Title: LCLE: a web portal for lncRNA network analysis in liver cancer

Authors: Xiuquan Wang; Keli Xu; Junqing Wang; Yunyun Zhou

Addresses: Department of Mathematics and Computer Science, Natural Science Division, Tougaloo College, 500 W. County Line Rd, Jackson, MS, 39174, USA ' Department of Neurobiology and Anatomical Sciences, University of Mississippi Medical Center, Jackson, Mississippi, 39216, USA ' Department of Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 20025, China ' Department of Data Science, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, Mississippi, 39216, USA

Abstract: Most of the currently available co-expression network analysis method only can capture linear correlation among genes; however, ignore the non-linear dependent correlations. Accurately and easily getting the distance values among genes are of significant importance in clustering genes which are shared in the same biological functions. We developed an online tool, lncRNA explorer (LCLE), which is able to systematically analyse gene expression data to identify more comprehensive relationships among lncRNAs and proteincoding genes (PCGs) from five different distances metrics. Our simulation results demonstrated that the selection of an appropriate distance method could help to identify novel important genes from networks. LCLE allows users to visualise figures, and download tables analysed from publically available RNAseq data such as The Cancer Genome Atlas (TCGA) and genotype-tissue expression (GTEx) or upload their own data for analysis. Overall, our web portal will benefit for biologists or clinicians without programming background in identifying novel co-regulation relations for lncRNAs and PCGs.

Keywords: adjacency matrix; network analysis; correlation; non-coding RNA; cancer; liver cancer; lncRNA; gene expression; non-linear distance.

DOI: 10.1504/IJCBDD.2020.113855

International Journal of Computational Biology and Drug Design, 2020 Vol.13 No.5/6, pp.520 - 528

Received: 27 Sep 2019
Accepted: 28 Apr 2020
Published online: 31 Mar 2021 *

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