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Hepatocyte nuclear factor 1α (HNF1α) is a liver-enriched transcription factor that is critical for the maintenance of hepatocyte function. Our previous studies have demonstrated the therapeutic effects of HNF1α on hepatic fibrosis and hepatocellular carcinoma (HCC) in animals. In this study, we created hepatocyte-specific Hnf1α knockout mice using the Cre-loxP recombination system. The knockout mice display increased fatty acid synthesis in the liver. Moreover, these mice spontaneously develop HCC through fatty liver without cirrhosis. Inflammatory cytokines, such as tumor necrosis factor α and IL-6, are upregulated and accompanied by increased phosphorylation of Akt, p-65 and STAT3 in the livers of HNF1α knockout mice. Our findings suggest that HNF1α plays a crucial role in hepatocyte lipid metabolism and hepatocarcinogenesis.

Drug–target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665).

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