ABSTRACT
Although deubiquitinases (DUBs) have been well described in liver tumorigenesis, their potential roles and mechanisms have not been fully understood. In this study, we identified ubiquitin-specific protease 1 (USP1) as an oncogene with essential roles during hepatocellular carcinoma (HCC) progression. USP1, with elevated expression levels and clinical significance, was identified as a hub DUB for HCC in multiple bioinformatics datasets. Functionally, USP1 overexpression significantly enhanced the malignant behaviors in HCC cell lines and spheroids in vitro, as well as the zebrafish model and the xenograft model in vivo. In contrast, genetic ablation or pharmacological inhibition of USP1 dramatically impaired the phenotypes of HCC cells. Specifically, ectopic USP1 enhanced aggressive properties and metabolic reprogramming of HCC cells by modulating mitochondrial dynamics. Mechanistically, USP1 induced mitochondrial fission by enhancing phosphorylation of Drp1 at Ser616 via deubiquitination and stabilization of cyclin-dependent kinase 5 (CDK5), which could be degraded by the E3 ligase NEDD4L. The USP1/CDK5 modulatory axis was activated in HCC tissues, which was correlated with poor prognosis of HCC patients. Furthermore, Prasugrel was identified as a candidate USP1 inhibitor for targeting the phenotypes of HCC by an extensive computational study combined with experimental validations. Taken together, USP1 induced malignant phenotypes and metabolic reprogramming by modulating mitochondrial dynamics in a CDK5-mediated Drp1 phosphorylation manner, thereby deteriorating HCC progression.
ABSTRACT
MOTIVATION: Drug-food interactions (DFIs) occur when some constituents of food affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic and/or pharmacokinetic processes. Many computational methods have achieved remarkable results in link prediction tasks between biological entities, which show the potential of computational methods in discovering novel DFIs. However, there are few computational approaches that pay attention to DFI identification. This is mainly due to the lack of DFI data. In addition, food is generally made up of a variety of chemical substances. The complexity of food makes it difficult to generate accurate feature representations for food. Therefore, it is urgent to develop effective computational approaches for learning the food feature representation and predicting DFIs. RESULTS: In this article, we first collect DFI data from DrugBank and PubMed, respectively, to construct two datasets, named DrugBank-DFI and PubMed-DFI. Based on these two datasets, two DFI networks are constructed. Then, we propose a novel end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents. In topology space, we adopt a simplified graph convolution network-based method to learn the topological structure features. In feature space, we use a deep neural network to extract attribute features from the original node attributes. The evaluation results indicate that DFinder performs better than other baseline methods. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/23AIBox/23AIBox-DFinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Subject(s)
Food-Drug Interactions , Neural Networks, Computer , SoftwareABSTRACT
BACKGROUND: Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment. METHODS: We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts. RESULTS: Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database. CONCLUSIONS: This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.
