9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      HCCDB: A Database of Hepatocellular Carcinoma Expression Atlas

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references29

          • Record: found
          • Abstract: found
          • Article: not found

          Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma.

          Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to develop genomic-based classification for HCC have yielded highly divergent results, indicating difficulty in identifying unified molecular anatomy. We performed a meta-analysis of gene expression profiles in data sets from eight independent patient cohorts across the world. In addition, aiming to establish the real world applicability of a classification system, we profiled 118 formalin-fixed, paraffin-embedded tissues from an additional patient cohort. A total of 603 patients were analyzed, representing the major etiologies of HCC (hepatitis B and C) collected from Western and Eastern countries. We observed three robust HCC subclasses (termed S1, S2, and S3), each correlated with clinical parameters such as tumor size, extent of cellular differentiation, and serum alpha-fetoprotein levels. An analysis of the components of the signatures indicated that S1 reflected aberrant activation of the WNT signaling pathway, S2 was characterized by proliferation as well as MYC and AKT activation, and S3 was associated with hepatocyte differentiation. Functional studies indicated that the WNT pathway activation signature characteristic of S1 tumors was not simply the result of beta-catenin mutation but rather was the result of transforming growth factor-beta activation, thus representing a new mechanism of WNT pathway activation in HCC. These experiments establish the first consensus classification framework for HCC based on gene expression profiles and highlight the power of integrating multiple data sets to define a robust molecular taxonomy of the disease.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            The Genotype-Tissue Expression (GTEx) Project.

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              EpCAM and alpha-fetoprotein expression defines novel prognostic subtypes of hepatocellular carcinoma.

              The heterogeneous nature of hepatocellular carcinoma (HCC) and the lack of appropriate biomarkers have hampered patient prognosis and treatment stratification. Recently, we have identified that a hepatic stem cell marker, epithelial cell adhesion molecule (EpCAM), may serve as an early biomarker of HCC because its expression is highly elevated in premalignant hepatic tissues and in a subset of HCC. In this study, we aimed to identify novel HCC subtypes that resemble certain stages of liver lineages by searching for EpCAM-coexpressed genes. A unique signature of EpCAM-positive HCCs was identified by cDNA microarray analysis of 40 HCC cases and validated by oligonucleotide microarray analysis of 238 independent HCC cases, which was further confirmed by immunohistochemical analysis of an additional 101 HCC cases. EpCAM-positive HCC displayed a distinct molecular signature with features of hepatic progenitor cells including the presence of known stem/progenitor markers such as cytokeratin 19, c-Kit, EpCAM, and activated Wnt-beta-catenin signaling, whereas EpCAM-negative HCC displayed genes with features of mature hepatocytes. Moreover, EpCAM-positive and EpCAM-negative HCC could be further subclassified into four groups with prognostic implication by determining the level of alpha-fetoprotein (AFP). These four subtypes displayed distinct gene expression patterns with features resembling certain stages of hepatic lineages. Taken together, we proposed an easy classification system defined by EpCAM and AFP to reveal HCC subtypes similar to hepatic cell maturation lineages, which may enable prognostic stratification and assessment of HCC patients with adjuvant therapy and provide new insights into the potential cellular origin of HCC and its activated molecular pathways.
                Bookmark

                Author and article information

                Contributors
                Journal
                Genomics Proteomics Bioinformatics
                Genomics Proteomics Bioinformatics
                Genomics, Proteomics & Bioinformatics
                Elsevier
                1672-0229
                2210-3244
                25 September 2018
                August 2018
                25 September 2018
                : 16
                : 4
                : 269-275
                Affiliations
                [1 ]MOE Key Laboratory of Bioinformatics, Beijing National Research Center for Information Science and Technology, Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China
                [2 ]International Co-operation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai 200438, China
                Author notes
                [#]

                Equal contribution.

                [a]

                ORCID: 0000-0002-5279-1989.

                [b]

                ORCID: 0000-0003-3819-9660.

                [c]

                ORCID: 0000-0001-5138-9691.

                [d]

                ORCID: 0000-0003-1368-028X.

                [e]

                ORCID: 0000-0002-3633-4307.

                [f]

                ORCID: 0000-0003-1013-147X.

                [g]

                ORCID: 0000-0002-9380-9559.

                [h]

                ORCID: 0000-0003-3968-8036.

                Article
                S1672-0229(18)30338-3
                10.1016/j.gpb.2018.07.003
                6205074
                30266410
                535fa45a-e5a5-47c8-87d5-a55d5aaf1e5a
                © 2018 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 31 March 2018
                : 9 July 2018
                : 16 July 2018
                Categories
                Database

                hepatocellular carcinoma,database,transcriptome,integrative analysis,meta-analysis

                Comments

                Comment on this article