Identification of key gene modules and pathways of human glioma through coexpression network.

Glioma causes great harm to people worldwide. Systemic coexpression analysis of this disease could be beneficial for the identification and development of new prognostic and predictive markers in the clinical management of glioma. In this study, we extracted data sets from the Gene Expression Omnibus data set by using "glioma" as the keyword. Then, a coexpression module was constructed with the help of Weighted Gene Coexpression Network Analysis software. Besides, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the genes in these modules. As a result, the critical modules and target genes were identified. Eight coexpression modules were constructed using the 4,000 genes with a high expression value of the total 141 glioma samples. The result of the analysis of the interaction among these modules showed that there was a high scale independence degree among them. The GO and KEGG enrichment analyses showed that there was a significant difference in the enriched terms and degree among these eight modules, and module 5 was identified as the most important module. Besides, the pathways it was enriched in, hsa04510: Focal adhesion and hsa04610: Complement and coagulation cascades, were determined as the most important pathways. In summary, module 5 and the pathways it was enriched in, hsa04510: Focal adhesion and has 04610: Complement and coagulation cascades, have the potential to serve as biomarkers for patients with glioma.

Docetaxel-decorated anticancer drug and gold nanoparticles encapsulated apatite carrier for the treatment of liver cancer.

Liver malignancy is one of serious sicknesses which undermine human life and wellbeing. Concentrates on the treatment of liver malignancy have attracted widespread consideration. Utilization of drug delivery system cannot just enhance particular medication conveyance to liver tissue and enhance the bioavailability of medication, yet additionally can lessen the reactions of medications when it is uniquely changed in the regards of structure adjustment or particular target particles improvement. Docetaxel (Dxtl) remains the favored decision of enhancing the survival of patients with hormone stubborn liver malignancy, however numerous patients experience the ill effects of humble medication reaction and huge lethality. In the present examination, we researched the productivity of novel Dtxl stacked gold doped apatite (Dtxl-GHANPs) and increase insights into the molecular mechanism of the apoptosis initiated by these novels Dxtl- stacked nanoparticles. In vitro anticancer tests demonstrated that Dtxl-GHANPs had a higher cytotoxicity against human liver cancer cells (HepG2). The dual staining and Hoechst measure uncovered the dynamic idea of Dtxl-GHANPs in the cell core of the HepG2 cells. The potential mitochondrial break by apoptosis in HepG2 cells enormously expanded cell passing. Our discoveries demonstrated that nanoparticles, more than basic medication carriers, may assume a dynamic part in intervening the organic impacts.

MicroRNA-615-5p targets insulin-like growth factor 2 and exerts tumor-suppressing functions in human esophageal squamous cell carcinoma.

To investigate the expression pattern, clinical significance and functional roles of microRNA (miR)-615-5p in human esophageal squamous cell carcinoma (ESCC), , quantitative real-time PCR was performed to detect expression levels of miR­615­5p in ESCC tissues and cell lines. Associations between miR­615­5p expression and various clinicopathological features of ESCC patients were also statistically evaluated. The candidate targets of miR­615­5p were identified by integrating bioinformatics miRNA target prediction, western blot analysis and luciferase reporter assay. Moreover, the functions of miR­615­5p in ESCC cell migration and invasion were determined using the transfection of miRNA mimics, or co-transfected with miRNA mimics and the expression vector of its target gene. As a result, miR­615­5p expression in ESCC tissues and cells were markedly lower than those in non-cancerous esophageal mucosa and human normal esophageal cells, respectively (both P<0.001). miR­615­5p downregulation was significantly associated with advanced tumor-node-metastasis stage, positive lymph node metastasis and moderate-poor differentiation. Functionally, the re-expression of miR­615­5p suppressed the invasion and migration of ESCC cells in vitro. Interestingly, insulin-like growth factor 2 (IGF2) was identified as a direct target gene of miR­615­5p, and the inhibitory effects of miR­615­5p in ESCC cell motility were reversed by the restoration of IGF2 expression. In conclusion, miR­615­5p downregulation may be an underlying molecular mechanism of development and progression of ESCC, and may function as a potential therapeutic target of this malignancy. Also, we illustrate that the miR­615­5p/IGF2 axis may bring important contributions to cell motility of human ESCC.

Identification of a prognostic 5-Gene expression signature for gastric cancer.

PURPOSE: Gastric cancer (GC) is a major tumor throughout the world with remaining high morbidity and mortality. The aim is to generate a gene model to assess the prognoses risk of patients with GC. METHODS: Gene expression profiling of gastric cancer patients, GSE62254 (300 samples) and GSE26253 (432 samples), was downloaded from Gene Expression Omnibus (GEO) database. Univariate survival analysis and LASSO (Least Absolute Shrinkage and Selectionator operator) (1000 iterations) of differentially expressed genes in GSE62254 was assessed using survival and glmnet in R package, respectively. Kaplan-Meier analysis on the clustering algorithm from each regression model was performed to calculate the influence to the prognosis. Random samples in GSE26253 were analyzed in multivariate and univariate survival analysis for one thousand times to calculate statistical stability of each regression model. RESULTS: A total of 854 Genes were identified differentially expressed in GSE62254, among which 367 Genes were found influencing the prognoses. Six gene clusters were selected with good stability. Hereinto, five or more genes in 11-Gene model, TRPC1, SGCE, TNFRSF11A, LRRN1, HLF, CYS1, PPP1R14A, NOV, NBEA, CES1 and RGN, was available to evaluate the prognostic risk of GC patients in GSE26253 (P = 0.00445). The validity and reliability was validated. CONCLUSION: In conclusion, we successfully generated a stable 5-Gene model, which could be utilized to predict prognosis of GC patients and would contribute to postoperational treatment and follow-up strategies.