Journal of clinical bioinformatics最新文献

{"title":"Proteomic characterization of non-small cell lung cancer in a comprehensive translational thoracic oncology database.","authors":"Mosmi Surati,&nbsp;Matthew Robinson,&nbsp;Suvobroto Nandi,&nbsp;Leonardo Faoro,&nbsp;Carley Demchuk,&nbsp;Cleo E Rolle,&nbsp;Rajani Kanteti,&nbsp;Benjamin D Ferguson,&nbsp;Rifat Hasina,&nbsp;Tara C Gangadhar,&nbsp;April K Salama,&nbsp;Qudsia Arif,&nbsp;Colin Kirchner,&nbsp;Eneida Mendonca,&nbsp;Nicholas Campbell,&nbsp;Suwicha Limvorasak,&nbsp;Victoria Villaflor,&nbsp;Thomas A Hensing,&nbsp;Thomas Krausz,&nbsp;Everett E Vokes,&nbsp;Aliya N Husain,&nbsp;Mark K Ferguson,&nbsp;Theodore G Karrison,&nbsp;Ravi Salgia","doi":"10.1186/2043-9113-1-8","DOIUrl":"https://doi.org/10.1186/2043-9113-1-8","url":null,"abstract":"<p><strong>Background: </strong>In recent years, there has been tremendous growth and interest in translational research, particularly in cancer biology. This area of study clearly establishes the connection between laboratory experimentation and practical human application. Though it is common for laboratory and clinical data regarding patient specimens to be maintained separately, the storage of such heterogeneous data in one database offers many benefits as it may facilitate more rapid accession of data and provide researchers access to greater numbers of tissue samples.</p><p><strong>Description: </strong>The Thoracic Oncology Program Database Project was developed to serve as a repository for well-annotated cancer specimen, clinical, genomic, and proteomic data obtained from tumor tissue studies. The TOPDP is not merely a library-it is a dynamic tool that may be used for data mining and exploratory analysis. Using the example of non-small cell lung cancer cases within the database, this study will demonstrate how clinical data may be combined with proteomic analyses of patient tissue samples in determining the functional relevance of protein over and under expression in this disease. Clinical data for 1323 patients with non-small cell lung cancer has been captured to date. Proteomic studies have been performed on tissue samples from 105 of these patients. These tissues have been analyzed for the expression of 33 different protein biomarkers using tissue microarrays. The expression of 15 potential biomarkers was found to be significantly higher in tumor versus matched normal tissue. Proteins belonging to the receptor tyrosine kinase family were particularly likely to be over expressed in tumor tissues. There was no difference in protein expression across various histologies or stages of non-small cell lung cancer. Though not differentially expressed between tumor and non-tumor tissues, the over expression of the glucocorticoid receptor (GR) was associated improved overall survival. However, this finding is preliminary and warrants further investigation.</p><p><strong>Conclusion: </strong>Though the database project is still under development, the application of such a database has the potential to enhance our understanding of cancer biology and will help researchers to identify targets to modify the course of thoracic malignancies.</p>","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"1 8","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2011-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-1-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40101783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The NFI-Regulome Database: A tool for annotation and analysis of control regions of genes regulated by Nuclear Factor I transcription factors.","authors":"Richard M Gronostajski,&nbsp;Joseph Guaneri,&nbsp;Dong Hyun Lee,&nbsp;Steven M Gallo","doi":"10.1186/2043-9113-1-4","DOIUrl":"https://doi.org/10.1186/2043-9113-1-4","url":null,"abstract":"<p><strong>Background: </strong>Genome annotation plays an essential role in the interpretation and use of genome sequence information. While great strides have been made in the annotation of coding regions of genes, less success has been achieved in the annotation of the regulatory regions of genes, including promoters, enhancers/silencers, and other regulatory elements. One reason for this disparity in annotated information is that coding regions can be assessed using high-throughput techniques such as EST sequencing, while annotation of regulatory regions often requires a gene-by-gene approach.</p><p><strong>Results: </strong>The NFI-Regulome database http://nfiregulome.ccr.buffalo.edu was designed to promote easy annotation of the regulatory regions of genes that contain binding sites for the NFI (Nuclear Factor I) family of transcription factors, using data from the published literature. Binding sites are annotated together with the sequence of the gene, obtained from the UCSC Genome site, and the locations of all binding sites for multiple genes can be displayed in a number of formats designed to facilitate inter-gene comparisons. Classes of genes based on expression pattern, disease involvement, or types of binding sites present can be readily compared in order to assess common \"architectural\" structures in the regulatory regions.</p><p><strong>Conclusions: </strong>The NFI-Regulome database allows rapid display of the relative locations and number of transcription factor binding sites of individual or defined sets of genes that contain binding sites for NFI transcription factors. This database may in the future be expanded into a distributed database structure including other families of transcription factors. Such databases may be useful for identifying common regulatory structures in genes essential for organ development, tissue-specific gene expression or those genes related to specific diseases.</p>","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":" ","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2011-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-1-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29968892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual screening, identification and experimental testing of novel inhibitors of PBEF1/Visfatin/NMPRTase for glioma therapy.","authors":"Nagasuma Chandra, Raghu Bhagavat, Eshita Sharma, P Sreekanthreddy, Kumaravel Somasundaram","doi":"10.1186/2043-9113-1-5","DOIUrl":"10.1186/2043-9113-1-5","url":null,"abstract":"<p><strong>Background: </strong>Pre-B-cell colony enhancing factor 1 gene (PBEF1) encodes nicotinamide phosphoribosyltransferase (NMPRTase), which catalyses the rate limiting step in the salvage pathway of NAD+ metabolism in mammalian cells. PBEF1 transcript and protein levels have been shown to be elevated in glioblastoma and a chemical inhibitor of NMPRTase has been shown to specifically inhibit cancer cells.</p><p><strong>Methods: </strong>Virtual screening using docking was used to screen a library of more than 13,000 chemical compounds. A shortlisted set of compounds were tested for their inhibition activity in vitro by an NMPRTase enzyme assay. Further, the ability of the compounds to inhibit glioma cell proliferation was carried out.</p><p><strong>Results: </strong>Virtual screening resulted in short listing of 34 possible ligands, of which six were tested experimentally, using the NMPRTase enzyme inhibition assay and further with the glioma cell viability assays. Of these, two compounds were found to be significantly efficacious in inhibiting the conversion of nicotinamide to NAD+, and out of which, one compound, 3-amino-2-benzyl-7-nitro-4-(2-quinolyl-)-1,2-dihydroisoquinolin-1-one, was found to inhibit the growth of a PBEF1 over expressing glioma derived cell line U87 as well.</p><p><strong>Conclusions: </strong>Thus, a novel inhibitor has been identified through a structure based drug discovery approach and is further supported by experimental evidence.</p>","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":" ","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2011-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29968890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatic-driven search for metabolic biomarkers in disease.","authors":"Christian Baumgartner, Melanie Osl, Michael Netzer, Daniela Baumgartner","doi":"10.1186/2043-9113-1-2","DOIUrl":"10.1186/2043-9113-1-2","url":null,"abstract":"<p><p> The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.</p>","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":" ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2011-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29968889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}