservation and expression dynamics of ACC and FASN and improves our understanding of lipid metabolic adaptation in crustaceans.

Animals↗

PubMed2003

Testing the validity of c-fos expression profiling to aid the therapeutic classification of psychoactive drugs.

RATIONALE: Different stimuli, including pharmacological stimuli, induce different neuroanatomical profiles of c-fos expression. Can these profiles be used in classifying psychoactive drugs and predicting therapeutic utility? OBJECTIVE: To test the validity of c-fos expression profiling to aid therapeutic classification. METHODS: Anxiolytics, antidepressants, antipsychotics and psychostimulants were compared. (i) A meta-analysis was performed and profiles compiled from literature reports of changes in c-fos expression in rat brain regions, measured by in situ hybridisation histochemistry or immunohistochemistry, after acute injection of psychoactive drugs. (ii) Male rat brains were profiled for changes in c-fos mRNA expression induced by acute injection of psychoactive drugs. RESULTS: (i) The meta-analysis showed that anxiolytics activate few (mostly stress-related) brain regions; antidepressants activate more regions, including the central amygdaloid nucleus; antipsychotics activate more regions still, including the nucleus accumbens and striatal areas; and psychostimulants activate the greatest number of all, including the most cortical regions (especially the piriform cortex). Profiles also varied within drug classes. (ii) Our experimental profiles confirmed and extended meta-analysis profiles, showing more downregulation. (iii) Sites activated by mirtazapine (an antidepressant not previously profiled) matched those of the antidepressant imipramine. CONCLUSIONS: (i) Differences between drug classes support their classification by means of c-fos profiling. Differences within classes may reflect mechanistic variations. (ii) Greater downregulation in our experiments might be because of inclusion of low, clinically relevant, drug doses and fuller coverage of brain regions. (iii) The agreement between mirtazapine and imipramine increases our confidence in the validity of c-fos expression profiling to aid drug classification and predict therapeutic utility.

Animals↗

PubMed2001

Similarity and variation in gene expression among human cerebral cortical subregions revealed by DNA macroarrays: technical consideration of RNA expression profiling from postmortem samples.

The functional regionality of the human cerebral cortex suggests that a set of genes might be activated in each subregion of the neocortex to support its specific functions. To test this hypothesis, we employed the DNA array technique to compare the mRNA expression profiles of three neocortical subregions of the human brain: prefrontal cortex (Area 46), motor cortex (Area 4) and visual cortex (Area 17). The macroarray analysis on high quality mRNA from postmortem brains revealed that the expression profiles of the different cortical areas are almost similar: only six out of 1088 known genes exhibited significant differences (>2-fold) in their expression. RT-PCR studies with an increased number of samples confirmed that expression of only two genes, annexin II and early growth response protein 1, varied by 2-fold among the regions, whereas expression of the others showed large inter-individual difference. These results suggest that the whole neocortex of humans is more homogeneous than we expected at the level of gross gene expression profiles. In parallel, sensitivity and accuracy of radioisotope-based DNA macroarrays and fluorescence-based DNA microarrays were tested.

Aged↗

PubMed2005

[Effect of gardenin on gene expression profile in brain of rats with focal cerebral ischemia].

OBJECTIVE: To explore the pharmacologic mechanism of gardenin in treating cerebral ischemia, by studying its effect on gene expression profile in brain of rats with focal cerebral ischemia (FCI). METHODS: Total RNAs were isolated from rats with FCI and those treated with gardenin. The mRNAs were reversely transcribed to cDNA with incorporation of fluorescent Cy5- or Cy3-dUTP to prepare hybridization probes. The PCR products of 4096 genes were spotted on the chip after a serial treatment. The mixed probes were hybridized to the cDNA microarray. Axon Genepix 4000B and GenePixPro 3.0 software were used to scan and analyze the fluorescent signals. RESULTS: In the group treated with gardenin, there were 70 genes had expression profiles different to that in the model group in the focal cerebral ischemic brain tissue, in which 68 were up-regulated and 2 down-regulated. CONCLUSION: Gardenin has regulatory effect on the gene expression in rats with focal cerebral ischemia, which elucidates part of the pharmacologic mechanism of Qingkailing in molecular level.

Animals↗

PubMed2000

The use of expression profiling to study pigment cell biology and dysfunction.

Regulation of gene expression is a fundamental process by which cells respond to both intracellular and extracellular signals. For a pigment cell, alterations in gene expression regulate the processes of cell migration, lineage restriction, differentiation, type of pigment produced, and progression from a normal pigment cell to that of melanoma. To date, the identification of genes involved in normal pigment cell development has been accomplished by the cloning of individual mutant alleles, a single gene at a time. Current advances in technology have now made it possible to use expression profile analysis to investigate, on a genomic scale, the process of pigment cell development and function. This review compares and contrasts the methods of subtractive suppressive polymerase chain reaction (PCR) and differential display with that of cDNA microarray analysis.

Cell Differentiation↗

PubMed2002

Expression profiling of MMA-1a and splice variant MMA-1b: new cancer/testis antigens identified in human melanoma.

Using high-density oligonucleotide array analysis, we have recently compared the gene expression profiles of 2 human melanoma cell lines with marked difference in metastatic behavior after subcutaneous inoculation into nude mice (de Wit et al., Melanoma Res, in press). We identified an expressed sequence tag (EST), which we called malignant melanoma-associated 1 (MMA-1a), showing evident differential expression between the 2 cell lines. The MMA-1a gene is localized on chromosome 21q22.2 and its mRNA exists of 4 exons. Homology search displayed a splice variant of MMA-1a that lacks exon 3 and that was called MMA-1b. Expression profiles of MMA-1a and MMA-1b are determined by reverse transciptase polymerase chain reaction (RT-PCR) analysis. Among 30 different normal tissue samples, expression of MMA-1a and MMA-1b was exclusively found in the testis after a first PCR of 30 cycles. Even more sensitive screening achieved by performing multiple semi-nested RT-PCR showed no or very low expression in the other normal tissues tested. During melanocytic tumor progression, MMA-1a and/or MMA-1b exhibited an emergence of expression in primary melanoma (20%) and melanoma metastasis samples (30%) after only 1 round of PCR. Expression of MMA-1a and/or MMA-1b was also identified in other tumor cell lines and fresh tumor samples of variable origin, e.g., lung, liver, bladder and soft tissues (sarcomas). We conclude that MMA-1a and MMA-1b are new members of the family of cancer/testis antigens.

Alternative Splicing↗

PubMed2004

Classification of human breast cancer using gene expression profiling as a component of the survival predictor algorithm.

PURPOSE: Selection of treatment options with the highest likelihood of successful outcome for individual breast cancer patients is based to a large degree on accurate classification into subgroups with poor and good prognosis reflecting a different probability of disease recurrence and survival after therapy. Here we propose a breast cancer classification algorithm taking into account three main prognostic features determined at the time of diagnosis: estrogen receptor (ER) status; lymph node (LN) status; and gene expression signatures associated with distinct therapy outcome. EXPERIMENTAL DESIGN: Using microarray expression profiling and quantitative reverse transcription-PCR analyses, we compared expression profiles of the 70-gene breast cancer survival signature in established breast cancer cell lines and primary breast carcinomas from cancer patients. We classified 295 breast cancer patients using 14-, 13-, 6-, and 4-gene survival predictor signatures into subgroups having statistically distinct probability of therapy failure (P < 0.0001). We evaluated the prognostic power of breast cancer survival predictor signatures alone and in combination with ER and LN status using Kaplan-Meier analysis. RESULTS: The breast cancer survival predictor algorithm allowed highly accurate classification into subgroups with dramatically distinct 5- and 10-year survival after therapy of a large cohort of 295 breast cancer patients with either ER+ or ER- tumors as well as LN+ or LN- disease (P < 0.0001, log-rank test). CONCLUSIONS: Our data imply that quantitative laboratory tests measuring expression profiles of a limited set of identified small gene clusters may be useful in stratification of breast cancer patients at the time of diagnosis into subgroups with statistically distinct probability of positive outcome after therapy and assisting in selection of optimal treatment strategies. The estimated increase in survival due to the optimization of treatment protocols may reach many thousands of breast cancer survivors every year at the 10-year follow-up check point.

Algorithms↗

PubMed2004

Gene expression profiles define a key checkpoint for type 1 diabetes in NOD mice.

cDNA microarrays with >11,000 cDNA clones from an NOD spleen cDNA library were used to identify temporal gene expression changes in NOD mice (1-10 weeks), which spontaneously develop type 1 diabetes, and changes between NOD and NOD congenic mice (NOD.Idd3/Idd10 and NOD.B10Sn-H2(b)), which have near zero incidence of insulitis and diabetes. The expression profiles identified two distinct groups of mice corresponding to an immature (1-4 weeks) and mature (6-10 weeks) state. The rapid switch of gene expression occurring around 5 weeks of age defines a key immunological checkpoint. Sixty-two known genes are upregulated, and 18 are downregulated at this checkpoint in the NOD. The expression profiles are consistent with increased antibody production, antigen presentation, and cell proliferation associated with an active autoimmune response. Seven of these genes map to confirmed diabetes susceptibility regions. Of these seven, three are excellent candidate genes not previously implicated in type 1 diabetes. Ten genes are differentially expressed between the NOD and congenic NOD at the immature stage (Hspa8, Hif1a, and several involved in cellular functions), while the other 70 genes exhibit expression differences during the mature (6-10 week) stage, suggesting that the expression differences of a small number of genes before onset of insulitis determine the disease progression.

Animals↗