The overall survival rate of patients with hepatocellular carcinoma (HCC) has remained unchanged over the last several decades. Therefore, novel drugs and therapies are required for HCC treatment. Isoliquiritigenin (ISL), a natural flavonoid predominantly isolated from the traditional Chinese medicine Glycyrrhizae Radix (Licorice), has a high anticancer potential and broad application value in various cancers. Here, we aimed to investigate the anticancer role of ISL in the HCC cell line Hep3B. Functional analysis revealed that ISL inhibited the proliferation of Hep3B cells by causing G1/S cell cycle arrest in vitro. Meanwhile, the inhibitory effect of ISL on proliferation was also observed in vivo. Further analysis revealed that ISL could suppress the migration and metastasis of Hep3B cells in vitro and in vivo. Mechanistic analysis revealed that ISL inhibited cyclin D1 and up-regulated the proteins P21, P27 that negatively regulate the cell cycle. Furthermore, ISL induced apoptosis while inhibiting cell cycle transition. In addition, phosphatidylinositol 3′-kinase/protein kinase B (PI3K/AKT) signal pathway was suppressed by ISL treatment, and the epithelial marker E-cadherin was up-regulated when the mesenchymal markers Vimentin and N-cadherin were down-regulated. In brief, our findings suggest that ISL could be a promising agent for preventing HCC tumorigenesis and metastasis.

Evidences increasingly indicate the involvement of gene network rewiring in disease development and cell differentiation. With the accumulation of high-throughput gene expression data, it is now possible to infer the changes of gene networks between two different states or cell types via computational approaches. However, the distribution diversity of multi-platform gene expression data and the sparseness and high noise rate of single-cell RNA sequencing (scRNA-seq) data raise new challenges for existing differential network estimation methods. Furthermore, most existing methods are purely rely on gene expression data, and ignore the additional information provided by various existing biological knowledge. In this study, to address these challenges, we propose a general framework, named weighted joint sparse penalized D-trace model (WJSDM), to infer differential gene networks by integrating multi-platform gene expression data and multiple prior biological knowledge. Firstly, a non-paranormal graphical model is employed to tackle gene expression data with missing values. Then we propose a weighted group bridge penalty to integrate multi-platform gene expression data and various existing biological knowledge. Experiment results on synthetic data demonstrate the effectiveness of our method in inferring differential networks. We apply our method to the gene expression data of ovarian cancer and the scRNA-seq data of circulating tumor cells of prostate cancer, and infer the differential network associated with platinum resistance of ovarian cancer and anti-androgen resistance of prostate cancer. By analyzing the estimated differential networks, we find some important biological insights about the mechanisms underlying platinum resistance of ovarian cancer and anti-androgen resistance of prostate cancer.