Subclassification of lung squamous cell carcinoma
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Carl Virtanen
PROVIDER: E-GEOD-2088 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology. Keywords: other
2004-02-10 | E-GEOD-1037 | biostudies-arrayexpress
Project description:Pharmaceuticals are pseudo persistent aquatic pollutants with unknown effects at environmentally relevant concentrations. Atlantic salmon (Salmo salar) were exposed to Acetaminophen: 54.77 ± 34.67; Atenolol: 11.08 ± 7.98 and Carbamazepine: 7.85 ± 0.13 µg•L-1 for 5 days. After Acetaminophen treatment, 19 proteins were differently expressed, of which 11 were significant with respect to the control group (eight up-regulated and three down-regulated). After Atenolol treatment, 7 differently expressed proteins were obtained in comparison with the control, of which 6 could be identified (four up-regulated and 2 down-regulated). Carbamazepine exposure resulted in 15 differently expressed proteins compared with the control, with 10 of them identified (seven up-regulated and three down-regulated). Out of these, 3 features were common between Acetaminophen and Carbamazepine and one between Carbamazepine and Atenolol. One feature was common across all treatments. Principal component analysis and heat map clustering showed a clear grouping of the variability due to the applied treatments. The obtained data suggest (1) that exposure to environmentally relevant concentrations of the pharmaceuticals alters the hepatic protein expression profile of the Atlantic salmon; and (2) the existence of treatment specific processes that may be useful for biomarker development.
2014-11-18 | PXD001354 | Pride
Project description:Caco-2, HT29, CCD841CoTr exposed to 10ng/mL, 100ng/mL and 4000ng/mL folic acid
2010-06-10 | E-GEOD-2047 | biostudies-arrayexpress
Project description:Global gene expression patterns of 91 human hepatocellular carcinomas (HCC) were analyzed to define the molecular characteristics of the tumors and to test the prognostic value of the expression profiles. Total RNAs were isolated from frozen liver tissue using CsCl density gradient centrifugation methods. Total RNA from 18 normal livers were pooled and used as reference in entire microarray experiments. To obtain gene expression profile data from human HCC, 20 μg of total RNAs from tissues were used to drive fluorescently (Cy-5 or Cy-3) labeled cDNA. At least two hybridizations were carried out for each tissue using dye-swap strategy to eliminate dye labeling bias. Unsupervised classification methods revealed two distinctive subclasses of HCC that are highly associated with survival of the patients. This association was validated by five independent supervised learning methods. Genes most strongly associated with survival were identified by using the Cox proportional hazards survival analysis. This approach identified a limited number of genes that accurately predicted the length of survival and provides new molecular insight into the pathogenesis of HCC. Tumors from the low survival subclass have strong cell proliferation and anti-apoptosis gene expression signatures. In addition, the low survival subclass displayed higher expression of genes involved in ubiquitination and histone modification suggesting an etiological involvement of these processes in accelerating the progression of HCC. The biological differences identified in the HCC subclasses should provide an attractive source for the development of therapeutic targets (e.g. HIF1a) for selective treatment(s) of HCC patients.
2004-10-28 | E-GEOD-1898 | biostudies-arrayexpress
Project description:Comparison of gene expression profiles of wild-type and fl-mutant upland cotton ovules Data for six replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: wt+5-Cy5 wt+3-Cy3 Set 1 (dye swap): wt+5-Cy3 wt+3-Cy5 Set 2: wt+5-Cy5 wt+3-Cy3 Set 2 (dye swap): wt+5-Cy3 wt+3-Cy5 Set 3: wt+5-Cy5 wt+3-Cy3 Set 3 (dye swap): wt+5-Cy3 wt+3-Cy5 GSM63341, GSM63342, GSM63343, GSM63344, GSM63345, GSM63346 Data for 3 replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: wt+3-Cy5 vs wt+3-Cy3 Set 2: wt+3-Cy3 vs wt+3-Cy5 Set 3: wt+3-Cy5 vs wt+3-Cy3 GSM63365, GSM63366, GSM63367 Data for six replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: wt+3-Cy5 wt-3-Cy3 Set 1 (dye swap): wt+3-Cy3 wt-3-Cy5 Set 2: wt+3-Cy5 wt-3-Cy3 Set 2 (dye swap): wt+3-Cy3 wt-3-Cy5 Set 3: wt+3-Cy5 wt-3-Cy3 Set 3 (dye swap): wt+3-Cy3 wt-3-Cy5 GSM63368, GSM63369, GSM63370, GSM63371, GSM63372, GSM63373 Data for six replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: 0wt-Cy5 wt+3-Cy3 Set 1 (dye swap): 0wt-Cy3 wt+3-Cy5 Set 2: 0wt-Cy5 wt+3-Cy3 Set 2 (dye swap): 0wt-Cy3 wt+3-Cy5 Set 3: 0wt-Cy5 wt+3-Cy3 Set 3 (dye swap): 0wt-Cy3 wt+3-Cy5 GSM63374, GSM63375, GSM63376, GSM63377, GSM63378, GSM63379 Data for six replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: wt+3-Cy5 fl+3-Cy3 Set 1 (dye swap): wt+3-Cy3 fl+3-Cy5 Set 2: wt+3-Cy5 fl+3-Cy3 Set 2 (dye swap): wt+3-Cy3 fl+3-Cy5 Set 3: wt+3-Cy5 fl+3-Cy3 Set 3 (dye swap): wt+3-Cy3 fl+3-Cy5 GSM63391, GSM63392, GSM63393, GSM63394, GSM63395, GSM63396 Data for six replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: wt+10-Cy5 fl+10-Cy3 Set 1 (dye swap): wt+10-Cy3 fl+10-Cy5 Set 2: wt+10-Cy5 fl+10-Cy3 Set 2 (dye swap): wt+10-Cy3 fl+10-Cy5 Set 3: wt+10-Cy5 fl+10-Cy3 Set 3 (dye swap): wt+10-Cy3 fl+10-Cy5 GSM63397, GSM63398, GSM63399, GSM63400, GSM63401, GSM63402 Data for six replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: wt+10-Cy5 wt+3-Cy3 Set 1 (dye swap): wt+10-Cy3 wt+3-Cy5 Set 2: wt+10-Cy5 wt+3-Cy3 Set 2 (dye swap): wt+10-Cy3 wt+3-Cy5 Set 3: wt+10-Cy5 wt+3-Cy3 Set 3 (dye swap): wt+10-Cy3 wt+3-Cy5 GSM63403, GSM63404, GSM63405, GSM63406, GSM63407, GSM63408 Data for six replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: wt+15-Cy5 wt+3-Cy3 Set 1 (dye swap): wt+15-Cy3 wt+3-Cy5 Set 2: wt+15-Cy5 wt+3-Cy3 Set 2 (dye swap): wt+15-Cy3 wt+3-Cy5 Set 3: wt+15-Cy5 wt+3-Cy3 Set 3 (dye swap): wt+15-Cy3 wt+3-Cy5 GSM63409, GSM63410, GSM63411, GSM63412, GSM63413, GSM63414 Data for six replicate experiments performed with biologically independent samples. Cy3 and Cy5 dyes were used as follows: Set 1: wt+20-Cy5 wt+3-Cy3 Set 1 (dye swap): wt+20-Cy3 wt+3-Cy5 Set 2: wt+20-Cy5 wt+3-Cy3 Set 2 (dye swap): wt+20-Cy3 wt+3-Cy5 Set 3: wt+20-Cy5 wt+3-Cy3 Set 3 (dye swap): wt+20-Cy3 wt+3-Cy5 GSM63415, GSM63416, GSM63417, GSM63418, GSM63419, GSM63420
2010-07-01 | E-GEOD-2901 | biostudies-arrayexpress
Project description:High-density phage epitope microarray from 31 samples were used for unsupervised analysis (GSM36153...GSM36183). 129 samples from prostate cancer patients and controls were screened on small focused epitope chips, which contained 180 phage elements. These data were used to train GA/KNN program (GSM36184...GSM36312). 128 samples from localized prostate cancer patients and controls were screened on small focused epitope chips. These independent data were used to validate the epitomic profile (GSM36313...GSM36375, GSM40203...GSM40213, GSM40216, GSM40218, GSM40219, GSM40222, GSM40225, GSM40227, GSM40229, GSM40233, GSM40237, GSM40246...GSM40294). Three subgroups of samples were used as test sets to validate the specificity of epitomic profile (GSM36376...GSM36410, GSM40214, GSM40215, GSM40217, GSM40220, GSM40221, GSM40224, GSM40226, GSM40228, GSM40231, GSM40234...GSM40236, GSM40238...GSM40244). Project----Identification of humoral signature for prostate cancer diagnosis We constructed a prostate cancer cDNA phage display library. cDNAs were reverse-synthesized from mDNA pool isolated from prostate cancer tissues. Enzyme-digested cDNA fragments were then inserted into phage vector to make a whole prostate cancer phage expressed cDNA library. In order to select cancer specific phage epitope from this library, we performed several cycles of affinity enrichment. We used the bounded IgG pool isolated from prostate cancer patient sera to select the tumor specific phage epitope clones. Once we had the enriched phage epitope library, we cultured the phage library on LB-agar dish for individual phage colonies. About 2300 phage colonies from agar dish were picked up using toothstick and cultured in 96-well microtiter plates. Each clone was labeled as microtiter plate #, column #, row#, i.e. clone ID. These 2300 clones were then spotted on slides in single spot (no any duplicate), i.e. each spot (labeled by clone ID) represents a single phage clone. The phage epitope microarrays were then screened using cancer or control sera. We employed two color system. Cy5-anti human IgG was to detect human IgG. For green color, we used Cy3-labeled anti-phage capsid protein as internal reference to normalize the ammount difference of phage particles spotted on each spot. Thus the ratio of Cy5/Cy3 would count for the immune response in cancer or control sera. Once we identified humoral signature in prostate cancer patients, we could sequence the phage clone to characterize the nature of the genes or proteins.
2005-10-04 | E-GEOD-2010 | biostudies-arrayexpress
Project description:Duplicate hybridizations with fluorochrome switching were performed following the first two fractions of total body irradiation for accumulated doses of 1.5 Gy and 3.0 Gy. Control samples for all hybridizations were from the same patient before the beginning of treatment.
2010-06-09 | E-GEOD-1366 | biostudies-arrayexpress
Project description:As maize seedlings germinate into the soil, they encounter an environment teeming with insects seeking rich sources of nutrition. Maize presumably has developed a number of molecular mechanisms to ensure survival at the beginning of its life cycle. Bioassays indicated maize seedlings were more toxic to caterpillars than shoots from 3 or 4 leafed plants. Microarray technology was utilized to document the expression of a number of genes with potential defensive functions in seedling tissue. In addition to elevated levels of the genes involved in the biosynthesis of DIMBOA (2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one), an anti-insect resistance molecule, other highly expressed genes in the seedling encode the following putative proteins: defensin, hydroxyproline and proline-rich protein, thaumatin-like protein, lipase, cystatin, protease inhibitor, and a variety of proteases. Changes in the levels of gene expression by the microarray technology were well correlated to results using quantitative RT-PCR. The potential resistance genes identified occurred mainly on chromosomes 1 and 5 in the B73 genome. Analysis of promoters of four DIMBOA biosynthetic genes determined that a Dof transcription factor, two of which were differentially expressed in three vegetative stages, is possibly involved in regulation of the DIMBOA biosynthetic pathway. The results indicate that maize employs a wide variety of potential resistance mechanisms in seedling tissue to resist a possible insect attack. Three different Zea mays plant (B73) life stages were sampled: a) seedlings, which had grown just above the soil line, which also served as reference tissue (reference tissue was always labeled Cy5 and consisted of leaf shoots pooled from 12 plants); b) leaf shoots from plants with four leaves; and c) leaf shoots from plants with six leaves. There were 3 samples (biological replications) of each treatment (6 plants per sample) and the samples were labeled Cy3 and which were combined with a reference chip for a total of 9 chips: (3 biological replicates of seedling, 4 leaf stage and 6 leaf stage).
2011-11-03 | E-GEOD-27709 | biostudies-arrayexpress
Project description:Whole-genome microarray technology and state-of-the-art proteomic techniques were applied to provide a global and time-resolved picture of the physiological response of B. subtilis cells exposed to a severe and sudden osmotic up-shift. This combined experimental approach provided quantitative data for 3961 mRNA profiles, 590 expression profiles of proteins detected in the cytosol and 383 expression profiles of proteins detected in the membrane fraction. Our study uncovered a well-coordinated induction of gene expression subsequent to an osmotic up-shift that involves large parts of the SigB, SigW, SigM and SigX regulons and additionally osmotic up-regulation of a large number of genes that do not belong to these regulons. In total, osmotic up-regulation of about 500 B. subtilis genes was observed. Our data provide an unprecedented rich basis for further in-depth investigation on the physiological and genetic responses of B. subtilis to hyperosmotic stress. Cells were grown in a minimal medium to early exponential phase and were then exposed to a strong osmotic up-shift by the addition of 6% (w/v) NaCl. Samples were taken before and 10, 30, 60 and 120 min subsequent to the addition of NaCl. Microarray hybridizations were performed with RNA from three biological replicates. The individual samples were labeled with Cy5; a reference pool consisting of equal amounts of RNA from all 15 samples was labeled with Cy3 (common reference design). After intensity-dependent (Lowess) normalization, the ratios of duplicate spots were averaged resulting in three biologically independent expression values per gene and time point.
2010-05-19 | E-GEOD-18345 | biostudies-arrayexpress
Project description:Samples and RNA preparation: The series is composed of 4 hybridizations for analysis of differentially expressed genes in the liver of ciprofibrate dosed vs. control rats. Rats (200-250 g) were given (by gastric intubation) tap water (control group, n=5) or ciprofibrate (50 mg/kg body weight, once daily for 60 days, n=4). Liver total RNA extraction was performed first by ultracentrifugation on a cesium chloride cushion and then using TRIZOL Reagent (GIBCO BRL Life Technologies, New York, NY). Labeling and hybridization: One microgram of total RNA from the control pool (n=5), and from each ciprofibrate dosed rat was reverse transcribed and labeled with Cy3- and Cy5-attached dendrimer, respectively, using 3DNA Array Detection Submicro kit as described in the manufacturer's protocols (Genisphere, Montvale, NJ). The labeling reactions were also spiked with exogenous control RNAs (Stratagene) at expected Cy5/Cy3 ratios of 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.5, 0.33, 0.25 and 0.125 for control spike 1, 2, 3, 4, 5, 6, 8, 9, 10 and 7, respectively. The Cy3 and Cy5 labeled samples (combined volume 5 microliter) were mixed with 30 microliter of the hybridization solution containing 0.25 M NaPO4, 1 mM EDTA, 4.5% SDS, 1 x SSC, 2 x Denhardt's solution, 0.25 microliter antifade reagent (Genisphere) and 0.2 microgram mouse COT-1 DNA (GIBCO BRL Life Technologies), added onto the array and covered with 22 x 50mm cover slip. The array was assembled into humidified hybridization chamber (Corning) and hybridized at 65oC for 18 h by submerging in a water bath. Post-hybridization washes were done once in 2 x SSC, 0.2% SDS at 55oC for 15 min, then in 2 x SSC at room temperature for 15 min, and finally with 0.2 x SSC at room temperature for 15 min. Scanning, image analysis and data processing: Arrays were scanned at 10 micrometer resolution with a confocal laser scanner constructed in-house in collaboration with NEMKO (Trondheim, Norway) according to a prototype developed at NHGRI (http://www.nhgri.gov/DIR/LCG/15K(HTML/). Microarray image analysis was done with MicroArray Suite software version 2.1 with globally normalization (Scanalytics, Inc., Fairfax VA). First, spots undetected (target intensity=1) in at least one array were excluded. Then, spots with signal intensity (averages of the 4 arrays) less than 300 in both channels were removed. The fluorescence intensity ratios (Cy5/Cy3) for genes measured from probes spotted in duplicate were averaged for each slide. Then, mean ratios of the 4 hybridizations were calculated for each gene, and differentially regulated genes were determined using 1.4 fold change cutoff values (1.4 for up-regulated or 0.7 for down-regulated genes).
2010-06-05 | E-GEOD-335 | biostudies-arrayexpress