Identification of the miRNA–mRNA regulatory network in multiple sclerosis

<p><b>Objectives:</b> Multiple sclerosis (MS) is the common neurological disorders in young adults, which affects the central nervous system myelin or oligodendrocytes and results in disability. This study aimed to identify the key miRNAs in blood of patients in MS for better understanding the underlying mechanisms of MS.</p> <p><b>Methods:</b> The publicly available Gene Expression Omnibus data-sets of MS were performed to integrated analysis. miRNA expression and mRNA expression were analyzed in whole blood samples from patients with MS and healthy controls by microarray analysis, Gene Ontology enrichment analyses, Kyoto Encyclopedia of Genes and Genomes pathway analyses, construction of miRNA–mRNA interaction network, and quantitative real-time polymerase reaction.</p> <p><b>Results:</b> In patients with MS, microarray analysis identified 45 significantly dysregulated miRNAs and 621 significantly dysregulated mRNAs. 1165 negative correlation pairs of miRNA–mRNA were predicted and used to construct the interaction network. hsa-miR-30a, hsa-miR-93, hsa-miR-20b, and hsa-miR-20a occurred as central hubs regulating 87, 38, 34, and 34 genes. Dysregulated mRNAs were significantly enriched in ribosome, tuberculosis, and pathways in cancer. The verification of qRT-PCR displayed that hsa-miR-328-3p was significantly up-regulated in MS and its target genes RAC2 had the down-regulated tendency in MS. hsa-miR-20a-5p had the up-regulated tendency and the corresponding target gene EIF4EBP2 had the down-regulated tendency in MS compared to healthy controls.</p> <p><b>Discussion:</b> hsa-miR-30a, hsa-miR-93, hsa-miR-20b, and hsa-miR-20a might be the key participant in the pathophysiology of MS involved in signaling pathways including ribosome, tuberculosis, and pathways in cancer.</p

Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

<div><p>The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of–the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.</p> </div

Classification accuracies of distinct classification methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).

<p>Classification accuracies of distinct classification methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).</p

Classification accuracies of PPI-SVM-KNN with the change of parameter C.

<p>The bars and error ticks represent mean values and standard deviations respectively. (A) shows the result for Baranzini dataset. (B) shows the result for Goertsches dataset.</p

Precision, Recall and F-measure of different classification approaches.

<p>The bars and error ticks represent mean values and standard deviations respectively. (A) shows the result for Baranzini dataset. (B) shows the result for Goertsches dataset.</p

Classification accuracies of different discretization methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).

<p>Classification accuracies of different discretization methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).</p

Schematic overview of classification of time series gene expression.

<p>The prediction process primarily consists of 4 or 5 steps. Firstly, gene states are inferred by an HMM/GMM hybrid model. Secondly, the QL-biclustering algorithm extracts biclusters of every patient from the gene state matrix. Thirdly, every bicluster is scored according to its genes' connection in the protein-protein interaction (PPI) network. Finally, the label of every test patient is predicted by PPI-SVM-KNN based on patient similarity, taking into account both bicluster similarity and its PPIScore.</p

Randomly selected biclustering examples from Baranzini dataset and Goertsches dataset.

<p>The expression values of genes in each bicluster are shown in (A) and (B). The state transitions of genes in each bicluster are shown in (C) and (D). The bicluster from Baranzini dataset consists of gene ITGAL and gene ITGB1 and their state transitions from time point 1 to time point 7. The bicluster from Goertsches dataset consists of gene CASP5 and gene CASP1 and their state transitions from time point 1 to time point 3.</p

Prediction accuracies of different classification approaches with the change of measurements.

<p>The points in the figure represent mean values. (A) shows the accuracies from time point 3 to time point 7 for Baranzini dataset. (B) shows the accuracies from time point 3 to time point 5 for Goertsches dataset.</p

Classification accuracies of PPI-SVM-KNN with the change of parameter K from 3 to 9.

<p>The bars and error ticks represent mean values and standard deviations respectively. (A) shows the result for Baranzini dataset. (B) shows the result for Goertsches dataset.</p