CCG: an integrative resource of cancer protein-coding genes and long noncoding RNAs.
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.
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.|These authors contributed equally to this article.
Mengrong
M
Liu
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.|These authors contributed equally to this article.
Yu-Cheng T
YT
Yang
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
Gang
G
Xu
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
Chang
C
Tan
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
Zhi John
ZJ
Lu
D056915:DNA Copy Number Variations; D004252:DNA Mutational Analysis; D030541:Databases, Genetic / Q000639:trends; D055808:Drug Discovery / Q000379:methods; D059472:Exome / Q000235:genetics; D020869:Gene Expression Profiling; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D058990:Molecular Targeted Therapy; D009154:Mutation; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics*; D009857:Oncogenes / Q000235:genetics*; D062085:RNA, Long Noncoding / Q000235:genetics*
D062085:RNA, Long Noncoding