Ginsenoside improves papillary thyroid cancer cell malignancies partially through upregulating connexin 31.

Connexin 31 (Cx31) is considered a suppressor for many tumors. Ginsenoside (Rg1) is a traditional Chinese herb that is widely acknowledged due to its anti-tumor characteristics. However, limited studies have focused on the role of Rg1 in papillary thyroid cancer (PTC) cells. In the current study, we found that the expression of Cx31 in thyroid cancer tissues and thyroid cancer cell lines was significantly lower than that in normal thyroid epithelial tissues and cell lines. Overexpression of Cx31 reduced thyroid cancer cell proliferation, migration and invasion. Furthermore, we found that Rg1 significantly enhanced the expression of Cx31. Moreover, the proliferation and migration of IHH-4 and BCPAP cells were significantly reduced by Rg1 treatment. In contrast, the silencing of Cx31 enhanced the expression of Ki67 and proliferating cell nuclear antigen (PCNA). Meanwhile, treatment with Rg1 significantly decreased the protein levels of Ki67 and PCNA, but these effects could be abolished by transfection with si-Cx31. In summary, we provide novel evidence that the expression of Cx31 was decreased in thyroid cancer cells, but Rg1 treatment could significantly enhance the expression of Cx31 thereby suppressing thyroid cancer cell proliferation and migration.

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.