Banke Robbins (metalperch1)

Objective The aim of this study was to use bioinformatic analyses to identify key genes and pathways driving gastric cancer (GC). Materials and Methods The gene expression profiles, from human gastric tissue samples were downloaded from the Gene Expression Omnibus (GSE)29272 dataset. These data revealed 284 differentially expressed genes (DEGs) that included a group upregulated in cancer tissues (n = 142) and another group that were downregulated in cancer tissues. (n = 142). These DEGs were identified using the GEO2R. We used multiple online analysis tools, including, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction networks, gene expression profiling interactive analysis (GEPIA), and the cBio Cancer Genomics Portal (cBioportal) database. Next, we identified the most significant DEGs using the Kaplan-Meier plotter (KM-plotter) database. Multiple bioinformatic platforms were used to identify candidate prognostic marker genes. We then analyzed freshly frozen GC tissues for the expression of these marker genes to validate the informatic findings. Results We identified three DEGs related to overall survival from our analyses of the GEO data. Next, we analyzed these three DEGs in GEPIA and the cBioportal database and found that the biglycan (BGN) gene was related to invasion and metastases of GCs. This finding of differential gene expression was confirmed in a separate laboratory analysis of normal and GC tissues. In this analysis we found that high levels of BGN expression were correlated with GC clinicopathological characteristics, including microvascular tumor thrombus (p = 0.018), lymph node metastases (p = 0.013), and vessel invasion (p = 0.004). Conclusions BGN expression levels appear to be an independent prognostic factor for predicting the survival times of GC patients.Objective Osteogenesis imperfecta (OI) includes a group of disorders characterized by susceptibility to bone fractures with different severities. The increasing number of genes that may underlie the disorder, along with the broad phenotypic spectrum that overlaps with other skeletal diseases, provided a compelling case for the use of high-throughput sequencing (HTS) technology as an aid to OI diagnoses. The aim of this analysis was to present the data from our 5-year targeted HTS results, that includes the reporting of 9 novel and 24 known mutations, found in OI patients, from 5 different regions of Turkey. Materials and Methods We performed a retrospective cross-sectional study, reporting the HTS results of 43 patients (23 female and 20 male; mean age 9.5 years), directed to our center with a suspicion of OI between February 2015 and May 2020. Genetic analyses were also performed for 24 asymptomatic parents to aid the segregation analyses. We utilized an HTS panel targeting the coding regions of 57 genes asspatients.Aims We analyzed and compared the gene expression profiles (GSE92763) from normal melanocytes with malignant melanoma cell lines to identify genes that were differentially expressed that could serve as potential biomarkers for melanoma diagnosis. Materials and Methods Gene expression profiles from the GSE92763 dataset were downloaded from the Gene Expression Omnibus (GEO) database. By comparing normal human melanocytes with multiple melanoma cell lines we identified 127 differentially expressed genes whose expression was altered. These data were used to identify hub genes associated with protein-protein interaction networks using Cytoscape software. To explore the biological functions of the aforementioned hub genes, we utilized the clusterProfiler package in R studio to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We then used the Gene Expression Profiling Interactive Analysis (GEPIA) website to determine the association of these hub genes with overall su5.586). MDV3100 Androgen Receptor antagonist FBLN1 has extremely high DNA copy number a