Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.6.1.44 (AGT)
 770 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Pituitary adenoma results from accumulation of multiple genetic and/or epigenetic aberrations such as GNAS, MEN1, CNC, and FIPA. LRRC4 is relatively tissue-specific expressed gene in the normal brain and downregulated expression in glioma (87.5%), meningioma (80.9%), and pituitary adenoma (85.5%). It has been suggested that the aberrant expression of LRRC4 contributes to tumorigenesis in glioma. However, little is known yet about association between LRRC4 and risk of pituitary adenoma. In this study, we genotyped three LRRC4 haplotype-tagging SNPs (htSNP) by direct sequencing in case-control studies, which included 183 Han Chinese patients diagnosed with pituitary adenoma and 183 age-, gender-matched, and geographically matched Han Chinese controls. Haplotypes were reconstructed according to the genotyping data and linkage disequilibrium status of the htSNP. We observed statistically significant differences regarding the genotype TT + CT of rs6944446 in the NCA. Haplotype AC of rs3823994-rs6944446 is suggested to have a protective effect in the development of pituitary adenoma (OR 0.339; 95% CI 0.123-0.934). However, haplotype GT of rs3808058-rs6944446 (OR 1.575; 95% CI 1.048-2.368) and AGT of rs3823994-rs6944446-rs3808058 (OR 1.673; 95% CI 1.056-2.651) might be a risk factor for pituitary adenoma development. In a brief, the results support the hypothesis that polymorphisms or haplotypes in the LRRC4 may have important research significance and could be used to predict the risk of pituitary adenoma.
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PMID:LRRC4 haplotypes are associated with pituitary adenoma in a Chinese population. 2456 34

Meningioma is the most frequently occurring type of brain tumor. The present study aimed to conduct a comprehensive bioinformatics analysis of key genes and relevant pathways involved in meningioma, and acquire further insight into the underlying molecular mechanisms. Initially, differentially expressed genes (DEGs) in 47 meningioma samples as compared with 4 normal meninges were identified. Subsequently, these DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. In addition, a protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape. In total, 1,683 DEGs were identified, including 66 upregulated and 1,617 downregulated genes. The GO analysis results revealed that the DEGs were significantly associated with the 'protein binding', 'cytoplasm', 'extracellular matrix (ECM) organization' and 'cell adhesion' terms. The KEGG analysis results demonstrated the significant pathways included 'AGE-RAGE signaling pathway in diabetic complications', 'PI3K-Akt signaling pathway', 'ECM-receptor interaction' and 'cell adhesion molecules'. The top five hub genes obtained from the PPI network were JUN, PIK3R1, FOS, AGT and MYC, and the most enriched KEGG pathways associated with the four obtained modules were 'chemokine signaling pathway', 'cytokine-cytokine receptor interaction', 'allograft rejection', and 'complement and coagulation cascades'. In conclusion, bioinformatics analysis identified a number of potential biomarkers and relevant pathways that may represent key mechanisms involved in the development and progression of meningioma. However, these findings require verification in future experimental studies.
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PMID:Identification of key genes and pathways in meningioma by bioinformatics analysis. 2980 58