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CanGEM

http://www.cangem.org/

Description :
Gene copy number changes in cancer

CTDatabase

http://www.cta.lncc.br/

Description :
Cancer-Testis Antigens database

DDOC

http://apps.sanbi.ac.za/ddoc/

Description :
Functional context of genes implicated in ovarian cancer

EHCO

http://ehco.iis.sinica.edu.tw/

Description :
The significant advances in microarray and proteomics analyses has resulted in an exponential increase in potential new targets and has promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment. EHCO can be freely accessed through http://ehco.iis.sinica.edu.tw.

Aknowledgement :
This project was supported in part by grants from the National Health Research Institutes and National Science Council (Taiwan) (NSC95-2320-B-400-009-MY3) to C. F. Huang, and by the National Research Program for Genomic Medicine (NRPGM), National Science Council (Taiwan) (NSC95-3112-B-011-013-Y, Advanced Bioinformatics Core) to C. Hsu.

References :
1. Hsu CN, Lai JM, Liu CH, Tseng HH, Lin CY, Lin KT, Yeh HH, Sung TY, Hsu WL, Su LJ, Lee SA, Chen CH, Lee GC, Lee DT, Shiue YL, Yeh CW, Chang CH, Kao CY, Huang CY. (2007) Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online). BMC Bioinformatics. 2007, 8:66.

MethyCancer

http://methycancer.genomics.org.cn/

Description :
Links between DNA methylation levels and cancer

MoKCa

http://strubiol.icr.ac.uk/extra/mokca/

Description :
Mutations of Kinases in Cancer

PubMeth

http://matrix.ugent.be/pubmeth/

Description :
Links between DNA methylation levels and cancer

Tumor Associated Gene database

http://www.binfo.ncku.edu.tw/TAG/GeneDoc.php

Description :
The completion of human genome sequence has advanced a gigantic step toward a new era of biomedical research. Though the sequence itself is not the key for many currently unanswered questions related to human diseases, it sheds light on potential genetic solution of these diseases. We believe that the available annotations including physical characterization and functional protein domains of known tumor-related genes can be used to study the mechanisms underlying tumorigenesis. The tumor-associated gene (TAG; http://www.binfo.ncku.edu.tw/TAG) database was designed to utilize information from well-characterized oncogenes and tumor suppressor genes to facilitate cancer research. All target genes were identified through text-mining approach from the PubMed database. A semi-automatic information retrieving engine was built to collect specific information of these target genes from various resources and store in the TAG database. The information are arranged into five tables including ¡§Gene content¡¨, ¡§Protein domain/family¡¨, ¡§Gene Ontology¡¨, ¡§Disorder & Mutation¡¨, and ¡§Cross reference¡¨. At current stage, TAG database contains information for 198 oncogenes and 167 tumor suppressor genes that were known to be involved in the tumorigenesis in at least one cancer type. In addition, 151 genes that are known to be involved in oncogenesis but the genes actions have not been classified were also collected in the TAG database. We have designed user-friendly web interfaces that provide functions in searching and analysis of TAG information. Furthermore, we categorized all TAG domains and generate a weight matrix table to provide additional analysis in "oncogenic potential" for any customer-provided protein. The TAG is an integrated database that collected only well studied gene in related to oncogenesis. Beside to provide comprehensive information of well-known tumor-related genes to promote biomedical studies, our final goal is to identify novel TAG and study their role in tumorigenesis using a more systemic approach.

Aknowledgement :
This study was supported by grant of National research program for genomic medicine (NRPGM) from National Science Council, Taiwan

HPTAA

http://www.bioinfo.org.cn/hptaa/

Description :
HPTAA is a database of potential tumor-associated antigens that uses expression data from various expression platforms, including carefully chosen publicly available microarray expression data, GEO SAGE data and Unigene expression data.

Recent develoments :
A new database has been developed that covers expression information for deregulated miRNAs in human cancers. To date, it contains 345 human miRNAs expressed in 9 different cancer tissues, and 28 cell lines.

Atlas of Genetics and Cytogenetics in Oncology and Haematology

http://atlasgeneticsoncology.org/

Description :
The Atlas of Genetics and Cytogenetics in Oncology and Haematology http://atlasgeneticsoncology.org/ contains concise and updated cards on cancer related genes, chromosomal abnormalities, cancers, and cancer-prone diseases, a portal towards genetics/cancer, and teaching materials in genetics (1-3). This database is made for and by researchers and clinicians, who are encouraged to contribute. Contributions are reviewed before acceptance.

Database Structure
Cards: well structured papers which represent the body of the Atlas. Cards on genes include data on: DNA/RNA, protein, mutations, and diseases. Cards on leukemias and solid tumours include data on: clinics, cytogenetics, genes, hybrid gene and fusion protein. Cards on cancer-prone diseases include data on: inheritance mode, clinics, neoplastic risk, cytogenetics, genes and proteins, mutations. Cards also include hyperlinks towards Medline, and towards the main complementary databases (nomenclature, cartography, gene structure, transcripts, proteins, domain families, diseases, mutations, probes). Pages classified by chromosome present genes and diseases and also point towards external resources.
Deep Insights/Case Reports: Deep insights are review articles. The Case Reports Section is dedicated to rare cytogenetic entities of leukemia to document these poorly known entities, to further delineate their epidemiology, including the associated prognosis: the Atlas intend to provide new information in cancer epidemiology.
Portal: towards Internet databases devoted to genetics and/or cancer, and towards 100 journals, with specific pages on both the lattest issue and the archives, with indications on free access when available. Teaching materials in Genetics are being developed in English, French, German, Portuguese, and Spanish, and we are (still) looking for an Italian translation. We are happy that some Universities already use the Atlas for teaching; they can also provide us with more authors.

Comments
The Atlas is a peer reviewed on-line journal and database indexed by the Current Contents. More than a 260 collaborators are contributing and more than 600 papers are available. More than 28 000 individual machines connect each month. The Atlas is part of the genome project and participates in the research in cancer epidemiology. The Atlas is at the crossroads of research, university and post-university teaching (virtual medical university), and telemedicine. It contributes to meta-medicine, this mediation, using new information technology, between the overflowing information provided by the scientific community and the individual practitioner. Contributions and collaborations are MOST welcome. It will serve the entire community of clinicians, researchers, and students

Aknowledgement :
The Atlas is funded by: Ministère de la Recherche, Ministère de l’ Education Nationale, Département de la Vienne, Communauté d’ Agglomération de Poitiers, Ligue Nationale contre le Cancer (Charente, Cher, Corrèze, Indre, et Poitou-Charentes), and benefits from INFOBIOGEN resources.

References :

  1. Huret,J.L., Dessen,P. and Bernheim,A. (2001) Atlas of Genetics and Cytogenetics in Oncology and Haematology, Updated. Nucleic Acids Res., 29, 303-304.
  2. Kaiser,J. (2001) Fingerprinting a killer. Science 292, 1803. http://www.sciencemag.org/cgi/content/summary/292/5523/1803b
  3. Pearson,H. (2001) Lifelines: browsing the cancer catalogue. Nature Science Update 30 May 2001. http://www.nature.com/nsu/010531/010531-8.html#

CGED - Cancer Gene Expression Database

http://love2.aist-nara.ac.jp/CGED

Description :
CGED (Cancer Gene Expression Database) is a database containing gene expression profiles and accompanying clinical information. The data in CGED have been obtained through collaborative efforts made at the Nara Institute of Science and Technology and Osaka University School of Medicine to identify genes of clinical importance. The expression data have been obtained by a high-throughput RT-PCR technique (adaptor-tagged competitive PCR) (1), and patients were recruited mainly from a single hospital. The quality of the data has been demonstrated by successful identification of diagnostic genes (2-3). In CGED, the expression and clinical data are presented in a way useful for scientists interested in specific genes or biological functions. The data can be retrieved either using gene identifiers or by functional categories defined by Gene Ontology terms or the SwissProt annotation. Gene expression data are displayed in mosaic plots, and expression patterns of multiple genes, selected by names or similarity search of the patterns, can be compared. The sorting function enables users for easy recognition of relationships between gene expression and clinical parameters. Currently, data on breast, colorectal, and hepatocellular cancers are available.

Recent develoments :
Data analysis of esophageal (squamous cell carcinoma) and thyroid cancers is now ongoing. In addition, expression data acquisition for lung cancer and glioma will be finished within the fiscal year 2003. These data will be deposited in CGED after completion of the data analysis. Some of the patients recruited for these studies are being clinically followed, and the clinical information will be regularly updated.

Aknowledgement :
This work was partly supported by a Grant-in-Aid for Scientific Research on Priority Areas "Genome Information Science" from the Ministry of Education, Culture, Sports, Science and Technology of Japan.

References :
1. Kato, K. (1997) Adaptor-tagged competitive PCR: a novel method for measuring relative gene expression. Nucleic Acids Res. 25, 4694-4696.
2. Iwao, K., Matoba, R., Ueno, N., Ando, A., Miyoshi, Y., Matsubara, K., Noguchi, S. and Kato, K. (2002) Molecular classification of primary breast tumors possessing distinct prognostic properties. Hum. Mol. Genet. 11, 199-206.
3. Muro, S., Takemasa, I., Oba, S., Matoba, R., Ueno, N., Maruyama, M., Yamashita, R., Sekimoto, M., Yamamoto, H., Nakamori, S., Monden, M., Ishii, S., and Kato, K. (2003) Identification of expressed genes linked to the malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data. Genome Biology 4, R21.
4. Kurokawa, Y., Matoba, R., Takemasa, I., Nakamori, S., Tsujie, M., Nagano, H., Dono, K., Umeshita, K., Sakon, M., Ueno, N., Kita, H., Oba, S., Ishii, S., Kato, K. and Monden, M. Molecular features of non-B, non-C hepatocellular carcinoma: a PCR-array gene expression profiling study. J. Hepatol., in press.

COSMIC - Catalogue Of Somatic Mutations In Cancer

http://www.sanger.ac.uk/perl/CGP/cosmic

Description :
Sequence data, samples and publications

Database of Germline p53 Mutations

http://www.lf2.cuni.cz/win/projects/germline_mut_p53.htm

Description :
Somatic mutations in the p53 tumour suppressor gene are found in many human cancers. In addition, germline p53 mutations have been identified in individuals from cancer-prone families and in isolated cancer patients affected at a young age or suffering from multiple tumours. A large fraction of the cancer-prone families with germline p53 mutation conform to the criteria of Li-Fraumeni syndrome (LFS), a rare familial autosomal dominant cancer syndrome characterised by early-onset sarcomas, brain tumours, premenopausal breast cancer, leukaemias and adrenocortical tumours. Individuals carrying germline p53 mutations have a very broad spectrum of clinical manifestations in terms of penetrance, tumour type, tumour location and age of onset. The collection of a large set of data is thus necessary to establish possible correlations between the type and location of a germline p53 mutation and its phenotypic consequences. Such genotype-phenotype correlations may in turn improve the counselling and preventive approaches in the affected families. Reports of germline p53 mutations have accumulated rapidly since 1990, when their association with LFS and increased cancer susceptibility was made. Because the currently available databases of p53 gene mutations either exclude germline mutations or contain incomplete data, we created a comprehensive database of those cases of germline p53 mutations for which sufficient detail is given in the literature. In addition to listing all mutations, the database includes detailed information about the families, affected individuals and their tumours. It therefore provides a powerful means for drawing correlations between various aspects of germline p53 mutations. The database describes each p53 mutation (type of the mutation, exon and codon affected by the mutation, nucleotide and amino acid change), each family (family history of cancer, diagnosis of LFS), each affected individual (sex, generation, p53 status, from which parent the mutation was inherited) and each tumour (type, age of onset, p53 status (loss of heterozygosity and immunostaining)). Each entry contains the original reference(s). Individuals affected by cancer who were experimentally shown not to carry a germline p53 mutation are not listed. Affected individuals belonging to a branch of the pedigree where a germline p53 mutation was excluded (phenocopies) are also not included. The current (August 1999) version of the database lists 697 tumours from 542 individuals belonging to 141 independent pedigrees with germline p53 mutations. The database is updated every four months. It is freely available and can be accessed via the World Wide Web at http://www.lf2.cuni.cz/homepage.htm. It is in Excel 97 format and can be loaded as Excel file or tab delimited text file. The legend to the database can be loaded as Word 97 file or plain text file.

References :
Sedlacek Z., Kodet R., Poustka A., Goetz P. (1998) A database of germline p53 mutations in cancer-prone families. Nucleic Acids Res. 26: 214-215

IARC TP53 Database

http://www.iarc.fr/p53/

Description :
Somatic mutations in the tumor suppressor gene TP53 are frequent in most human cancers and germline TP53 mutations are associated with a rare cancer-prone syndrome, the Li-Fraumeni syndrome. Over the past ten years, several databases of TP53 mutations have been developed. The most extensive of these databases is maintained and developed at the International Agency for Research on Cancer. The IARC TP53 Database (http://www.iarc.fr/p53) compiles data on human somatic and germline TP53 genetic variations that are reported in the peer-reviewed literature. With over 18,500 somatic and 225 germline mutations and 1,000 citations in the world literature, this database is now recognized as a major source of information on TP53 mutation patterns in human cancer. It can be searched and analyzed online and is useful to draw hypotheses on the nature of the molecular events involved in TP53 mutagenesis and on the natural history of cancer. The database is available at http://www.iarc.fr/P53/ .

Recent develoments :
Recent developments include:
- restructuring of the database which is now patient-centered, with more detailed annotations on the patient (carcinogen exposure, virus infection, genetic background).
- data on mutation prevalence (R6 update) and on clinical outcome (next update).
- an online search system that allows the online analysis of somatic mutation data (available through the 'database search' option). This ASP (Active Server Pages) application allows the identification and selection of specific sets of data according to user's queries and produces graphical outputs (histograms and pie charts) of mutation patterns, codon distribution and tumor spectrum. A search of reference criteria (author name, PubMed entry, title, etc...) allows the analysis of mutation data by individual publication to generate graphs and figures.
- the entire dataset, or sets of data selected according to the user's queries, can be downloaded as tab-delimited text files, in a compressed format.
- a comprehensive user guide is available online as well as a slide-show on TP53 mutations, database structure/content and examples of mutation analysis.

Aknowledgement :
The project is funded by IARC and supported by the European Community (contract: QLG1-1999-00273).

References :
1. Olivier M, Eeles R, Hollstein M, Khan MA, Harris C.C, Hainaut P. The IARC TP53 Database: new online mutation analysis and recommendations to users. Hum Mutat 2002 Jun;19(6):607-14
2. Hernandez-Boussard T, Montesano R, Hainaut P. Sources of bias in the detection and reporting of p53 mutations in human cancer: analysis of the IARC p53 mutation database. Genet Anal. 1999;14(5-6):229-33.

ITTACA

http://bioinfo.curie.fr/ittaca

Description :
ITTACA centralizes public datasets containing both microarray gene expression and clinical data of tumors. ITTACA currently focuses on breast carcinoma, bladder carcinoma, and uveal melanoma. A web interface allows users to carry out different class comparison analyses, including the comparison of expression distribution profiles, tests for differential expression, and patient survival analyses.

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