Comparison of Intraperitoneal and Intratesticular GYY4137 Therapy for the Treatment of Testicular Ischemia Reperfusion Injury in Rats.

The efficacy of intraperitoneal GYY4137 therapy and intratesticular GYY4137 therapy in an experimental rat model was investigated. Four groups were set up as the sham-operation group, torsion/detorsion (T/D) group, T/D plus intraperitoneal GYY4137 (G-IP) group, and T/D plus intratesticular GYY4137 (G-IT) group. In order to establish a testicular T/D model, the left testis was operated and the rotation reached 720° clockwise which lasted 1 h before reperfusion. The G-IP group accepted 100 µmol/kg of GYY4137 intraperitoneally 30 min after testicular rotation, while the G-IT group was treated with the same dose by intratesticular injection. Six h after detorsion, the testis was collected and subsequently assessed. The T/D group showed significant changes in histology and an enhancement in the level of oxidative stress and apoptosis compared to the sham-operation group. The expression of Caspase-3 and Bax turned out to be strengthened by T/ D and relatively decreased with GYY4137 treatment in both the G-IP and G-IT groups. Moreover, the Bcl-2 expression was inhibited in the T/D group, and promoted by GYY4137 in the G-IP and G-IT groups. GYY4137, moderating these observed changes, displayed a more protective effect with G-IT therapy than G-IP therapy.This study indicated that the efficacy of intratesticular therapy with GYY4137 is better than that of intraperitoneal therapy, which may provide a more valuable approach for testicular torsion therapy.

Identification of hub genes associated with outcome of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumor types of the urinary system. Bioinformatics tools have been used to identify new biomarkers of ccRCC and to explore the mechanisms underlying development and progression of ccRCC. The present study analyzed the differentially expressed genes (DEGs) associated with RCC using data obtained from Gene Expression Omnibus datasets and GEO2R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of these DEGs was performed and analyzed using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes to identify the hub genes, defined as the genes with the highest degree of interrelation. Subsequently, differential expression and survival analyses of hub genes was performed using The Cancer Genome Atlas database and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Using GEO2R, 1,650 DEGs were identified, including 743 upregulated and 907 downregulated genes. GO and KEGG pathway analyses indicated that the upregulated DEGs were primarily involved in blood vessel and vasculature development, whereas downregulated DEGs were primarily involved in organic acid metabolic processes and carboxylic acid metabolic processes. Subsequently, important modules were identified in the PPI network using Cytoscape's Molecular Complex Detection. The 15 most connected hub genes were identified among DEGs, including glycine decarboxylase (GLDC), enolase 2 (ENO2) and topoisomerase II alpha. GEPIA revealed the association between expression levels of hub genes and survival. Specifically, GLDC and ENO2 were associated with the prognosis of patients with RCC and thus, the effects of GLDC and ENO2 involvement in renal cancer were investigated in vitro. GLDC and ENO2 affected the proliferation and apoptosis of renal cancer cells. These hub genes may reveal a new mechanism underlying development or progression of RCC and identify new markers for its diagnosis and prognosis.

Identification of Core Biomarkers Associated with Outcome in Glioma: Evidence from Bioinformatics Analysis.

Glioma is the most common neoplasm of the central nervous system (CNS); the progression and outcomes of which are affected by a complicated network of genes and pathways. We chose a gene expression profile of GSE66354 from GEO database to search core biomarkers during the occurrence and development of glioma. A total of 149 samples, involving 136 glioma and 13 normal brain tissues, were enrolled in this article. 1980 differentially expressed genes (DEGs) including 697 upregulated genes and 1283 downregulated genes between glioma patients and healthy individuals were selected using GeoDiver and GEO2R tool. Then, gene ontology (GO) analysis as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were carried out using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plug-in was employed to imagine protein-protein interaction (PPI) of these DEGs. The upregulated genes were enriched in cell cycle, ECM-receptor interaction, and p53 signaling pathway, while the downregulated genes were enriched in retrograde endocannabinoid signaling, glutamatergic synapse, morphine addiction, GABAergic synapse, and calcium signaling pathway. Subsequently, 4 typical modules were discovered by the PPI network utilizing MCODE software. Besides, 15 hub genes were chosen according to the degree of connectivity, including TP53, CDK1, CCNB1, and CCNB2, the Kaplan-Meier analysis of which was further identified. In conclusion, this bioinformatics analysis indicated that DEGs and core genes, such as TP53, might influence the development of glioma, especially in tumor proliferation, which were expected to be promising biomarkers for diagnosis and treatment of glioma.