DMFMDA: Prediction of Microbe-Disease Associations Based on Deep Matrix Factorization Using Bayesian Personalized Ranking

Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. First, the disease one-hot encoding is fed into neural network, which is transformed into a low-dimensional dense vector in implicit semantic space via embedding layer, and so is microbe. Then, matrix factorization is realized by neural network with embedding layer. Furthermore, our model synthesizes the non-linear modeling advantages of multi-layer perceptron based on the linear modeling advantages of matrix factorization. Finally, different from other methods using square error loss function, Bayesian Personalized Ranking optimizes the model from a ranking perspective to obtain the optimal model parameters, which makes full use of the unobserved data. Experiments show that DMFMDA reaches average AUCs of 0.9091 and 0.9103 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-state-of-art methods. In case studies, 10, 9 and 9 out of top-10 candidate microbes are verified by recently published literature for asthma, inflammatory bowel disease and colon cancer, respectively. In conclusion, DMFMDA is successful application of deep learning in the prediction of microbe-disease association.

Identifying lncRNA and mRNA Co-Expression Modules from Matched Expression Data in Ovarian Cancer

Long non-coding RNAs (lncRNAs) have been shown to be involved in multiple biological processes and play critical roles in tumorigenesis. Numerous lncRNAs have been discovered in diverse species, but the functions of most lncRNAs still remain unclear. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood. With the advances of high-throughput technologies, the increasing availability of genomic data creates opportunities for deciphering the molecular mechanism and underlying pathogenesis of human diseases. Here, we develop an integrative framework called JONMF to identify lncRNA-mRNA co-expression modules based on the sample-matched lncRNA and mRNA expression profiles. We formulate the module detection task as an optimization problem with joint orthogonal non-negative matrix factorization that could effectively prevent multicollinearity and produce a good modularity interpretation. The constructed lncRNA-mRNA co-expression network and the gene-gene interaction network are used as the network-regularized constraints to improve the module accuracy, while the sparsity constraints are simultaneously utilized to achieve modular sparse solutions. We applied JONMF to human ovarian cancer dataset and the experiment results demonstrate that the proposed method can effectively discover biologically functional co-expression modules, which may provide insights into the function of lncRNAs and molecular mechanism of human diseases.

The Lnc LINC00461/miR-30a-5p facilitates progression and malignancy in non-small cell lung cancer via regulating ZEB2

Studies have found that Lnc LINC00461 is an important regulator of cancer. However, the function of Lnc LINC00461 in NSCLC is not known. Therefore, this experimental design was based on Lnc LINC00461 to explore the pathogenesis of Non-small cell lung cancer (NSCLC). RT-qPCR was used to detect the expression of lnc LINC00461 and miR-30a-5p in NSCLC. The CCK-8 method and Transwell assay were used to detect the effects of lnc LINC00461 and miR-30a-5p on proliferation, migration in NSCLC. Target gene prediction and screening, luciferase reporter assays were used to validate downstream target genes of lnc LINC00461 and miR-30a-5p. The protein expression of ZEB2 was detected by Western blot. The tumor changes in mice were detected by in vivo experiments. Lnc LINC00461 was significantly elevated in NSCLC. Lnc LINC00461 knockdown significantly inhibited proliferation and migration in NSCLC. miR-30a-5p was a direct target of lnc LINC00461 and miR-30a-5p was significantly reduced in NSCLC. shLINC00461 and miR-30a-5p inhibitor partially eliminated the effect of shLINC00461 on cell proliferation. And lnc LINC00461 was negatively correlated with miR-30a-5p expression. ZEB2 was a direct target of miR-30a-5p, and miR-30a-5p mimic and sh lnc LINC00461 significantly reduced ZEB2 expression levels. Finally, In vivo, lnc LINC00461 promoted tumor growth by modulating the miR-30a-5p / ZEB2 axis. In conclusion, LncLINC00461 promoted the progression of NSCLC by the miR-30a-5p / ZEB2 axis, and lnc LINC00461 may be a potential therapeutic target for NSCLC.

A miR-567-PIK3AP1-PI3K/AKT-c-Myc feedback loop regulates tumour growth and chemoresistance in gastric cancer

Background

Gastric cancer (GC) ranks the fifth most common cancer, and chemotherapy is one of the most common treatments for GC. However, chemoresistance limits the effectiveness of chemotherapy and leads to treatment failure. This study aims to investigate the biological effect of miR-567 on gastric tumourigenesis and chemoresistance and reveal the possible mechanism.

Methods

We measured the expression of miR-567 in 37 paired normal and stomach tumour specimens, as well as GC cell lines by Real-time PCR. The functional effects of miR-567 were validated using in vitro and in vivo assays. Dual-luciferase report assays and Chromatin immunoprecipitation (ChIP) assay were conducted for target evaluation, western blot assay was used to confirm the relationships.

Findings

Our data showed that miR-567 was downregulated in gastric tissues and gastric cancer cells compared with normal tissues and gastric epithelial cells. In vitro, Gain- and lose-of-function assays showed miR-567 not only weakened cells proliferative ability, but also sensitized GC cells to 5-FU and oxaliplatin. In vivo, miR-567 overexpression significantly repressed the tumourigenesis of GC cells compared with the vector control. Mechanistic analysis showed that PIK3AP1 activated AKT phosphorylation in GC. Meanwhile, miR-567 directly targeted PIK3AP1 to inactivate PI3K/AKT/c-Myc pathway and c-Myc inversely regulated miR-567 expression, thus forming a miR-567-PIK3AP1- PI3K/AKT-c-Myc feedback loop explaining the function of miR-567.

Interpretation

Our studies revealed that miR-567 acts as a tumour suppressor gene and suppresses GC tumorigenesis and chemoresistance via a miR-567-PIK3AP1- PI3K/AKT-c-Myc feedback loop. These results suggest that miR-567 may serve as a target for chemoresistance and a potential prognostic biomarker for GC.

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miR-567 sensitized GC cells to 5-FU and oxaliplatin, suggesting that it can be a target for chemoresistance.

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miR-567 functions as a suppressor in GC progression and may serve as a novel prognostic and therapeutic biomarker for GC.

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miR-567 directly targeted PIK3AP1 to inactivate PI3K/AKT/c-Myc and regulated its own expression.

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These findings uncover a plausible mechanism for AKT/PI3K signalling activation in cancer progression.

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test.

BiModule: biclique modularity strategy for identifying transcription factor and microRNA co-regulatory modules

Systematic identification of gene regulatory modules can provide invaluable knowledge towards understanding aberrant transcriptional/post-transcriptional collaborative regulatory (co-regulatory) effects in cancer. Transcription factor (TF) and microRNA (miRNA) are known as two classes of prominent regulators that play crucial roles in gene regulation. Existing studies on gene regulatory modules identification mainly focused on the miRNA-mediated regulatory network, and few considered these two regulators in a co-occurring network. In this current study, we developed a computational method called BiModule for systematically identifying TF-miRNA co-regulatory modules. BiModule operates in two main stages: it first constructs a cancerspecific regulator-mRNA network and then identifies modules based on maximal bicliques by employing biclique modularity strategy, which is a novel flexible method for bipartite graph mining. We applied our model to a cervical cancer dataset. The results showed that the TF-miRNA co-regulatory modules identified by BiModule exhibit denser connections and stronger expression correlations than another existing related method. Moreover, the BiModule-modules exhibit high biological functional enrichment. In addition, based on Kaplan-Meier survival analysis, we found a number of modules with significant prognostic associations.