Anti-EGR1 monoclonal antibody, clone nBcdbn66271 [FITC]

Chronic obstructive pulmonary disease (COPD) is a multifactorial and heterogeneous disease that creates public health challenges worldwide. The underlying molecular mechanisms of COPD are not entirely clear. In this study, we aimed to identify the critical genes and potential molecular mechanisms of COPD by bioinformatic analysis. The gene expression profiles of lung tissues of COPD cases and healthy control subjects were obtained from the Gene Expression Omnibus. Differentially expressed genes were analyzed by integration with annotations from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, followed by construction of a protein-protein interaction network and weighted gene coexpression analysis. We identified 139 differentially expressed genes associated with the progression of COPD, among which 14 Hub genes were identified and found to be enriched in certain categories, including immune and inflammatory response, response to lipopolysaccharide and receptor for advanced glycation end products binding; in addition, these Hub genes are involved in multiple signaling pathways, particularly hematopoietic cell lineage and cytokine-cytokine receptor interaction. The 14 Hub genes were positively or negatively associated with COPD by wgcna analysis. The genes CX3CR1, PTGS2, FPR1, FPR2, S100A12, EGR1, CD163, S100A8 and S100A9 were identified to mediate inflammation and injury of the lung, and play critical roles in the pathogenesis of COPD. These findings improve our understanding of the underlying molecular mechanisms of COPD.

Background: Gliosarcoma (GS) is a rare primary neoplasm of the central nervous system. It is a subtype of glioblastoma and has a biphasic pattern consisting of glial and malignant mesenchymal elements. Its onset is between the fourth and sixth decade of life. Objectives: Since protein - protein interaction (PPI) network analysis can provide useful information about molecular aspects of diseases, the aim of this study is GS protein analysis via PPI network and gene ontology assessment. Methods: The related genes to GS were gathered from STRING DB and organized in the interacted network by Cytoscape software version 3.6.0. The network was analyzed based on topological parameters and the central nodes were introduced. The significant clusters were identified by ClusterONE and the cluster included more key genes enriched via gene ontology by ClueGO. Results: Nine crucial genes including TP53, EGFR, PTEN, EGR1, VEGFA, HSP90AA1, IL2, KNG1, and HSP90AB1 were introduced as related key genes to GS. Two significant clusters contain most of central genes. Twenty - one elements of cluster - 1, which included 7 key genes, were enriched via gen ontology and 115 related terms were determined and discussed. Conclusions: The nine introduced central genes may play main roles in pathology of GS. However, experimental investigation is proposed to validate the findings.