CRISPR-DO is available at http://cistrome.org/crispr/ CONTACT: qiliu@tongji.edu.cn or hanxu@jimmy.harvard.edu or xsliu@jimmy.harvard.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Hepatocellular carcinoma (HCC) is a common solid tumor worldwide with a poor prognosis. Accumulating evidence has implicated important regulatory roles of epigenetic modifications in the occurrence and progression of HCC. In the present study, we analyzed 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) levels in the tumor tissues and paired adjacent peritumor tissues (APTs) from four individual HCC patients using a (hydroxy)methylated DNA immunoprecipitation approach combined with deep sequencing [(h)MeDIP-Seq]. Bioinformatics analysis revealed that the 5-mC levels in the promoter regions of 2796 genes and the 5-hmC levels in 507 genes differed significantly between HCC tissues and APTs. These differential genes were grouped into various clusters and pathways and found to be particularly enriched in the 'metabolic pathways' that include 'Glycolysis/gluconeogenesis', 'Oxidative phosphorylation' and 'Citrate cycle (TCA cycle)', implicating a potential role of metabolic alterations in HCC. Furthermore, 144 genes had both 5-mC and 5-hmC changes in HCC patients, and 10 of them (PCNA, MDM2, STAG1, E2F4, FGF4, FGF19, RHOBTB2, UBE2QL1, DCN and HSP90AA1) were enriched and interconnected in five pathways including the 'Cell cycle', 'Pathway in cancer', 'Ubiquitin mediated proteolysis', 'Melanoma' and 'Prostate cancer' pathways. The genome-wide mapping of 5-mC and 5-hmC in HCC tissues and APTs indicated that both 5-mC and 5-hmC epigenetic modifications play important roles in the regulation of HCC, and there may be some interconnections between them. Taken together, in the present study we conducted the first genome-wide mapping of DNA methylation combined with hydroxymethylation in HBV-related HCC and provided a series of potential novel epigenetic biomarkers for HCC.

Background & ObjectiveGenome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need.ResultscanEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis.ConclusionAt present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets.AvailabilityThe canEvolve web portal is available at http://www.canevolve.org/.

BackgroundHepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignancies worldwide. Studies seeking to advance the overall understanding of lncRNA profiling in HCC remain rare.MethodsThe transcriptomic profiling of 12 HCC tissues and paired adjacent normal tissues was determined using high-throughput RNA sequencing. Fifty differentially expressed mRNAs (DEGs) and lncRNAs (DELs) were validated in 21 paired HCC tissues via quantitative real-time PCR. The correlation between the expression of DELs and various clinicopathological characteristics was analyzed using Student’s t-test or linear regression. Co-expression networks between DEGs and DELs were constructed through Pearson correlation co-efficient and enrichment analysis. Validation of DELs’ functions including proliferation and migration was performed via loss-of-function RNAi assays.ResultsIn this study, we identified 439 DEGs and 214 DELs, respectively, in HCC. Furthermore, we revealed that multiple DELs, including NONHSAT003823, NONHSAT056213, NONHSAT015386 and especially NONHSAT122051, were remarkably correlated with tumor cell differentiation, portal vein tumor thrombosis, and serum or tissue alpha fetoprotein levels. In addition, the co-expression network analysis between DEGs and DELs showed that DELs were involved with metabolic, cell cycle, chemical carcinogenesis, and complement and coagulation cascade-related pathways. The silencing of the endogenous level of NONHSAT122051 or NONHSAT003826 could significantly attenuate the mobility of both SK-HEP-1 and SMMC-7721 HCC cells.ConclusionThese findings not only add knowledge to the understanding of genome-wide transcriptional evaluation of HCC but also provide promising targets for the future diagnosis and treatment of HCC.

Cancer is known to have abundant copy number alterations (CNAs) that greatly contribute to its pathogenesis and progression. Investigation of CNA regions could potentially help identify oncogenes and tumor suppressor genes and infer cancer mechanisms. Although single-nucleotide polymorphism (SNP) arrays have strengthened our ability to identify CNAs with unprecedented resolution, a comprehensive collection of CNA information from SNP array data is still lacking. We developed a web-based CaSNP (http://cistrome.dfci.harvard.edu/CaSNP/) database for storing and interrogating quantitative CNA data, which curated ∼11 500 SNP arrays on 34 different cancer types in 104 studies. With a user input of region or gene of interest, CaSNP will return the CNA information summarizing the frequencies of gain/loss and averaged copy number for each study, and provide links to download the data or visualize it in UCSC Genome Browser. CaSNP also displays the heatmap showing copy numbers estimated at each SNP marker around the query region across all studies for a more comprehensive visualization. Finally, we used CaSNP to study the CNA of protein-coding genes as well as LincRNA genes across all cancer SNP arrays, and found putative regions harboring novel oncogenes and tumor suppressors. In summary, CaSNP is a useful tool for cancer CNA association studies, with the potential to facilitate both basic science and translational research on cancer.