Identify and Validate the Transcriptomic, Functional Network, and Predictive Validity of FBXL19-AS1 in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common neoplastic diseases worldwide. Available biomarkers are not sensitive enough for the diagnosis of HCC, hence seeking new biomarkers of HCC is urgent and challenging. The purpose of this study was to investigate the role of F-box and leucine-rich repeat protein 19-antisense RNA 1 (FBXL19-AS1) through a functional network and inquire into its diagnostic and prognostic value in HCC. A comprehensive strategy of genomic data mining, bioinformatics and experimental validation was used to evaluate the clinical value of FBXL19-AS1 in the diagnosis and prognosis of HCC and to identify the pathways in which FBXL19-AS1 might be involved. FBXL19-AS1 was up-regulated in HCC tissues, and its high expression was associated with TNM stage and poor prognosis of HCC patients. The combination of FBXL19-AS1 and alpha-fetoprotein (AFP) in plasma could prominently improve the diagnostic validity for HCC. FBXL19-AS1 might stabilize FBXL19 to reduce the amount of macrophage M1, and then promote the occurrence and development of HCC. Meanwhile, FBXL19-AS1 might participate in regulating HCC related pathways through FBXL19-AS1-miRNA-mRNA network. Our findings indicated that FBXL19-AS1 not only serves as a potential biomarker for HCC diagnosis and prognosis, but also might be functionally carcinogenic.

EBIC: an open source software for high-dimensional and big data analyses

Motivation In this paper, we present an open source package with the latest release of Evolutionary-based BIClustering (EBIC), a next-generation biclustering algorithm for mining genetic data. The major contribution of this paper is adding a full support for multiple graphics processing units (GPUs) support, which makes it possible to run efficiently large genomic data mining analyses. Multiple enhancements to the first release of the algorithm include integration with R and Bioconductor, and an option to exclude missing values from the analysis. Results Evolutionary-based BIClustering was applied to datasets of different sizes, including a large DNA methylation dataset with 436 444 rows. For the largest dataset we observed over 6.6-fold speedup in computation time on a cluster of eight GPUs compared to running the method on a single GPU. This proves high scalability of the method. Availability and implementation The latest version of EBIC could be downloaded from http://github.com/EpistasisLab/ebic. Installation and usage instructions are also available online. Supplementary information Supplementary data are available at Bioinformatics online.