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There are nearly 2.5 million cancer-related publications in MEDLINE as of December 2009, a= nd this number is rapidly increasing. Scientists cannot manually identify a= ll known cancer genes, and it is even more difficult to uncover the relatio= nships between these genes and various human cancers, for example. In theor= y, one could exhaustively search PubMed and compile a list of the genes rel= ated to a given disease or compound, but this would take many weeks, and it= is highly likely that such a manual search would still miss some genes and= relationships. The National Cancer Institute (NCI) recognized that a publi= cly-available resource that combined these gene-disease and gene-compound d= ata with relevant annotations would greatly facilitate research, and as par= t of its caBIG=C2=AE initiative, it created the Cancer Gene Index = Project.
The goal of the Cancer Gene Index is to further translational cancer res= earch by providing a high quality data resource consisting of genes that ha= ve been experimentally associated with human cancer diseases and/or pharmac= ological compounds, the evidence of these associations, and relevant annota= tions on the data. Thus, scientists can use the data resource to quickly di= scover and evaluate all of the genes associated with a disease, all of the = genes associated with a compound, or all of the diseases and compounds asso= ciated with a gene. This extremely valuable resource was created through a = unique process that coupled automated linguistic text analysis of millions = of MEDLINE abstr= acts with manual validation and annotation of the extracted data by expert = human curators. Details on this process are found in the section Creation of the Cancer= Gene Index.
The Cancer Gene Index includes data on 6,955 unique human genes, nearly = 12,000 NCI Thesaurus cancer disease terms, and 2,180 unique pharmacological= compounds from the NCI Thesaurus. The gene-disease and gene-compound assoc= iations were extracted from over 92 million analyzed sentences of nearly 20= million abstracts. The resource was last updated in June, 2009.
The Cancer Gene Index is available as computer-readable Gene-Disease and= Gene-Compound data files. To effectively use these files, you must be a bi= oinformaticist or computer programmer-scientist, or you must collaborate wi= th someone who has this expertise. Ideally, intuitive graphical user interf= aces (GUIs) would allow all scientists to quickly and easily access these d= ata and exploit the full power of the Cancer Gene Index. Already, geWorkbench and the Cancer Molecular An= alysis Portal both allow end users to view some Cancer Gene Index data.=
Several caBIO interfaces, on the other hand, expose al= l of the Cancer Gene Index data, and these can give you an appreciation for= the full potential of the data resource. Many of these interfaces, however= , require more experience with computer programming than the average bench = scientist may have. Data within caBIO may be programmatically accessed thro= ugh a variety of Application Programming Interfaces (APIs). The caBIO GUIs = include the caBIO Portlet Templated Search, the caBIO Home Page, and Simple Sea= rch of the caBIO Portlet on the caGrid Portal.These caBIO GUIs are simi= lar to PubMed in that queries will retrieve many results that you must sift= through, examining each to determine whether or not it is useful. Unlike P= ubMed, caBIO is much more likely to return the information that you want.= p>
The following section will help you select the best means to access Canc= er Gene Index data based on your experience with bioinformatics and compute= r programming.
You should use the step-by-step guide for the caBIO Portlet Templated Search tool. All that is required to access this web-based GUI is a computer with an = internet connection and a web browser. Although it is easy to uncover gene-= disease and gene-compound associations with this tool, it does not allow yo= u to limit your search results and thus can return genes-disease and gene-c= ompound associations that were not validated by human curators. Also, it do= es not necessarily return all of the data you would like. Because of these = issues, you must use this tool in conjunction with the
You can use the step-by-step guide to the caBIO Home Page, which has the Freesty= le Lexical Mine and Search for Biological Entities tools. Although these in= terfaces expose the entirety of the Cancer Gene Index and the rest of caBIO= , they require knowledge of the caBIO object model. All that is required to= access these web-based caBIO search tools is a computer with an internet c= onnection and a web browser.
caBIO Portlet Simple Search
The caBIO Portlet also has a Simple Search tool, whic= h provides an overview of the data in caBIO in a way that does not require = knowledge of the caBIO object model and can be useful for a quick look of t= he kinds of data within caBIO. Because the Simple Search tool does not allo= w you to differentiate Cancer Gene Index data from other data in caBIO, it = is recommended that you instead use the XML, caBIO Portlet Templated Search, or even the caBIO Home Pag= e. In the event that you would prefer to use the Simple Search a guide is provi= ded.
Scientists On the Go
If you would like to view Cancer Gene Index data on the go, you can use = the caBIO iPhone Application. A limited guide to accessing Cancer Gene Inde= x data is provided here.
The Cancer Gene Index can facilitate many different types of cancer rese= arch. Two examples are given below.
In this first example from the Cancer Gene Index Project poster, the data resource = is used to validate colon cancer translational medicine research data. Here= , scientists have obtained access to deidentified demographic data, histopa= thology data (lymph node [ICR:pN], tumor size [ICR:pT], and degree of metas= tasis [ICR:G]), and tumor tissue biospecimens from patients, which are repr= esented by gray figures. The scientists perform gene expression microarrays= on each colon cancer biospecimen (pink and red colon tissue cells). The ge= nes (purple DNA fragments) with significantly altered expression are valida= ted by cross-referencing the Cancer Gene Index.
The Cancer Gene Index also may be used for lymphoma biomarker discovery.= This example from the Cancer Gene Index Project poster illustrates that researchers c= an use the data resource to quickly identify the genes (purple DNA fragment= s) that are associated with and may be biomarkers for Lymphoma. Here, gene-= disease concept pair associations are shown as blue "to diseases" arrows. B= y searching the Cancer Gene Index for therapeutic compounds that are associ= ated with these genes, scientists easily uncover which of these candidate d= isease biomarkers are also associated with lymphoma-related compounds. An a= ssociation between the gene encoding SPN, also known as sialophorin or CD43= , and the compound leflunomide is represented by a black "has validated ass= ociation with" arrow. Cancer Gene Index data can be cross-referenced to oth= er resources, such as the clinical trial protocol database Physician Data Query=C2=AE (PDQ) to obtain information about trials th= at link these data.