Plasma miR-409-3p promotes acute cerebral infarction via suppressing CTRP3.

Abnormal expression of miR-409-3p has been found in several neurodevelopmental disorders, but whether it is dysregulated in the patients with acute cerebral infarction (ACI) has not been evaluated. The current study mainly focused on the clinical significance and the underlying mechanism of plasma miR-409-3p in the progression of ACI. The level of plasma miR-409-3p was determined in ACI patients (n = 80) and healthy controls (n = 30). Pearson correlation assay was performed to evaluate the association and cardiovascular risk factors. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic value of plasma miR-409-3p levels in patients with ACI. Dual luciferase reporter assay and western blot were performed to determine the possible target gene of miR-409-3p. Our data showed that the expression of plasma miR-409-3p in the ACI group was higher than that in the healthy controls. Furthermore, Pearson correlation analysis indicated a positive correlation between plasma miR-409-3p and the NIHSS score. ROC analysis indicated that plasma miR-409-3p could differentiate plasma miR-409-3p in ACI patients from healthy controls. Then, we explored the possible target genes of miR-409-3p. Interestingly, C1q and TNF-related 3 (CTRP3), a novel adipose tissue-derived secreted factor, was found to be a target gene of miR-409-3p. We found that knockdown of CTRP3 significantly induced PC12 cell apoptosis, even in PC12 cells transfected with miR-409-3p inhibitor. These data suggested that miR-409-3p induced PC12 cell apoptosis by targeting CTRP3. Altogether, elevated plasma miR-409-3p is correlated with disease severity and may be efficient for the early diagnosis of ACI.

Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network.

The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein-protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.

Integration of gene expression data and genetic variations involved in breast cancer.

PURPOSE: The studies of transcriptome and genome involved in breast cancer are effectively promote the understanding of biological processes and the development of novel targeted therapies. Here we performed an integrated analysis of gene expression and genetic variation to disclose the molecular pathogenesis in breast cancer. METHODS: Gene expression profiles were applied to identify differential expression levels of genes between breast cancer and normal subjects. DNA sequencing data were extracted to analyze gene mutational information including number of mutations, number of mutated genes and their chromosomal distributions. Correlation analysis of gene mutations and differential expression was performed. Network-based approach was applied to compare the topological properties between the differentially expressed (DE) genes prone to mutation and those that(were not. Two-tailed p<0.05 was considered as statistically significant. RESULTS: Statistical analysis showed that DE genes presented significantly positive correlation with the number of mutations (p=1.267E(-05)), mutated genes (p=0.00001) and total genes in the genome (p=2.489E(-06)). There were 81 genes, both DE and mutant, and they were distributed on chromosome 4 (N=51), chromosome 15 (N=29), and chromosome 18 (N=1). These 81 genes showed an increase in the number of genes interacting with in the protein-protein network. CONCLUSION: Analysis of the integration of transcriptome and genome in breast cancer disclosed distinctive topology between the DE genes prone to mutation and those that were not.