Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:Q86TM3 (cage)
 29,987 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

An evolutionary recombination hotspot around the GSDML-GSDM locus at human chromosome 17q21 is closely linked to an oncogenomic recombination hotspot around the PPP1R1B-STARD3-TCAP-PNMT-PERLD1 (MGC9753)-ERBB2-C17orf37 (MGC14832)-GRB7 locus at human chromosome 17q12. Here, we identified DFNA5L (GSDMDC1) gene related to GSDM and GSDML genes by using bioinformatics. Human DFNA5L gene at chromosome 8q24.3 was linked to ZC3HDC3, PP3856, EEF1D, and TIGD5 genes. NM_024736.4 (AK127941.1), AK022212.1, BC008904.2, and BC069000.1 cDNAs were derived from human DFNA5L gene. BC008904.2 was the representative human DFNA5L cDNA, while NM_024736.4 was an aberrant human DFNA5L cDNA with frame shifts due to the retention of introns 1, 3, 4, 5 and 8. Human DFNA5L mRNA was expressed in placenta, pancreatic cancer, prostate cancer, melanoma, salivary gland tumor, Jarkat T cells, and Ramos B cells. Complete coding sequence of rat Dfna5l cDNA was determined by assembling 11 exons of rat Dfna5l gene within AC120830.4 genome sequence, and that of mouse Dfna5l cDNA was derived from 1810036L03 (NM_026960.1). Exon-intron boundaries were conserved among human DFNA5L and rodent Dfna5l genes. Human DFNA5L (484 aa) showed 59.5% total-amino-acid identity with rat Dfna5l (488 aa), and 58.7% total-amino-acid identity with mouse Dfna5l (487 aa). DFNA5L orthologs were DNFA5 (GSDM) domain containing DFNA5 DC or GSDMDC proteins with Coiled-coil and Leucine zipper domains. Human DFNA5L, GSDM, GSDML, MLZE, DFNA5 and their mammalian orthologs were found to constitute the DFNA5 DC (GSDMDC) family. Because DFNA5 and MLZE are cancer-associated genes, DFNA5L, GSDM, and GSDML are predicted cancer associated genes.
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PMID:Identification and characterization of human DFNA5L, mouse Dfna5l, and rat Dfna5l genes in silico. 1528 81

HER2/neu is associated with poorer clinical outcome in breast cancer. Expression patterns of co-localised cancer-associated genes at 17q12-21 were examined using RT-PCR. The study group consisted of a 96-patient cohort. Relative quantity of mRNA expression was calculated using the comparative cycle threshold method and Qbase software. Results were analysed to detect expression patterns among the genes, and to identify associations between expression levels and clinical data. Levels of HER2/neu correlated with those of GRB7 (r=0.551, p<0.001), RARA (r=0.391, p<0.001), RPL19 (r=0.549, p<0.001) and LASP1 (r=0.399, p<0.001). GRB7 was significantly inversely associated with improved DFS at 60 months (p=0.036). RARA levels were greater in HER2/neu-positive as opposed to HER2/neu-negative patients (p=0.021); levels were significantly higher in ER-positive patients, relative to those who were ER-negative (p=0.003). Levels of RPL19 were significantly higher in the HER2/neu-overexpressing (p=0.010) and luminal B subtypes (p=0.007). LASP1 levels were higher in those patients who had been classified clinically as HER2/neu-positive (p=0.004). This study reaffirms the correlation between HER2/neu and the co-localised LASP1 and GRB7; the latter target may hold additional significance in addition to being a surrogate marker for HER2/neu expression. The relationship identified between RARA and ER-positivity may herald an avenue for targeted therapy of these tumours.
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PMID:Expression levels of HER2/neu and those of collocated genes at 17q12-21, in breast cancer. 2255 11

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Here we describe a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets. We carried out GISTIC2 analysis of TCGA datasets spanning 16 cancer subtypes and identified 486 genes that were amplified in two or more datasets. The list was narrowed to 75 cancer-associated genes with potential "druggable" properties. The majority of the genes were localized to 14 amplicons spread across the genome. To identify potential cancer driver genes, we analyzed gene copy number and mRNA expression data from individual patient samples and identified 42 putative cancer driver genes linked to diverse oncogenic processes. Oncogenic activity was further validated by siRNA/shRNA knockdown and by referencing the Project Achilles datasets. The amplified genes represented a number of gene families, including epigenetic regulators, cell cycle-associated genes, DNA damage response/repair genes, metabolic regulators, and genes linked to the Wnt, Notch, Hedgehog, JAK/STAT, NF-KB and MAPK signaling pathways. Among the 42 putative driver genes were known driver genes, such as EGFR, ERBB2 and PIK3CA. Wild-type KRAS was amplified in several cancer types, and KRAS-amplified cancer cell lines were most sensitive to KRAS shRNA, suggesting that KRAS amplification was an independent oncogenic event. A number of MAP kinase adapters were co-amplified with their receptor tyrosine kinases, such as the FGFR adapter FRS2 and the EGFR family adapters GRB2 and GRB7. The ubiquitin-like ligase DCUN1D1 and the histone methyltransferase NSD3 were also identified as novel putative cancer driver genes. We discuss the patient tailoring implications for existing cancer drug targets and we further discuss potential novel opportunities for drug discovery efforts.
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PMID:Identification of druggable cancer driver genes amplified across TCGA datasets. 2487 71