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HCCDB: A Database of Hepatocellular Carcinoma Expression Atlas / 基因组蛋白质组与生物信息学报·英文版
Genomics, Proteomics & Bioinformatics ; (4): 269-275, 2018.
Article in English | WPRIM | ID: wpr-772980
ABSTRACT
Hepatocellular carcinoma (HCC) is highly heterogeneous in nature and has been one of the most common cancer types worldwide. To ensure repeatability of identified gene expression patterns and comprehensively annotate the transcriptomes of HCC, we carefully curated 15 public HCC expression datasets that cover around 4000 clinical samples and developed the database HCCDB to serve as a one-stop online resource for exploring HCC gene expression with user-friendly interfaces. The global differential gene expression landscape of HCC was established by analyzing the consistently differentially expressed genes across multiple datasets. Moreover, a 4D metric was proposed to fully characterize the expression pattern of each gene by integrating data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). To facilitate a comprehensive understanding of gene expression patterns in HCC, HCCDB also provides links to third-party databases on drug, proteomics, and literatures, and graphically displays the results from computational analyses, including differential expression analysis, tissue-specific and tumor-specific expression analysis, survival analysis, and co-expression analysis. HCCDB is freely accessible at http//lifeome.net/database/hccdb.
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Full text: Available Index: WPRIM (Western Pacific) Main subject: Gene Expression Regulation, Neoplastic / Carcinoma, Hepatocellular / Gene Expression Profiling / Databases, Genetic / Genetics / Liver Neoplasms Limits: Humans Language: English Journal: Genomics, Proteomics & Bioinformatics Year: 2018 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Main subject: Gene Expression Regulation, Neoplastic / Carcinoma, Hepatocellular / Gene Expression Profiling / Databases, Genetic / Genetics / Liver Neoplasms Limits: Humans Language: English Journal: Genomics, Proteomics & Bioinformatics Year: 2018 Type: Article