BCDB - A database for breast cancer research and information

In pursuit of a better updated source including 'omics' information for breast cancer, Breast Cancer Database (BCDB) has been developed to provide the researcher with the quick overview of the Breast cancer disease and other relevant information. This database comprises of myriad of information about genes involved in breast cancer, its functions and drug molecules which are currently being used in the treatment of breast cancer. The data available in BCDB is retrieved from the biomedical research literature. It facilitates the user to search information on gene, its location in chromosome, functions and its importance in cancer diseases. Broadly, this can be queried by giving gene name, protein name and drug name. This database is platform independent, user friendly and freely accessible through internet. The data present in BCDB is directly linked to other on-line resources such as NCBI, PDB and PubMed. Hence, it can act as a complete web resource comprising gene sequences, drug structures and literature information related to breast cancer, which is not available in any other breast cancer database. AVAILABILITY: The database is freely available at http://122.165.25.137/bioinfo/breastcancerdb/     

Database and interaction network of genes involved in oral cancer: Version II

The oral cancer gene database has been compiled to enable fast retrieval of updated information and role of the genes implicated in oral cancer. The first version of the database with 242 genes was published in Online Journal of Bioinformatics 8(1), 41-44, 2007. In the second version, the database has been enlarged to include 374 genes by adding 132 gene entries. The architecture and format of the database is similar to the earlier version, and includes updated information and external hyperlinks for all the genes. The functional gene interaction network for important biological processes and molecular functions has been rebuilt based on 374 genes using 'String 8.3'. The database is freely available at http://www.actrec.gov.in/OCDB/index.htm and provides the scientist information and external links for the genes involved in oral cancer, interactions between them, and their role in the biology of oral cancer along with clinical relevance.     

An Atlas on genes and chromosomes in oncology and haematology.

The "Atlas of Genetics and Cytogenetics in Oncology and Haematology" http://www.infobiogen.fr/services/chromcancer is a peer-reviewed and free internet database aimed at genes involved in cancer, cytogenetics and clinical entities in cancer, and cancer-prone diseases. It contains concise and updated review articles, a huge portal towards genetics and/or cancer databases, and teaching materials in genetics for the students. This database is made for and by clinicians and researchers, who are encouraged to contribute. The Atlas is part of the genome project. It provides information in cancer epidemiology. It contributes to research, university and post-university teaching, and telemedicine. It contributes to 'meta-medicine', a mediation using new information technology, between the overflowing information provided by the scientific community and the individual practitioner.     

Atlas of Genetics and Cytogenetics in Oncology and Haematology,year 2003

The 'Atlas of Genetics and Cytogenetics in Oncology and Haematology' (http://www.infobiogen.fr/services/chromcancer) contains concise and updated cards on genes involved in cancer, cytogenetics and clinical entities in oncology, and cancer-prone diseases, a portal towards genetics/cancer, and teaching materials in genetics. This database is made for and by researchers and clinicians, who are encouraged to contribute. The Atlas is part of the genome project and it participates in research on cancer epidemiology.     

Oncomine数据库在肿瘤研究中的应用

Oncomine 是目前世界上最大的癌基因芯片数据库和综合数据挖掘平台之一,该数据库整合了GEO、TCGA和已发表文献来源的RNA和DNA-seq数据。数据库目前含有715个基因表达数据集（datasheet）、86 733个人体肿瘤组织和正常组织样本的信息,且有新的数据不断更新。Oncomine 数据库囊括的肿瘤类型有19种,包括：膀胱癌、脑/中枢神经系统肿瘤、乳腺癌、宫颈癌、结直肠癌、食管癌、胃癌、头/颈肿瘤、肾癌、白血病、肝癌、肺癌、淋巴瘤、黑色素瘤、骨髓瘤、卵巢癌、胰腺癌、前列腺癌、肉瘤。本文就如何利用Oncomine数据库,进行肿瘤组织中癌基因表达差异性分析以及基因共表达分析、癌基因在肿瘤组织中的表达及拷贝数分析、多组研究数据集的荟萃分析（meta analysis）、以及癌基因表达与患者生存率关系等进行分析。通过该数据库可以对肿瘤癌基因进行研究前的筛查,有利于发现新的肿瘤生物标记物或治疗靶点,为临床科学研究奠定一定的理论基础。     

A database on cytogenetics in haematology and oncology.

The aim of 'Atlas of Genetics and Cytogenetics in Oncology and Haematology' (http://www.infobiogen.fr/services/chromcancer) is to present summarized information on chromosome abnormalities in cancer, with extensions to genes involved in cancer and to cancer-prone diseases. Information is to be updated. This database is made for and by cytogeneticists, molecular biologists, clinicians in oncology and in haematology, and pathologists.     

APC gene: database of germline and somatic mutations in human tumors and cell lines.

A database (http://perso.curie.fr/tsoussi ) is described, in which over 1000 mutations in the human APC gene of tumors (colon cancer predominantly) are compiled from the literature. It includes both molecular information about the mutations and clinical data about the patients. Software has been designed to analyse all this information in the database.     

Development of predictive models to identify advanced-stage cancer patients in a US healthcare claims database

BackgroundAlthough healthcare databases are a valuable source for real-world oncology data, cancer stage is often lacking. We developed predictive models using claims data to identify metastatic/advanced-stage patients with ovarian cancer, urothelial carcinoma, gastric adenocarcinoma, Merkel cell carcinoma (MCC), and non-small cell lung cancer (NSCLC).MethodsPatients with ≥1 diagnosis of a cancer of interest were identified in the HealthCore Integrated Research Database (HIRD), a United States (US) healthcare database (2010–2016). Data were linked to three US state cancer registries and the HealthCore Integrated Research Environment Oncology database to identify cancer stage. Predictive models were constructed to estimate the probability of metastatic/advanced stage. Predictors available in the HIRD were identified and coefficients estimated by Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation to control overfitting. Classification error rates and receiver operating characteristic curves were used to select probability thresholds for classifying patients as cases of metastatic/advanced cancer.ResultsWe used 2723 ovarian cancer, 6522 urothelial carcinoma, 1441 gastric adenocarcinoma, 109 MCC, and 12,373 NSCLC cases of early and metastatic/advanced cancer to develop predictive models. All models had high discrimination (C > 0.85). At thresholds selected for each model, PPVs were all >0.75: ovarian cancer = 0.95 (95% confidence interval [95% CI]: 0.94–0.96), urothelial carcinoma = 0.78 (95% CI: 0.70–0.86), gastric adenocarcinoma = 0.86 (95% CI: 0.83–0.88), MCC = 0.77 (95% CI 0.68–0.89), and NSCLC = 0.91 (95% CI 0.90 – 0.92).ConclusionPredictive modeling was used to identify five types of metastatic/advanced cancer in a healthcare claims database with greater accuracy than previous methods.     

APRO家族基因在胃癌中的表达及预后意义

目的:基于大数据挖掘分析BTG/Tob抗增殖蛋白家族(anti-proliferativeprotein family,APRO)基因在胃癌组织的表达及其对胃癌患者预后的影响。方法:采用Oncomine数据库分析APRO家族6个成员在胃癌组织中的m RNA表达情况,通过Kaplan-Meier Plotter数据库进行胃癌患者总生存期的分析。结果:相比正常胃组织,BTG2在胃癌组织中呈低表达;BTG3在肠型胃癌组织中呈高表达,而在总体胃癌组织中呈低表达。BTG3低表达的患者总生存期较短;对5-氟尿嘧啶辅助化疗的胃癌患者,低表达BTG2的预后较差。结论:BTG2、BTG3的m RNA表达在胃癌和正常胃组织中有明显差异。BTG3低表达的胃癌患者预后较差;BTG2可能参与调节胃癌患者5-氟尿嘧啶治疗的敏感性。     

Atlas of Genetics and Cytogenetics in Oncology and Haematology, updated

The 'Atlas of Genetics and Cytogenetics in Oncology and Haematology' (http://www.infobiogen.fr/services/chromcancer) is an Internet database aimed at genes involved in cancer, cytogenetics and clinical entities in cancer, and cancer-prone diseases. It presents information in concise and updated reviews (cards) or longer texts (deep insights), a (new) case report section, a huge portal towards genetics and/or cancer databases, and teaching items in genetics for students in medicine and the sciences. This database is made for and by clinicians and researchers in the above-mentioned fields, who are encouraged to contribute. It deals with cancer research, genomics and cytogenomics. It is at the crossroads of research, post-university teaching and telemedicine. The Atlas is available at no cost.     

Consolidation of proteomics data in the Cancer Proteomics database

Cancer is a class of diseases characterized by abnormal cell growth and one of the major reasons for human deaths. Proteins are involved in the molecular mechanisms leading to cancer, furthermore they are affected by anti‐cancer drugs, and protein biomarkers can be used to diagnose certain cancer types. Therefore, it is important to explore the proteomics background of cancer. In this report, we developed the Cancer Proteomics database to re‐interrogate published proteome studies investigating cancer. The database is divided in three sections related to cancer processes, cancer types, and anti‐cancer drugs. Currently, the Cancer Proteomics database contains 9778 entries of 4118 proteins extracted from 143 scientific articles covering all three sections: cell death (cancer process), prostate cancer (cancer type) and platinum‐based anti‐cancer drugs including carboplatin, cisplatin, and oxaliplatin (anti‐cancer drugs). The detailed information extracted from the literature includes basic information about the articles (e.g., PubMed ID, authors, journal name, publication year), information about the samples (type, study/reference, prognosis factor), and the proteomics workflow (Subcellular fractionation, protein, and peptide separation, mass spectrometry, quantification). Useful annotations such as hyperlinks to UniProt and PubMed were included. In addition, many filtering options were established as well as export functions. The database is freely available at http://cancerproteomics.uio.no .     

Repair-FunMap: a functional database of proteins of the DNA repair systems

Repair-FunMap is a functional database of the DNA repair systems. This database contains not only the proteins directly involved in DNA repair, but also the proteins that interact with the DNA repair proteins. A protein interaction network associated with the human DNA repair processes was established according to the functional relationship between proteins in the database. This network represents the current knowledge on the intrinsic signaling pathways related to DNA repair. The Repair-FunMap could become an essential resource center for cancer research, providing clues to understanding the inter-relationship between proteins in the network, and to building scientific models of the DNA repair processes. AVAILABILITY: http://astro.temple.edu/~feng/Servers/BioinformaticServers.htm     

Cell-culture Database: Literature-based reference tool for human and mammalian experimentallybased cell culture applications

Cultivation of primary cells is essential for biotechnological research and viral vaccine production. Significant advances in cell and tissue culture, more specifically, advances in the transfection and transduction of human and mammalian cells, has directly led to giant leaps forward in fields such as cancer research, genetics, and public health. At the same time, a corresponding increase has been seen in available cell culture related literature. Often times, due to the sheer number and degree of variability of available literature, it is a challenge to find specific, yet practical cell culture related information.To respond to this rising tide of information, a practical, user-friendly database containing cell-lines, plasmids, vectors, selection agents, concentrations and media was created. The database currently consists of over 3,900 cell lines (Human and Mammalian) and 1,900 plasmids/vectors collected from 2,700 pieces of published literature. The database is continually being expanded and it is hoped that through the continual addition of unique data, the database can further serve and enrich the work of cell and molecular biologists, life-science professionals, and the worldwide scientific community at large. AVAILABILITY: The database is available for free at http://cell-lines.toku-e.com/     

基于网络的肾癌随访系统的建立与应用

目的:肾癌是泌尿系常见肿瘤,需长期随访,传统统计及随访方式已逐渐不能适应现代临床及科研需求,我们尝试建立基于网络的肾癌随访系统,满足临床实际工作和科研需求。方法:以Microsoft Office Access 2007软件构建数据库管理系统,分为资料录入模块、检索分析模块、网络随访模块,选取我院2008.5至2012.12收治的132例肾癌患者,将资料录入已建成的数据库,利用网络随访辅助传统随访方式进行随访。结果:构建的肾癌随访数据库,能够实现授权账户登录,检索、分析、肿瘤自动评级等功能,可根据不同检索条件筛选出适合病历,在传统随访方式的基础上联合应用网络随访方式可提高随访效率及随访率,并能获得更详实的随访资料。结论:基于网络的肾癌随访系统操作简便,能够实现无纸化管理,数据详实,检索便捷,为科学制定治疗方案提供帮助,可一定程度上提高随访率及随访效率,降低失访率和患者就医成本。     

Investigating core genetic-and-epigenetic cell cycle networks for stemness and carcinogenic mechanisms,and cancer drug design using big database mining and genome-wide next-generation sequencing data

Recent studies have demonstrated that cell cycle plays a central role in development and carcinogenesis. Thus, the use of big databases and genome-wide high-throughput data to unravel the genetic and epigenetic mechanisms underlying cell cycle progression in stem cells and cancer cells is a matter of considerable interest.

Real genetic-and-epigenetic cell cycle networks (GECNs) of embryonic stem cells (ESCs) and HeLa cancer cells were constructed by applying system modeling, system identification, and big database mining to genome-wide next-generation sequencing data. Real GECNs were then reduced to core GECNs of HeLa cells and ESCs by applying principal genome-wide network projection. In this study, we investigated potential carcinogenic and stemness mechanisms for systems cancer drug design by identifying common core and specific GECNs between HeLa cells and ESCs. Integrating drug database information with the specific GECNs of HeLa cells could lead to identification of multiple drugs for cervical cancer treatment with minimal side-effects on the genes in the common core. We found that dysregulation of miR-29C, miR-34A, miR-98, and miR-215; and methylation of ANKRD1, ARID5B, CDCA2, PIF1, STAMBPL1, TROAP, ZNF165, and HIST1H2AJ in HeLa cells could result in cell proliferation and anti-apoptosis through NFκB, TGF-β, and PI3K pathways. We also identified 3 drugs, methotrexate, quercetin, and mimosine, which repressed the activated cell cycle genes, ARID5B, STK17B, and CCL2, in HeLa cells with minimal side-effects.     

The Androgen Receptor Gene Mutations Database.

The current version of the androgen receptor (AR) gene mutations database is described. The total number of reported mutations has risen from 272 to 309 in the past year. We have expanded the database: (i) by giving each entry an accession number; (ii) by adding information on the length of polymorphic polyglutamine (polyGln) and polyglycine (polyGly) tracts in exon 1; (iii) by adding information on large gene deletions; (iv) by providing a direct link with a completely searchable database (courtesy EMBL-European Bioinformatics Institute). The addition of the exon 1 polymorphisms is discussed in light of their possible relevance as markers for predisposition to prostate or breast cancer. The database is also available on the internet (http://www.mcgill. ca/androgendb/ ), from EMBL-European Bioinformatics Institute (ftp. ebi.ac.uk/pub/databases/androgen ), or as a Macintosh FilemakerPro or Word file (MC33@musica.mcgill.ca).     

CMS: A Web-Based System for Visualization and Analysis of Genome-Wide Methylation Data of Human Cancers

Background

DNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters.

Methodology/Principal Findings

Cancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework.

Conclusions/Significance

CMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible at: http://cbbiweb.uthscsa.edu/KMethylomes/.