Human PRKG1 (Protein Kinase, cGMP Dependent Type I) ELISA Kit

This study aimed to uncover effects of non-coding RNA transcripts on ovarian endometriosis (OEM) development. Two transcription datasets (GSE105764 and GSE105765) about OME were downloaded from Gene Expression Omnibus (GEO) database and the differentially expressed mRNAs, lncRNAs and miRNAs (DEmRNAs, DElncRNAs and DEmiRNAs) between OEM cases and controls were identified followed by protein-protein interaction analysis. Then, co-expression analysis was conducted and DEmiRNAs-DEmRNAs as well as DElncRNAs-DEmiRNAs pairs were predicted to construct the ceRNA network followed by sub-ceRNA network associated with OEM extraction. Functional analyses of DEmRNAs in ceRNA and sub-module network and the survival analysis were also performed to evaluate the correlation of key regulators and OV outcomes. Totally, 1910 DEmRNAs, 158 DElncRNAs and 118 DEmiRNAs were screened between OEM cases and controls and the functional analyses of DEmRNAs showed that they were significantly enriched in cell adhesion. Furthermore, there were 505 nodes in PPI network and ceRNA network included 762 interaction pairs among 357 DEmRNAs, 28 DElncRNAs and 24 DEmiRNAs. The KEGG analysis suggested several genes including FOXO1, STAT5A and RUNX1 were predominately associated with pathways in cancer while IL15 was primarily enriched in cytokinecytokine receptor interaction pathway. Importantly, these two pathways were also found to be implicated with OEM development. Finally, survival analysis implied that overexpression of ZFPM2-AS1 had a good clinical outcome while the under-expression levels of FOXO1, JUP, STAT5A, RUNX1 and PRKG1AS1 exhibited a better prognosis for OV. FOXO1, STAT5A, RUNX1 and IL15, PRKG1-AS1 and ZFPM2-AS1 were promising diagnostic makers for OME. (C) 2020 Elsevier B.V. All rights reserved.

Objective To explore the molecular regulatory mechanisms underlying fibroblast differentiation and dysfunction in the development of adolescent idiopathic scoliosis (AIS) in an effort to identify candidate therapeutic targets for AIS. Methods The GSE110359 dataset, obtained from the bone marrow stromal cells of 12 AIS patients and five healthy controls, was retrieved from the GEO database. The data were preprocessed and differentially expressed genes (DEGs) were identified. KEGG pathway and Gene Ontology (GO)-Biological Process (BP) enrichment analyses were performed to identify the function of the DEGs. A protein-protein interaction (PPI) and a microRNA-transcription factor (TF)-target co-regulatory network were constructed to identify hub genes in the development of AIS. In addition, hub DEGs were evaluated by quantitative PCR (qPCR) and immunohistochemical staining. Results A total of 188 DEGs including 100 up-regulated and 88 down-regulated genes were obtained. The up-regulated DEGs were related to "p53 signaling pathway", "FoxO signaling pathway", and "cGMP-PKG signaling pathway" terms, while the down-regulated DEGs were significantly enriched in seven terms including "protein processing in endoplasmic reticulum". The key up-regulated genes,PRKG1,CCNG2, andKAT2B, and the key down-regulated genes,MAP2K1andDUSP6, were identified by the PPI and miRNA-TF-Target regulatory network analyses. mRNA expression patterns forPRKG1,DUSP6, andKAT2Bwere successfully verified by qPCR. In addition, PRKG1 protein levels were found to be elevated during the immunohistochemical analysis. Conclusion Increased expression of PRKG1 in AIS patients might be an attractive therapeutic target for AIS. However, further gain or loss-of-function studies should be conducted.