Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods.

Identifying biomarkers of Multiple Sclerosis is important for the diagnosis and treatment of Multiple Sclerosis. The existing study has shown that miRNA is one of the most important biomarkers for diseases. However, few existing methods are designed for predicting Multiple Sclerosis-related miRNAs. To fill this gap, we proposed a novel computation framework for predicting Multiple Sclerosis-associated miRNAs. The proposed framework uses a network representation model to learn the feature representation of miRNA and uses a deep learning-based model to predict the miRNAs associated with Multiple Sclerosis. The evaluation result shows that the proposed model can predict the miRNAs associated with Multiple Sclerosis precisely. In addition, the proposed model can outperform several existing methods in a large margin.

Association between IL-6-174G/C polymorphism and risk of multiple sclerosis: a meta-analysis.

OBJECTIVE: Interleukin-6 (IL-6) is a pleiotropic cytokine and important mediator of many inflammatory processes, which might affect susceptibility to multiple sclerosis (MS). The aim of this study was to assess the effect of IL-6-174G/C polymorphism on the risk of MS using a meta-analysis. MATERIALS AND METHODS: The Pubmed, ISI Web of Science, Wanfang, VIP, and China National Knowledge Infrastructure databases were screened and six studies were included in the meta-analysis. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CI) were calculated to evaluate the association between the IL-6-174G/C polymorphism and risk of MS. RESULTS: No significant association between the IL-6-174G/C polymorphism and risk of MS was observed in overall analyses. With stratification according to ethnicity, we found that carriers with the IL-6-174CC genotype had a 1.87-fold increased risk for the development of MS in Asians (recessive model: OR=1.87, 95% CI, 1.08-3.24), but not in Caucasians. CONCLUSION: This meta-analysis provides evidence that the IL-6-174G/C polymorphism may be a risk factor for the development of MS in Asians. Further association studies with a larger sample size are required to confirm the result in different populations.

Proteomic analysis of the cerebrospinal fluid in multiple sclerosis and neuromyelitis optica patients.

The present study aimed to compare the 2-dimensional (2D) electrophoresis pattern of the cerebrospinal fluid (CSF) in multiple sclerosis (MS), neuromyelitis optica (NMO) and control individuals, to identify the proteins with differential expression and to examine their significance. CSF samples from the three groups were collected and total protein was isolated and quantified using the Bradford method. 2D electrophoresis of the samples was conducted using equal amounts of CSF. In the CSF 2D gel electrophoresis map, 118 points were obtained from the MS group, 155 points from the NMO group and 350 points from the normal control group. Non-matching proteins appeared in 14 spots in the MS group and in 45 spots in the NMO group, and were also expressed in the 2D electrophoresis pattern of the normal control group. Four differential proteins were identified through the HD-MS/MS and MASCOT network search. Pre-albumin (PA) was found only in the CSF 2D gel electrophoresis map of the MS patients. Keratin 1 was expressed in the normal control group, but not in the MS group. The difference in the expression of keratin 9 in the MS group was twice that in the normal control group. The expression of keratin 1, keratin 9 and transferrin in the NMO group was twice that in the normal control group. The expression of PA was found only in the CSF 2D gel electrophoresis map of the MS patients, and not in the NMO group. Keratin 1 was expressed in the NMO group, but not in the MS group. The expression of a variety of proteins in the 2D electrophoresis pattern of CSF was significantly different in the MS, NMO and control groups. PA, keratin 1, transferrin and keratin 9 are identified as significantly differentially expressed proteins.