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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Mar 07, 2011 |
| Title |
Cancer genomics identifies regulatory gene networks associated with the transition from dysplasia to adenocarcinomas |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by array
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| Summary |
Lung cancer is a leading cause of deaths in the world. There is a need to improve an understanding of mechanisms of malignant transformation and to develop genetic markers of disease for better and targeted therapies.
Here, we report findings with a transgenic disease model where targeted expression of c-raf to respiratory epithelium induced adenocarcinomas. Specifically, by use of laser microdissection we harvested either tumor or transgenic and non-transgenic but otherwise morphologically unaltered cells. We then searched genome wide for regulated genes and validated results by quantitative real-time PCR. Overall, 473 and 541 genes were significantly regulated when cancer versus transgenic and cancer versus non-transgenic cells were compared. Principal component analysis and hierarchical clustering of the data clearly separated the cancer cells from transgenic and non-transgenics and we observed predominately repression of gene expression at advanced stages of tumor growth. Nonetheless genes up-regulated in dysplasia were also up-regulated in solid tumors. We observed groups of genes acting in concert either linked to development with an unexpected high number of genes coding either for the epithelial mesenchymal or mesenchymal endothelial transition. Additionally, genes coding for cell adhesion including the integrins and the tight and gap junction proteins were repressed (integrin alpha 1, integrin alpha 8, claudin 2, claudin 5, gap junction membrane channel protein alpha 5 and cadherin 5) but ligands for the membrane bound epidermal growth factor tyrosine kinase i.e. epi- and amphiregulin were up-regulated. Moreover, molecules in the signalling of vascular endothelial growth factor receptor- 2 and VEGFD, Notch and WNT were regulated as were glycosylases that facilitate cellular recognition. Other regulated signalling molecules included exchange factor such as RAP guanine nucleotide exchange factor 3, RHO guanine nucleotide exchange factor 10, RAS guanine releasing protein 2 and 3, and RAS guanine nucleotide-releasing factor 1 that play a role in an activation of the MAP kinases.
Notably, we found the tumor suppressors MCC (mutated in colorectal cancers), HEY1 (hairy/enhancer-of-split related with YRPW motif 1), FAT3 (FAT tumor suppressor homolog 3), ARMCX1 (armadillo repeat containing, X-linked 1) and RECK (reversion-inducing-cysteine-rich protein with kazal motifs) to be significantly repressed.
Taken collectively, our study provides valuable information for new candidate genes in lung adenocarcinoma induced by exaggerted c-raf kinase activity.
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| Overall design |
SP-C/c-raf model
SP-C/c-raf transgenic mice were obtained from the laboratory of Prof. Ulf Rapp (University of Würzburg, Germany), who bred the mice in the C57BL/6/DBA/2 hybrid background. We kept the SP-C/c-raf transgenic mice in the C57BL/6 background for at least five generations.
Lung cancer samples were derived from SP-C/c-raf mice (aged 12 – 14 months); unaltered lung tissue were always isolated from the transgenic mouse (aged 5 – 7 months). Endogenous normal lung tissue was studied of non-transgenic mice (aged 7 – 10 months). The non-transgenic littermates (wild-type) served as control for transgenic effects.
Mice were sacrificed and the lung tissues were immediately frozen on dry ice and stored at -80°C until further analysis.
The histopathological diagnosis was based on routinely processed hematoxylin-eosin stains.
Microdissection (LMPC – Laser Microbeam Microdissection and Laser Pressure Catapulting)
From each frozen lung tissue 10-µm thick sections were prepared and transferred on polyethylene napthalate foil-covered slides (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany).
The sections were fixed in methanol / acetic acid and stained in hematoxylin. The desired cells were microdissected using the PALM MicroLaser systems (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany) and collected in an adhesive cap (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany). Microdissected cells were resuspended in a guanidine isothiocyanate-containing buffer (RLT buffer from RNeasy MikroKit, Qiagen, Santa Clarita, CA, USA) with 10 µl/ml β-mercaptoethanol to ensure isolation of intact RNA. Approximately an area of 6 x 106 µm2 were pooled from a specific layer of interest in the same animal and used for RNA extraction.
Following microdissection, total RNA-extraction was performed with the RNeasy Micro Kit (RNeasy MicroKit Qiagen, Santa Clarita, CA, USA) according to the manufacturer’s instruction. A standard quality control of the total RNA was performed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, USA).
cRNA labeling and hybridization to microarrays
Total RNA (median: 175ng; range: 150 – 200ng) was used to generate biotin-labeled cRNA (10 µg) by means of Message Amp aRNA Premium Amplification Kit (Ambion, Austin, TX). Quality control of cRNA was performed using a bioanalyzer (Agilent 2001 Biosizing, Agilent Technologies). Following fragmentation, labeled cRNA of each sample was hybridized to Affymetrix GeneChip® Mouse Genome 430 2.0 Arrays covering over 34.000 genes and stained according to the manufacturer's instructions.
Quantification, normalization and statistical analysis
Array data was normalized using scaling or per-chip normalization to adjust the total or average intensity of each array to be approximately the same.
Microarray chips were analyzed by the GCOS (GeneChip Operating Software) from Affymetrix with the default settings except that the target signal was set to 500 and used to generate a microarray quality control and data report.
CEL files exported from GCOS were uploaded into ArrayTrack software (National Center for Toxicological Research, U.S. FDA, Jefferson, AR, USA (NCTR/FDA)) and normalized using Total Intensity Normalization after subtracting backgrounds for data management and analysis. ArrayTrack software includes some tools common to other bioinformatics software (e.g., ANOVA, T-test and SAM).
SAM
To compare the normalized data from dysplasia, normal lung tissue from transgenic mouse, tumor cells and non-transgenic of different mice, we used the Significance Analysis of Microarrays (SAM) algorithm (ArrayTrack), which contains a sliding scale for false discovery rate (FDR) of significantly up- and down-regulated genes [204]. All data were permuted 100 cycles by using the two classes, unpaired data mode of the algorithm. As cut-off for significance an estimated FDR of 0.001 was chosen. Moreover, a cut-off for fold-change of differential expression of 2 was used.
Two clustering approaches were used to determine components of variation in the data in this study as follows.
A) Principal-component analysis (PCA) that was used to obtain a simplified visualization of entire datasets. PCA is a useful linear approach to obtain a simplified visualization of entire datasets, without losing experimental information (variance). PCA allowed the dimension of complex data to be reduced and highlights the most relevant features of a given dataset to be highlighted.
B) Hierarchical gene clustering (HCA) where the data points were organized in a phylogenetic tree in which the branch lengths represent the degree of similarity between the values.
Lists of significantly differentially expressed genes were uploaded to Ingenuity Pathways Analysis (IPA, Ingenuity Systems Inc., Redwood City, CA, USA) (www.Ingenuity.com) and functional annotation and pathway analysis was performed. IPA is a commercial, web-based interface that uses a variety of computational algorithms to identify and establish cellular networks that statistically fit the input gene list and expression values from experiments. The analysis uses a database of gene interactions culled from literature and updated every quarter of the year.
Additionally, Venn diagrams were used to examine the overlap of resulting lists of genes differentially expressed between the different sample sets.
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| Contributor(s) |
Rohrbeck A, Borlak J |
| Citation(s) |
19812696 |
| Submission date |
Jan 05, 2009 |
| Last update date |
Oct 30, 2019 |
| Contact name |
Astrid Rohrbeck |
| E-mail(s) |
rohrbeck.astrid@mh-hannover.de
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| Phone |
0511-5322807
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| Organization name |
Medizinische Hochschule Hannover
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| Department |
Department of Toxicology
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| Street address |
Carl-Neuberg-Straße 1
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| City |
Hannover |
| State/province |
Niedersachsen |
| ZIP/Postal code |
30625 |
| Country |
Germany |
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| Platforms (1) |
| GPL1261 |
[Mouse430_2] Affymetrix Mouse Genome 430 2.0 Array |
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| Samples (15)
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| Relations |
| BioProject |
PRJNA111245 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE14277_RAW.tar |
60.9 Mb |
(http)(custom) |
TAR (of CEL, CHP) |
| Processed data included within Sample table |
| Processed data provided as supplementary file |
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