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Query: UNIPROT:Q86TM3 (cage)
 29,987 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

To catalog data on chromosomal aberrations in cancer derived from emerging molecular cytogenetic techniques and to integrate these data with genome maps, we have established two resources, the NCI and NCBI SKY/M-FISH & CGH Database and the Cancer Chromosomes database. The goal of the former is to allow investigators to submit and analyze clinical and research cytogenetic data. It contains a karyotype parser tool, which automatically converts the ISCN short-form karyotype into an internal representation displayed in detailed form and as a colored ideogram with band overlay, and also has a tool to compare CGH profiles from multiple cases. The Cancer Chromosomes database integrates the SKY/M-FISH & CGH Database with the Mitelman Database of Chromosome Aberrations in Cancer and the Recurrent Chromosome Aberrations in Cancer database. These three datasets can now be searched seamlessly by use of the Entrez search and retrieval system for chromosome aberrations, clinical data, and reference citations. Common diagnoses, anatomic sites, chromosome breakpoints, junctions, numerical and structural abnormalities, and bands gained and lost among selected cases can be compared by use of the "similarity" report. Because the model used for CGH data is a subset of the karyotype data, it is now possible to examine the similarities between CGH results and karyotypes directly. All chromosomal bands are directly linked to the Entrez Map Viewer database, providing integration of cytogenetic data with the sequence assembly. These resources, developed as a part of the Cancer Chromosome Aberration Project (CCAP) initiative, aid the search for new cancer-associated genes and foster insights into the causes and consequences of genetic alterations in cancer.
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PMID:The interactive online SKY/M-FISH & CGH database and the Entrez cancer chromosomes search database: linkage of chromosomal aberrations with the genome sequence. 1593 46

Human cancer is caused by multiple factors, such as genetic predisposition, chronic persistent inflammation, environmental factors, life style, and aging. Dysregulated proliferation, dysregulated adhesion, resistance to apoptosis, resistance to senescence, and resistance to anti-cancer drugs are features of cancer cells. Accumulation of multiple epigenetic changes and genetic alterations of cancer-associated genes during multi-stage carcinogenesis results in more malignant phenotypes. Post-genome science is characterized by omics data related to genome, transcriptome, proteome, metabolome, interactome, and epigenome as well as by high-throughput technology, such as whole-genome tiling oligonucleotide array, array CGH with 32,433 overlapping BAC clones, transcriptome microarray, mass spectrometry, tissue-based expression array, and cell-based transfection array. Benchtop oncology supplies Desktop oncology with large amounts of omics data produced by high-throughput technology. Desktop oncology establishes knowledge on cancer-related biomarkers, such as predisposition markers, diagnostic markers, prognostic markers, and therapeutic markers, by using bioinformatics and human intelligence of experts for data mining and text mining. Bedside oncology applies the knowledge established by Desktop oncology to determine therapeutics for cancer patients. Antibody drugs (Trastuzumab/Herceptin, Cetuximab/Erbitux, Bevacizumab/Avastin, et cetera), small molecule inhibitors for tyrosine kinases (Gefitinib/Iressa, Erlotinib/Tarceva, Imatinib/Gleevec, et cetera), conventional cytotoxic drugs, and anti-hormonal drugs are used for cancer chemotherapy. Biomarker monitoring contributes to therapeutic optional choice and drug dosage determination for cancer patients. Knowledge on biomarkers is feedforwarded from desktop to bedside in the translational research, and then biomarker monitoring is feedbacked from bedside to desktop in the reverse translational research. Desktop oncology is indispensable for cancer research in the post-genome era. Combination of genetic screening for cancer predisposition in the general population and precise selection of therapeutic options during cancer management could contribute to the realization of personalized prevention and to dramatically improve the prognosis of cancer patients in the future.
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PMID:Bioinformatics for cancer management in the post-genome era. 1655 Nov 36

Anogenital cancers are closely associated with human papillomavirus (HPV), and HPV-infected individuals, particularly those with high-grade dysplasias, are at increased risk for cervical and anal cancers. Although genomic instability has been documented in HPV-infected keratinocytes, the full spectrum of genetic changes in HPV-associated lesions has not been fully defined. To address this, we examined an HPV16-transformed foreskin keratinocyte cell line, 16-MT, by GTG-banding, spectral karyotyping (SKY), and array comparative genomic hybridization (array CGH); these analyses revealed multiple numerical, complex, and cryptic chromosome rearrangements. Based on GTG-banding, the 16-MT karyotype was interpreted as 78-83,XXY,+add(1)(p36.3),+3,+4,+5,+5,+7,+8,+i(8)(q10)x2,+10,?der(12),der(13;14)(q10;q10),+15,+16,add(19)(q13.3),+21,+21,-22[cp20]. Multicolor analysis by SKY confirmed and further characterized the anomalies identified by GTG banding. The add(1) was identified as a der(1)(1qter-->1q25::1p36.1-->1qter), the add(19) as a dup(19), and the der(12) interpreted as a der(11) involving a duplication of chromosome 11 material and rearrangement with chromosome 19. In addition, previously unidentified der(9)t(9;22), der(3)t(3;19), and der(4)t(4;9) were noted. The 16-MT cell line showed losses and gains of DNA due to unbalanced translocations and complex rearrangements of regions containing known tumor suppressor genes. Chromosomal changes in these regions might explain the increased risk of cancer associated with HPV. Also, array CGH detected copy-number gains or amplifications of chromosomes 2, 8, 10, and 11 and deletions of chromosomes 3, 4, 11, and 15. These results provide the basis for the identification of candidate oncogenes responsible for cervical and anal cancer in amplified regions, and for putative tumor suppressor genes in commonly deleted regions like 11q22-23. Furthermore, these data represent the first full characterization of the HPV-positive cell line 16-MT.
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PMID:Molecular cytogenetic characterization of human papillomavirus16-transformed foreskin keratinocyte cell line 16-MT. 1677 19

We report on the diagnosis of a 17p13.1 deletion in a 10-year-old boy. The patient presented with mild developmental delay, facial dysmorphism, joint hyperlaxity, pes planus, hypermetropia, hearing loss, and achromic patches following the Blaschko's lines on the right part of the thorax. Chromosome R-banding was normal. Array CGH using a 244 K oligonucleotide array showed a homogeneous de novo 17p13.1 microdeletion of 400 kb involving TP53 and 25 other genes, including genes involved in brain function (EFNB3, FXR2). To our knowledge, six patients presenting with a constitutional 17p13.1 microdeletion involving the TP53 gene have been reported. We discuss the phenotype of this microdeletion and the genetic counseling issues, especially regarding the risk of cancer associated with the deletion of the TP53 gene.
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PMID:17p13.1 microdeletion involving the TP53 gene in a boy presenting with mental retardation but no tumor. 2042 36