Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0376358 (prostate cancer)
 59,338 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

BACKGROUND The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases. MATERIAL AND METHODS IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein-protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively. RESULTS A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes. CONCLUSIONS The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.
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PMID:Underlying Mechanism of Insulin Resistance: A Bioinformatics Analysis Based on Validated Related-Genes from Public Disease Databases. 3265 53

The TMPRSS2-ERG fusion is the most common genomic rearrangement in human prostate cancer. However, in established adenocarcinoma, it is unknown how the ERG oncogene promotes a cancerous phenotype and maintains downstream androgen receptor (AR) signaling pathways. In this study, we utilized a murine prostate organoid system to explore the effects of ERG on tumorigenesis and determined the mechanism underlying prostate cancer dependence on ERG. Prostate organoids lacking PTEN and overexpressing ERG (Pten-/- R26-ERG) faithfully recapitulated distinct stages of prostate cancer disease progression. In this model, deletion of ERG significantly dampened AR-dependent gene expression. While ERG was able to reprogram the AR cistrome in the process of prostate carcinogenesis, ERG knockout in established prostate cancer organoids did not drastically alter AR binding, H3K27ac enhancer, or open chromatin profiles at these reprogrammed sites. Proteomic analysis of DNA-bound AR complexes demonstrated that ERG deletion causes a loss of recruitment of critical AR coregulators and basal transcriptional machinery, including NCOA3 and RNA polymerase II, but does not alter AR binding itself. Together, these data reveal a novel mechanism of ERG oncogene addiction in prostate cancer, whereby ERG facilitates AR signaling by maintaining coregulator complexes at AR bound sites across the genome. SIGNIFICANCE: These findings exploit murine organoid models to uncover the mechanism of ERG-mediated tumorigenesis and subsequent oncogenic dependencies in prostate cancer.
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PMID:ERG-Mediated Coregulator Complex Formation Maintains Androgen Receptor Signaling in Prostate Cancer. 3293 23


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