Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression profiling of 36 types of normal human tissues to identify tissue-specific genes

ABSTRACT: We performed expression profiling of 36 types of normal human tissues and identified 2,503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by re-analyzing a large collection of published DNA microarray datasets. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage and metastasis. Twenty five total RNA specimens were purchased from Clontech (Palo Alto, CA), Ambion (Austin, TX) and Strategene (La Jolla, CA). We tried to cover as many tissue types as possible by using pooled RNA samples. In order to define breadth-of-expression (BOE) accurately at a reasonable cost, we tried to cover as many tissue types as possible by using pooled RNA samples. Each specimen represents a human organ. We used RNA samples pooled from 2 to 84 donors to avoid differences at the individual level. Keywords = Normal human tissues Keywords = Tissue-specificity Detailed sample information and Affymetrix .CEL files are available at http://www.genome.rcast.u-tokyo.ac.jp/normal/ Publication:Ge X et al., Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics. 2005 Aug;86(2):127-141. PMID: 15950434 Keywords: other. This dataset is part of the TransQST collection.

ORGANISM(S): Homo sapiens

SUBMITTER: Xijin Ge

PROVIDER: E-GEOD-2361 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues.

Ge Xijin X Yamamoto Shogo S Tsutsumi Shuichi S Midorikawa Yutaka Y Ihara Sigeo S Wang San Ming SM Aburatani Hiroyuki H

Genomics 20050801 2

A critical and difficult part of studying cancer with DNA microarrays is data interpretation. Besides the need for data analysis algorithms, integration of additional information about genes might be useful. We performed genome-wide expression profiling of 36 types of normal human tissues and identified 2503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by reanalyzing a large collection of published DNA microarray datasets. We observed that the ex ...[more]

PMID: 15950434

Similar Datasets

Project description:Chronic infections by hepatitis B virus (HBV) and hepatitis C virus (HCV) appear to be the most significant causes of hepatocellular carcinoma (HCC). Aberrant promoter methylation is known to be deeply involved in cancer, including HCC. In this study, we analyzed aberrant promoter methylation on genome-wide scale in 6 HCCs including 3 HBV-related and 3 HCV-related HCCs, 6 matched noncancerous liver tissues and 3 normal liver tissues by methylated DNA immunoprecipitation-on-chip analysis. Candidate genes with promoter methylation were detected more frequently in HCV-related HCC. Candidate genes methylated preferentially to HBV-related or HCV-related HCCs were detected and selected, and methylation levels of the selected genes were validated using 125 liver tissue samples, including 61 HCCs (28 HBV-related HCCs and 33 HCV-related HCCs) and matched 59 matched noncancerous livers, and 5 normal livers, by quantitative methylation analysis using MALDI-TOF mass spectrometry. Among analyzed genes, preferential methylation in HBV-related HCC was validated in 1 gene only. However, 15 genes were found methylated preferentially in HCV-related HCC, which was independent from age. Hierarchical clustering of HCC using these 15 genes stratified HCV-related HCC as a cluster of frequently methylated samples. The 15 genes included genes inhibitory to cancer-related signaling such as RAS/RAF/ERK and Wnt/b-catenin pathways. It was indicated that genes methylated preferentially in HCV-related HCC exist, and it was suggested that DNA methylation might play an important role in HCV-related HCC by silencing cancer-related pathway inhibitors. we analyzed aberrant promoter methylation in 6 HCC clinical samples (including 3 HBV-related HCCs and 3 HCV-related HCCs) and their matched noncancerous tissues on genome-wide scale by the method. Candidate regions of promoter methylation preferentially to HBV-related HCC and HCV-related HCC were selected, and the methylation levels of these genes were measured quantitatively using MALDI-TOF mass spectrometry. Expression levels of these 6 pairs of HCC and 4 more pairs of HCCs and surrounding noncancerous tissues were analyzed by expression array and are reported in this Series. <br><br>This experiment was reloaded in November 2010 after additional curation. this dataset is part of the TransQST collection.

Project description:Background: Glioblastoma multiforme (GBM) is the most aggressive and most lethal primary malignant brain tumor, correlated with survival rates of less than one year from the time of diagnosis. Current surgical procedure attempts to remove the bulk of the tumor mass, whereas GBM frequently recurs within 1-3cm from the primary tumor resection site. Molecular mechanisms involved in the recurrence of the tumor are still poorly understood. The aim of the study was to define the molecular signature of GBM surrounding white matter (WM) in order to better understand the molecular mechanisms involved with tumor relapse. Material & Methods: Human GBM tumor bulk and surrounding tissue (1-3cm from the border of the tumor) were obtained from five patients who underwent total tumour resection, while normal white matter was harvested from patients who underwent surgical procedure for nonmalignant pathologies. Samples were processed for hybridization on the Affymetrix Human U133A arrays and data were examined with the GeneSpring analysis software. Results: Gene expression analysis of the samples was done in 2 independent steps. First, molecular profiling comparison of GBM surrounding WM and normal WM resulted in 59 genes differentially expressed between both tissues. Among these, numerous genes expressed by mature neural cells were down-regulated in GBM surrounding WM, while gene products supporting invasion were overexpressed. Moreover, KLRC1, a specific natural killer receptor naturally involved in the activation of antitumoral cells was drastically repressed in GBM surrounding WM, suggesting that the antitumoral immune surveillance is compromised in this tissue. Second, we focused our study on genes specifically regulated in GBM periphery respectively to GBM core. The highest up-regulated gene in GBM surrounding tissue encodes for DTX4, a regulator of NOTCH signalling pathway described for its key role in maintaining neural progenitors in an uncommitted state. Conclusion: This study revealed unique molecular characteristics of GBM surrounding tissue, showing the dysregulation of genes involved in immune surveillance along with genes associated to stemness maintenance. All together, these data may help to understand the molecular mechanisms associated with GBM recurrence This study attempted to define the molecular characteristics of the GBM surrounding tissue. To this end, GBM tumor samples were obtained from 5 patients who underwent total tumor resection. Surrounding tumor mass tissue was retrieved in all cases from not infiltrated white matter sited at 2 cm from the macroscopic tumor border. Furthermore, control white matter biopsies were harvested from patients operated on for deep intracerebral cavernomas. Each sample was hybridized onto Affymetrix human U133 arrays. For each patient, tumor core sample and surrounding tissue were harvested and are identified with the same suffix number. In 2 cases, (patients 3 and 4), two tumor peripheral tissue samples were harvested and are identified with the same number followed by "R" (replicate).

Dataset Information

Expression profiling of 36 types of normal human tissues to identify tissue-specific genes

Publications

Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets