LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods.

Introduction: Drug-target interaction (DTI) prediction is a key step in drug function discovery and repositioning. The emergence of large-scale heterogeneous biological networks provides an opportunity to identify drug-related target genes, which led to the development of several computational methods for DTI prediction. Methods: Considering the limitations of conventional computational methods, a novel tool named LM-DTI based on integrated information related to lncRNAs and miRNAs was proposed, which adopted the graph embedding (node2vec) and the network path score methods. First, LM-DTI innovatively constructed a heterogeneous information network containing eight networks composed of four types of nodes (drug, target, lncRNA, and miRNA). Next, the node2vec method was used to obtain feature vectors of drug as well as target nodes, and the path score vector of each drug-target pair was calculated using the DASPfind method. Finally, the feature vectors and path score vectors were merged and input into the XGBoost classifier to predict potential drug-target interactions. Results and Discussion: The 10-fold cross validations evaluate the classification accuracies of the LM-DTI. The prediction performance of LM-DTI in AUPR reached 0.96, which showed a significant improvement compared with those of conventional tools. The validity of LM-DTI has also been verified by manually searching literature and various databases. LM-DTI is scalable and computing efficient; thus representing a powerful drug relocation tool that can be accessed for free at http://www.lirmed.com:5038/lm_dti.

TLSEA: a tool for lncRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Long non-coding RNAs (lncRNAs) play an important regulatory role in gene transcription and post-transcriptional modification, and lncRNA regulatory dysfunction leads to a variety of complex human diseases. Hence, it might be beneficial to detect the underlying biological pathways and functional categories of genes that encode lncRNA. This can be carried out by using gene set enrichment analysis, which is a pervasive bioinformatic technique that has been widely used. However, accurately performing gene set enrichment analysis of lncRNAs remains a challenge. Most conventional enrichment analysis methods have not exhaustively included the rich association information among genes, which usually affects the regulatory functions of genes. Here, we developed a novel tool for lncRNA set enrichment analysis (TLSEA) to improve the accuracy of the gene functional enrichment analysis, which extracted the low-dimensional vectors of lncRNAs in two functional annotation networks with the graph representation learning method. A novel lncRNA-lncRNA association network was constructed by merging lncRNA-related heterogeneous information obtained from multiple sources with the different lncRNA-related similarity networks. In addition, the random walk with restart method was adopted to effectively expand the lncRNAs submitted by users according to the lncRNA-lncRNA association network of TLSEA. In addition, a case study of breast cancer was performed, which demonstrated that TLSEA could detect breast cancer more accurately than conventional tools. The TLSEA can be accessed freely at http://www.lirmed.com:5003/tlsea.

Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning.

MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.

[microRNA-1 gene delivery mediated by exosomes suppresses CAL-27 cell proliferation].

OBJECTIVES: This study aims to construct endogenous exosomes abundantly loaded with miR-1 and investigate the role of exosome-mediated microRNA-1 (miR-1) delivery on CAL-27 cell proliferation. METHODS: Exosomes secreted by miR-1-overexpressing HEK293 cells (miR1-EXO) were purified via ultracentrifugation and subjected to transmission electron microscopy, nanoparticle analysis, Western blot analysis, and quantitative polymerase chain reaction (qPCR). CAL-27 cells were cocultured with exosomes secreted by HEK293 cells (CON-EXO) and miR1-EXO and equivalent phosphate buffer saline. The intracellular transport of exosomes was measured by using immunofluorescence, the expression of miR-1 and its target gene MET were investigated via qPCR, CAL-27 cell proliferation was measured through MTT assay, and cell cycle state was determined by applying flow cytometry. RESULTS: Electron microscopy revealed that miR1-EXO and CON-EXO were spherical or cup-shaped with an average diameter of approximately 110 nm. The well-known exosome markers CD9, Tsg101, and Alix were enriched. The expression of miR-1 in miR1-EXO was higher than that in CON-EXO (285.80±14.33 vs 1.00±0.06, P<0.000 1). After coculture with CAL-27 cells, miR1-EXO was internalized and unloaded miR-1 into CAL-27 cells. After coculture with miR1-EXO, the expression of miR-1 in CAL-27 cells was upregulated, whereas that of MET, the target gene of miR-1, was suppressed and the proliferation of CAL-27 cells was inhibited significantly. Normal oral keratinocyte cell proliferation was negligibly affected after coculture with miR1-EXO. CONCLUSIONS: Exosomes secreted from miR1-EXO cells could load abundant miR-1. Exosomal miR-1 delivered into CAL-27 cells by using miR1-EXO suppressed the expression of MET mRNA and inhibited cell proliferation.