The aim of the present study was to investigate the potential role of GAS8 antisense RNA 1 (GAS8-AS1) in papillary thyroid carcinoma (PTC). PcDNA3.1-GAS8-AS1 and si-GAS8-AS1, miR-135b-5p mimic and si-CCND2 were transfected into PTC cells. Cell proliferation was evaluated by Cell Counting Kit-8 (CCK-8). QRT-PCR was used to determine expressions of GAS8-AS1, miR-135b-5p, and CCND2, and Western blot were detected protein level of CCND2. The miRNA target gene prediction site TargetScan was used to predict potential targets of GAS8-AS1 and miR-135b-5p. Cell cycle progression was analyzed by flow cytometry. We found that GAS8-AS1 was down-regulated in PTC cell lines and inhibited proliferation and cycle of PTC cell. GAS8-AS1 directly targets miR-135b-5p, and GAS8-AS1 could regulate a downstream target of miR-135b-5p, Cyclin G2 (CCNG2), in an miR-135b-5p-mediated manner. In addition, we also proved that overexpressed GAS8-AS1 inhibited tumor formation in vivo. GAS8-AS1 suppresses PTC cell growth through the miR-135b-5p/CCND2 axis.
Surgical site infection (SSI) is an important component of infections acquired from hospital. The most significant feature of vascular surgery different from other surgeries is frequent application of artificial grafts. Once SSI occurs after vascular operations with grafts, it might results in a serious disaster. Staphylococcus aureus and coagulase-negative Staphylococcus are the most common pathogenic bacteria for SSI after vascular surgery. Although SSI in vascular surgery often lacks of typical clinical characters, some clinical symptoms, laboratory data and certain imaging procedures may help to diagnose. In most cases of SSI after vascular procedures, the artificial grafts must be removed and sensitive antibiotics should be administered. However, for different cases, personalized management plan should be made depending on the severity and location of SSI.
The molecular mechanism of AAA formation is still poorly understood and has not been fully elucidated. The study was designed to identify the immune-related genes, immune-RAS in AAA using bioinformatics methods. The GSE175683 datasets were downloaded from the GEO database. The DEseq2 software was used to identify differentially expressed genes (DEGs). SUVA pipeline was used to quantify AS events and RAS events. KOBAS 2.0 server was used to identify GO terms and KEGG pathways to sort out functional categories of DEGs. The CIBERSORT algorithm was used with the default parameter for estimating immune cell fractions. Nine samples from GSE175683 were used to construct the co-disturbed network between expression of SFs and splicing ratio of RAS events. PCA analysis was performed by R package factoextra to show the clustering of samples, and the pheatmap package in R was used to perform the clustering based on Euclidean distance. The results showed that there were 3,541 genes significantly differentially expressed, of which 177 immune-related genes were upregulated and 48 immune-related genes were downregulated between the WT and WTA group. Immune-RAS events were mainly alt5P and IR events, and about 60% of it was complex splicing events in AAA. The WT group and the WTA group can be clearly distinguished in the first principal component by using the splicing ratio of immune-RAS events. Two downregulated genes, Nr4a1 and Nr4a2, and eight upregulated genes, Adipor2, Akt2, Bcl3, Dhx58, Pparg, Ptgds, Sytl1, and Vegfa were identified among the immune-related genes with RAS and DEGs. Eighteen differentially expressed SFs were identified and displayed by heatmap. The proportion of different types of cells and ratio of the average ratio of different cells were quite different. Both M1 and M2 types of macrophages and plasma cells were upregulated, while M0 type was downregulated in AAA. The proportion of plasma cells in the WTA group had sharply increased. There is a correlation between SF expression and immune cells/immune-RAS. Sf3b1, a splicing factor with significantly different expression, was selected to bind on a mass of immune-related genes. In conclusion, our results showed that immune-related genes, immune-RAS, and SFs by genome-wide identification were involved in AAA.
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