Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1075
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3195
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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The identification of cancer genes remains a main aim of cancer research. With the advances of high-throughput sequencing technologies, thousands of novel cancer genes were identified through recurrent mutation analyses and differential expression analyses between normal tissues and tumors in large populations. Many databases were developed to document the cancer genes. However, no public database providing both cancer protein-coding genes and cancer lncRNAs is available presently. Here, we present the Catalogue of Cancer Genes (CCG) database (http://ccg.xingene.net), a catalogue of cancer genes. It includes both well-supported and candidate cancer protein-coding genes and cancer lncRNAs collected from literature search and public databases. In addition, uniform genomic aberration information (such as somatic mutation and copy number variation) and drug-gene interactions were assigned to cancer genes in the database. CCG represents an effort on integrative assembly of well-supported and candidate cancer protein-coding and long noncoding RNA genes and takes advantages of high-throughput sequencing results on large populations. With the help of CCG, users can easily access a comprehensive list of cancer genes as well as genomic aberration related with these genes. The availability of integrative information will facilitate the understanding of cancer mechanisms. In addition, drug-gene information in CCG provides a useful guide to the development of new anti-cancer drugs and selection of rational combination therapies.
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