Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.

Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.

In 2017, we released GEPIA (Gene Expression Profiling Interactive Analysis) webserver to facilitate the widely used analyses based on the bulk gene expression datasets in the TCGA and the GTEx projects, providing the biologists and clinicians with a handy tool to perform comprehensive and complex data mining tasks. Recently, the deconvolution tools have led to revolutionary trends to resolve bulk RNA datasets at cell type-level resolution, interrogating the characteristics of different cell types in cancer and controlled cohorts became an important strategy to investigate the biological questions. Thus, we present GEPIA2021, a standalone extension of GEPIA, allowing users to perform multiple interactive analysis based on the deconvolution results, including cell type-level proportion comparison, correlation analysis, differential expression, and survival analysis. With GEPIA2021, experimental biologists could easily explore the large TCGA and GTEx datasets and validate their hypotheses in an enhanced resolution. GEPIA2021 is publicly accessible at http://gepia2021.cancer-pku.cn/.

ObjectiveIncreased de novo fatty acid (FA) synthesis and cholesterol biosynthesis have been independently described in many tumour types, including hepatocellular carcinoma (HCC).DesignWe investigated the functional contribution of fatty acid synthase (Fasn)-mediated de novo FA synthesis in a murine HCC model induced by loss of Pten and overexpression of c-Met (sgPten/c-Met) using liver-specific Fasn knockout mice. Expression arrays and lipidomic analysis were performed to characterise the global gene expression and lipid profiles, respectively, of sgPten/c-Met HCC from wild-type and Fasn knockout mice. Human HCC cell lines were used for in vitro studies.ResultsAblation of Fasn significantly delayed sgPten/c-Met-driven hepatocarcinogenesis in mice. However, eventually, HCC emerged in Fasn knockout mice. Comparative genomic and lipidomic analyses revealed the upregulation of genes involved in cholesterol biosynthesis, as well as decreased triglyceride levels and increased cholesterol esters, in HCC from these mice. Mechanistically, loss of Fasn promoted nuclear localisation and activation of sterol regulatory element binding protein 2 (Srebp2), which triggered cholesterogenesis. Blocking cholesterol synthesis via the dominant negative form of Srebp2 (dnSrebp2) completely prevented sgPten/c-Met-driven hepatocarcinogenesis in Fasn knockout mice. Similarly, silencing of FASN resulted in increased SREBP2 activation and hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase (HMGCR) expression in human HCC cell lines. Concomitant inhibition of FASN-mediated FA synthesis and HMGCR-driven cholesterol production was highly detrimental for HCC cell growth in culture.ConclusionOur study uncovers a novel functional crosstalk between aberrant lipogenesis and cholesterol biosynthesis pathways in hepatocarcinogenesis, whose concomitant inhibition might represent a therapeutic option for HCC.

A tough double-network (DN) ion gel composed of chemically cross-linked poly­(furfuryl methacrylate-co-methyl methacrylate) (P­(FMA-co-MMA)) and physically cross-linked poly­(vinylidene fluoride-co-hexafluoropropylene) (P­(VDF-co-HFP)) networks with 80 wt % of ionic liquid (IL) was fabricated via a one-pot method. This ion gel exhibits excellent mechanical strength and considerable ionic conductivity, which can be used as a solid gel electrolyte. Upon an adjustment of the weight ratio of P­(FMA-co-MMA) to P­(VDF-co-HFP) and the content of the cross-linker, remarkably robust DN ion gel (failure tensile stress 660 kPa, strain 268%; failure compressive stress 17 MPa, strain 85%) was obtained. The high mechanical strength is attributed to the chemical/physical interpenetrating networks. The rigid chemically cross-linked P­(FMA-co-MMA) network dissipates most of the loading energy, and the ductile physically cross-linked P­(VDF-co-HFP) network provides stretchability for the whole gel. More importantly, the P­(FMA-co-MMA) network is formed by dynamic covalent bonds that can undergo a thermally reversible reaction, giving the gel a unique and effective thermal healing capability. Furthermore, with the high content of IL, the DN ion gel possesses a high ionic conductivity of 3.3 mS cm–1 at room temperature, which is higher than those of most solid polymer electrolytes and comparable to those of commercial organic liquid electrolytes.