MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.

Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism.

MOTIVATION: Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. RESULTS: In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. AVAILABILITY AND IMPLEMENTATION: https://github.com/XuLew/MIDTI.

The role of CXCL10 in prognosis of patients with colon cancer and tumor microenvironment remodeling.

BACKGROUNG: Tumor microenvironment (TME) has gradually emerged as an important research topic in the fight against cancer. The immune system is a major contributing factor in TME, and investigations have revealed that tumors are partially infiltrated with numerous immune cell subsets. METHOD: We obtained transcriptome RNA-seq data from the the Cancer Genome Atlas databases for 521 patients with colon adenocarcinoma (COAD). ESTIMATE algorithms are then used to estimate the fraction of stromal and immune cells in COAD samples. RESULT: A total of 1109 stromal-immune score-related differentially expressed genes were identified and used to generate a high-confidence protein-protein interaction network and univariate COX regression analysis. C-X-C motif chemokine 10 (CXCL10) was identified as the core gene by intersection analysis of data from protein-protein interaction network and univariate COX regression analysis. Then, for CXCL10, we performed gene set enrichment analysis, survival analysis and clinical analysis, and we used CIBERSORT algorithms to estimate the proportion of tumor-infiltrating immune cells in COAD samples. CONCLUSION: We discovered that CXCL10 levels could be effective for predicting the prognosis of COAD patients as well as a clue that the status of TME is transitioning from immunological to metabolic activity, which provided additional information for COAD therapies.

TIAM2 enhances non-small cell lung cancer cell invasion and motility.

BACKGROUND: TIAM2, a Rac guanine nucleotide exchange factor, is closely associated with cell adherence and migration. Here, we aimed to investigate the role of TIAM2 in non-small cell lung cancer (NSCLC) cells. MATERIALS AND METHODS: A small interference RNA (siRNA) was introduced to silence the expression of TIAM2. Invasion and motility assays were then performed to assess the invasion and motility potential of NSCLC cells. GST-pull down assays were used to detect activation of Rac1. RESULTS: TIAM2 was highly expressed in NSCLC cells. Knockdown of TIAM2 inhibited the invasion and motility, and suppressed activation of Rac1. Further experiments demonstrated that knockdown of TIAM2 could up-regulate the expression of E-cadherin, and down- regulate the expression of MMP-3, Twist and Snail. CONCLUSIONS: Our data suggest that TIAM2 can promote invasion and motility of NSCLC cells. Activation of Rac1 and regulation of some EMT/invasion-related genes may be involved in the underlying processes.