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Tang J, Fu J, Wang Y, Li B, Li Y, Yang Q, Cui X, Hong J, Li X, Chen Y, Xue W, Zhu F. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief Bioinform 2021; 21:621-636. [PMID: 30649171 PMCID: PMC7299298 DOI: 10.1093/bib/bby127] [Citation(s) in RCA: 142] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 11/19/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022] Open
Abstract
Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Bo Li
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yinghong Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Xuejiao Cui
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiaofeng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
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2
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Pradhan S, Das S, Singh AK, Das C, Basu A, Majumder PP, Biswas NK. dbGENVOC: database of GENomic Variants of Oral Cancer, with special reference to India. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6287646. [PMID: 34048545 PMCID: PMC8163239 DOI: 10.1093/database/baab034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 05/12/2021] [Accepted: 05/15/2021] [Indexed: 11/18/2022]
Abstract
Oral cancer is highly prevalent in India and is the most frequent cancer type among Indian males. It is also very common in southeast Asia. India has participated in the International Cancer Genome Consortium (ICGC) and some national initiatives to generate large-scale genomic data on oral cancer patients and analyze to identify associations and systematically catalog the associated variants. We have now created an open, web-accessible database of these variants found significantly associated with Indian oral cancer patients, with a user-friendly interface to enable easy mining. We have value added to this database by including relevant data collated from various sources on other global populations, thereby providing opportunities of comparative geographical and/or ethnic analyses. Currently, no other database of similar nature is available on oral cancer. We have developed Database of GENomic Variants of Oral Cancer, a browsable online database framework for storage, retrieval and analysis of large-scale data on genomic variants and make it freely accessible to the scientific community. Presently, the web-accessible database allows potential users to mine data on ∼24 million clinically relevant somatic and germline variants derived from exomes (n = 100) and whole genomes (n = 5) of Indian oral cancer patients; all generated by us. Variant data from The Cancer Genome Atlas and data manually curated from peer-reviewed publications were also incorporated into the database for comparative analyses. It allows users to query the database by a single gene, multiple genes, multiple variant sites, genomic region, patient ID and pathway identities. Database URL: http://research.nibmg.ac.in/dbcares/dbgenvoc/
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Affiliation(s)
- Sanchari Pradhan
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Subrata Das
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Animesh K Singh
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Chitrarpita Das
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Analabha Basu
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Partha P Majumder
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India.,Human Genetics Unit, Indian Statistical Institute, Kolkata, West Bengal 700108, India
| | - Nidhan K Biswas
- Human Genetics Unit, National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
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3
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Qin G, Liu Z, Xie L. Multiple Omics Data Integration. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11508-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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4
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Wang Y, Zhang S, Li F, Zhou Y, Zhang Y, Wang Z, Zhang R, Zhu J, Ren Y, Tan Y, Qin C, Li Y, Li X, Chen Y, Zhu F. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res 2020; 48:D1031-D1041. [PMID: 31691823 PMCID: PMC7145558 DOI: 10.1093/nar/gkz981] [Citation(s) in RCA: 378] [Impact Index Per Article: 94.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 10/10/2019] [Accepted: 10/12/2019] [Indexed: 12/12/2022] Open
Abstract
Knowledge of therapeutic targets and early drug candidates is useful for improved drug discovery. In particular, information about target regulators and the patented therapeutic agents facilitates research regarding druggability, systems pharmacology, new trends, molecular landscapes, and the development of drug discovery tools. To complement other databases, we constructed the Therapeutic Target Database (TTD) with expanded information about (i) target-regulating microRNAs and transcription factors, (ii) target-interacting proteins, and (iii) patented agents and their targets (structures and experimental activity values if available), which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes. We also updated the TTD with the recently released International Classification of Diseases ICD-11 codes and additional sets of successful, clinical trial, and literature-reported targets that emerged since the last update. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. In case of possible web connectivity issues, two mirror sites of TTD are also constructed (http://db.idrblab.org/ttd/ and http://db.idrblab.net/ttd/).
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Affiliation(s)
- Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Ying Zhou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Zhengwen Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Runyuan Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Jiang Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yuxiang Ren
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Ying Tan
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Chu Qin
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore 117543, Singapore
| | - Yinghong Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xiaoxu Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore 117543, Singapore
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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5
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Yang Q, Zhang Y, Cui H, Chen L, Zhao Y, Lin Y, Zhang M, Xie L. dbDEPC 3.0: the database of differentially expressed proteins in human cancer with multi-level annotation and drug indication. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4904121. [PMID: 29688359 PMCID: PMC5824774 DOI: 10.1093/database/bay015] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 01/22/2018] [Indexed: 12/11/2022]
Abstract
Proteins are major effectors of biological functions, and differentially expressed proteins (DEPs) are widely reported as biomarkers in pathological mechanism, prognosis prediction as well as treatment targeting in cancer research. High-throughput technology of mass spectrometry (MS) has identified large amounts of DEPs in human cancers. Through mining published researches with detailed experiment information, dbDEPC was the first database aimed to provide a systematic resource for the storage and query of the DEPs generated by MS in cancer research. It was updated to dbDEPC 2.0 in 2012. Here, we provide another updated version of dbDEPC, with improvement of database contents and enhanced web interface. The current version of dbDEPC 3.0 contains 11 669 unique DEPs in 26 different cancer types. Multi-level annotations of DEPs have been firstly introduced this time, including cancer-related peptide amino acid variations, post-translational modifications and drug information. Moreover, these multi-level annotations can be displayed in the biological networks, which can benefit integrative analysis. Finally, an online enrichment analysis tool has been developed, to support a KEGG enrichment analysis and to browse the relationship among interested protein list and known DEPs in KEGG pathways. In summary, dbDEPC 3.0 provides a comprehensive resource for accessing integrated and highly annotated DEPs in human cancer. Database URL: https://www.scbit.org/dbdepc3/index.php
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Affiliation(s)
- Qingmin Yang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.,Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Yuqi Zhang
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.,School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology
| | - Hui Cui
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Lanming Chen
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.,Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China
| | - Yong Zhao
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.,Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China.,Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, Shanghai 201306, China
| | - Yong Lin
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology
| | - Menghuan Zhang
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
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6
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Doytchinova IA, Flower DR. In silico prediction of cancer immunogens: current state of the art. BMC Immunol 2018; 19:11. [PMID: 29544447 PMCID: PMC5856276 DOI: 10.1186/s12865-018-0248-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 03/06/2018] [Indexed: 01/22/2023] Open
Abstract
Cancer kills 8 million annually worldwide. Although survival rates in prevalent cancers continue to increase, many cancers have no effective treatment, prompting the search for new and improved protocols. Immunotherapy is a new and exciting addition to the anti-cancer arsenal. The successful and accurate identification of aberrant host proteins acting as antigens for vaccination and immunotherapy is a key aspiration for both experimental and computational research. Here we describe key elements of in silico prediction, including databases of cancer antigens and bleeding-edge methodology for their prediction. We also highlight the role dendritic cell vaccines can play and how they can act as delivery mechanisms for epitope ensemble vaccines. Immunoinformatics can help streamline the discovery and utility of Cancer Immunogens.
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Affiliation(s)
- Irini A. Doytchinova
- Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st, 1000 Sofia, Bulgaria
| | - Darren R. Flower
- School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET UK
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7
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Gupta S, Dingerdissen H, Ross KE, Hu Y, Wu CH, Mazumder R, Vijay-Shanker K. DEXTER: Disease-Expression Relation Extraction from Text. Database (Oxford) 2018; 2018:5025486. [PMID: 29860481 PMCID: PMC6007211 DOI: 10.1093/database/bay045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 04/02/2018] [Accepted: 04/19/2018] [Indexed: 01/23/2023]
Abstract
Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.
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Affiliation(s)
- Samir Gupta
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Avenue, Newark, DE 19716, USA
| | - Hayley Dingerdissen
- Department of Biochemistry and Molecular Medicine, The George Washington University, Ross Hall, 2300 Eye Street N.W., Washington, DC 20037, USA
| | - Karen E Ross
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven St. NW, Suite 1200 Washington, DC 20007, USA
| | - Yu Hu
- Department of Biochemistry and Molecular Medicine, The George Washington University, Ross Hall, 2300 Eye Street N.W., Washington, DC 20037, USA
| | - Cathy H Wu
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Avenue, Newark, DE 19716, USA
- Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Suite 205 Newark, DE 19711, USA
| | - Raja Mazumder
- Department of Biochemistry and Molecular Medicine, The George Washington University, Ross Hall, 2300 Eye Street N.W., Washington, DC 20037, USA
| | - K Vijay-Shanker
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Avenue, Newark, DE 19716, USA
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8
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Sarode GS, Sarode SC, Maniyar N, Anand R, Patil S. Oral cancer databases: A comprehensive review. J Oral Pathol Med 2017; 47:547-556. [PMID: 29193424 DOI: 10.1111/jop.12667] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/23/2017] [Indexed: 01/14/2023]
Abstract
Cancer database is a systematic collection and analysis of information on various human cancers at genomic and molecular level that can be utilized to understand various steps in carcinogenesis and for therapeutic advancement in cancer field. Oral cancer is one of the leading causes of morbidity and mortality all over the world. The current research efforts in this field are aimed at cancer etiology and therapy. Advanced genomic technologies including microarrays, proteomics, transcrpitomics, and gene sequencing development have culminated in generation of extensive data and subjection of several genes and microRNAs that are distinctively expressed and this information is stored in the form of various databases. Extensive data from various resources have brought the need for collaboration and data sharing to make effective use of this new knowledge. The current review provides comprehensive information of various publicly accessible databases that contain information pertinent to oral squamous cell carcinoma (OSCC) and databases designed exclusively for OSCC. The databases discussed in this paper are Protein-Coding Gene Databases and microRNA Databases. This paper also describes gene overlap in various databases, which will help researchers to reduce redundancy and focus on only those genes, which are common to more than one databases. We hope such introduction will promote awareness and facilitate the usage of these resources in the cancer research community, and researchers can explore the molecular mechanisms involved in the development of cancer, which can help in subsequent crafting of therapeutic strategies.
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Affiliation(s)
- Gargi S Sarode
- Department of Oral Pathology and Microbiology, Dr. D. Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, Maharashtra, India
| | - Sachin C Sarode
- Department of Oral Pathology and Microbiology, Dr. D. Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, Maharashtra, India
| | - Nikunj Maniyar
- Department of Oral Pathology and Microbiology, Dr. D. Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, Maharashtra, India
| | - Rahul Anand
- Department of Oral Pathology and Microbiology, Dr. D. Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, Maharashtra, India
| | - Shankargouda Patil
- Division of Oral Pathology, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Jazan University, Jazan, Saudi Arabia
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9
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Xu J, Teng IT, Zhang L, Delgado S, Champanhac C, Cansiz S, Wu C, Shan H, Tan W. Molecular Recognition of Human Liver Cancer Cells Using DNA Aptamers Generated via Cell-SELEX. PLoS One 2015; 10:e0125863. [PMID: 25938802 PMCID: PMC4418664 DOI: 10.1371/journal.pone.0125863] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 03/25/2015] [Indexed: 12/14/2022] Open
Abstract
Most clinical cases of liver cancer cannot be diagnosed until they have evolved to an advanced stage, thus resulting in high mortality. It is well recognized that the implementation of early detection methods and the development of targeted therapies for liver cancer are essential to reducing the high mortality rates associated with this disease. To achieve these goals, molecular probes capable of recognizing liver cancer cell-specific targets are needed. Here we describe a panel of aptamers able to distinguish hepatocarcinoma from normal liver cells. The aptamers, which were selected by cell-based SELEX (Systematic Evolution of Ligands by Exponential Enrichment), have Kd values in the range of 64-349 nM toward the target human hepatoma cell HepG2, and also recognize ovarian cancer cells and lung adenocarcinoma. The proteinase treatment experiment indicated that all aptamers could recognize target HepG2 cells through surface proteins. This outcome suggested that these aptamers could be used as potential probes for further research in cancer studies, such as developing early detection assays, targeted therapies, and imaging agents, as well as for the investigation of common membrane proteins in these distinguishable cancers.
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Affiliation(s)
- Jiehua Xu
- Department of Nuclear Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Department of Chemistry, Department of Biochemistry and Molecular Biology and Department of Physiology and Functional Genomics, Center for Research at the Bio/Nano Interface, Health Cancer Center, UF Genetics Institute and McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
| | - I-Ting Teng
- Department of Chemistry, Department of Biochemistry and Molecular Biology and Department of Physiology and Functional Genomics, Center for Research at the Bio/Nano Interface, Health Cancer Center, UF Genetics Institute and McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
| | - Liqin Zhang
- Department of Chemistry, Department of Biochemistry and Molecular Biology and Department of Physiology and Functional Genomics, Center for Research at the Bio/Nano Interface, Health Cancer Center, UF Genetics Institute and McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
| | - Stefanie Delgado
- Department of Chemistry, Department of Biochemistry and Molecular Biology and Department of Physiology and Functional Genomics, Center for Research at the Bio/Nano Interface, Health Cancer Center, UF Genetics Institute and McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
| | - Carole Champanhac
- Department of Chemistry, Department of Biochemistry and Molecular Biology and Department of Physiology and Functional Genomics, Center for Research at the Bio/Nano Interface, Health Cancer Center, UF Genetics Institute and McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
| | - Sena Cansiz
- Department of Chemistry, Department of Biochemistry and Molecular Biology and Department of Physiology and Functional Genomics, Center for Research at the Bio/Nano Interface, Health Cancer Center, UF Genetics Institute and McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
| | - Cuichen Wu
- Department of Chemistry, Department of Biochemistry and Molecular Biology and Department of Physiology and Functional Genomics, Center for Research at the Bio/Nano Interface, Health Cancer Center, UF Genetics Institute and McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
| | - Hong Shan
- Interventional Radiology Institute, Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Weihong Tan
- Department of Chemistry, Department of Biochemistry and Molecular Biology and Department of Physiology and Functional Genomics, Center for Research at the Bio/Nano Interface, Health Cancer Center, UF Genetics Institute and McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
- * E-mail:
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10
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Pavlopoulou A, Spandidos DA, Michalopoulos I. Human cancer databases (review). Oncol Rep 2014; 33:3-18. [PMID: 25369839 PMCID: PMC4254674 DOI: 10.3892/or.2014.3579] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 10/31/2014] [Indexed: 12/20/2022] Open
Abstract
Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.
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Affiliation(s)
- Athanasia Pavlopoulou
- Center of Systems Biology, Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece
| | - Demetrios A Spandidos
- Laboratory of Clinical Virology, Medical School, University of Crete, Heraklion 71003, Crete, Greece
| | - Ioannis Michalopoulos
- Center of Systems Biology, Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece
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11
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Zhao W, Yang Z, Liu X, Tian Q, Lv Y, Liang Y, Li C, Gao X, Chen L. Identification of α1-antitrypsin as a potential prognostic biomarker for advanced nonsmall cell lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors by proteomic analysis. J Int Med Res 2013; 41:573-83. [PMID: 23613495 DOI: 10.1177/0300060513476582] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
OBJECTIVE This retrospective study attempted to identify serum biomarkers that could help to indicate treatment response in advanced nonsmall-cell lung cancer (NSCLC) patients receiving epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) treatment. METHODS Two-dimensional fluorescence difference gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry were used to identify proteins expressed in serum samples from NSCLC patients with long (>6-month) progression-free survival (PFS) periods, following EGFR-TKI treatment. RESULTS Serum amyloid P component (APCS), α1-antitrypsin (AAT), fibrinogen-α (FGA), keratin type I cytoskeletal 10 (KRT10) and serotransferrin (TF) expression differed between samples taken from 18 patients before treatment (baseline) and when progressive disease (PD) was observed, during treatment. Changes in AAT, KRT10 and APCS levels were validated by Western blot analysis in the sample pool; findings were further validated by Western blot analysis in a random sample of four patients. These proteins were also present in serum samples obtained from the same patients at the partial response (PR) timepoint during EGFR-TKI treatment. AAT was upregulated at PD compared with baseline, but downregulated during the PR phase. CONCLUSION These observations suggest that AAT could be used as a serological biomarker for predicting the utility of EGFR-TKI treatment for advanced NSCLC.
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Affiliation(s)
- Wei Zhao
- Respiratory Institute, People's Liberation Army General Hospital, Beijing, China
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12
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Charoentong P, Angelova M, Efremova M, Gallasch R, Hackl H, Galon J, Trajanoski Z. Bioinformatics for cancer immunology and immunotherapy. Cancer Immunol Immunother 2012; 61:1885-903. [PMID: 22986455 PMCID: PMC3493665 DOI: 10.1007/s00262-012-1354-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Accepted: 09/04/2012] [Indexed: 01/24/2023]
Abstract
Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.
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Affiliation(s)
- Pornpimol Charoentong
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Mihaela Angelova
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Mirjana Efremova
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Ralf Gallasch
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Hubert Hackl
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
| | - Jerome Galon
- INSERM U872, Integrative Cancer Immunology Laboratory, Paris, France
| | - Zlatko Trajanoski
- Biocenter, Division of Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria
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13
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Lechner M, Höhn V, Brauner B, Dunger I, Fobo G, Frishman G, Montrone C, Kastenmüller G, Waegele B, Ruepp A. CIDeR: multifactorial interaction networks in human diseases. Genome Biol 2012; 13:R62. [PMID: 22809392 PMCID: PMC3491383 DOI: 10.1186/gb-2012-13-7-r62] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2012] [Accepted: 07/18/2012] [Indexed: 12/12/2022] Open
Abstract
The pathobiology of common diseases is influenced by heterogeneous factors interacting in complex networks. CIDeR http://mips.helmholtz-muenchen.de/cider/ is a publicly available, manually curated, integrative database of metabolic and neurological disorders. The resource provides structured information on 18,813 experimentally validated interactions between molecules, bioprocesses and environmental factors extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make CIDeR a versatile knowledge base for biologists, analysis of large-scale data and systems biology approaches.
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Yu J, Xing X, Zeng L, Sun J, Li W, Sun H, He Y, Li J, Zhang G, Wang C, Li Y, Xie L. SyStemCell: a database populated with multiple levels of experimental data from stem cell differentiation research. PLoS One 2012; 7:e35230. [PMID: 22807998 PMCID: PMC3396617 DOI: 10.1371/journal.pone.0035230] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2010] [Accepted: 03/13/2012] [Indexed: 11/18/2022] Open
Abstract
Elucidation of the mechanisms of stem cell differentiation is of great scientific interest. Increasing evidence suggests that stem cell differentiation involves changes at multiple levels of biological regulation, which together orchestrate the complex differentiation process; many related studies have been performed to investigate the various levels of regulation. The resulting valuable data, however, remain scattered. Most of the current stem cell-relevant databases focus on a single level of regulation (mRNA expression) from limited stem cell types; thus, a unifying resource would be of great value to compile the multiple levels of research data available. Here we present a database for this purpose, SyStemCell, deposited with multi-level experimental data from stem cell research. The database currently covers seven levels of stem cell differentiation-associated regulatory mechanisms, including DNA CpG 5-hydroxymethylcytosine/methylation, histone modification, transcript products, microRNA-based regulation, protein products, phosphorylation proteins and transcription factor regulation, all of which have been curated from 285 peer-reviewed publications selected from PubMed. The database contains 43,434 genes, recorded as 942,221 gene entries, for four organisms (Homo sapiens, Mus musculus, Rattus norvegicus, and Macaca mulatta) and various stem cell sources (e.g., embryonic stem cells, neural stem cells and induced pluripotent stem cells). Data in SyStemCell can be queried by Entrez gene ID, symbol, alias, or browsed by specific stem cell type at each level of genetic regulation. An online analysis tool is integrated to assist researchers to mine potential relationships among different regulations, and the potential usage of the database is demonstrated by three case studies. SyStemCell is the first database to bridge multi-level experimental information of stem cell studies, which can become an important reference resource for stem cell researchers. The database is available at http://lifecenter.sgst.cn/SyStemCell/.
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Affiliation(s)
- Jian Yu
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Xiaobin Xing
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lingyao Zeng
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Tongji University, Shanghai, China
| | - Jiehuan Sun
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Huazhong Science and Technology University, Wuhan, Hubei, China
| | - Wei Li
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Huazhong Science and Technology University, Wuhan, Hubei, China
| | - Han Sun
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ying He
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jing Li
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Huazhong Science and Technology University, Wuhan, Hubei, China
| | - Guoqing Zhang
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Chuan Wang
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Yixue Li
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- * E-mail: (LX); (YL)
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai, China
- * E-mail: (LX); (YL)
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15
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He Y, Zhang M, Ju Y, Yu Z, Lv D, Sun H, Yuan W, He F, Zhang J, Li H, Li J, Wang-Sattler R, Li Y, Zhang G, Xie L. dbDEPC 2.0: updated database of differentially expressed proteins in human cancers. Nucleic Acids Res 2012; 40:D964-71. [PMID: 22096234 PMCID: PMC3245147 DOI: 10.1093/nar/gkr936] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2011] [Revised: 10/10/2011] [Accepted: 10/11/2011] [Indexed: 01/07/2023] Open
Abstract
A large amount of differentially expressed proteins (DEPs) have been identified in various cancer proteomics experiments, curation and annotation of these proteins are important in deciphering their roles in oncogenesis and tumor progression, and may further help to discover potential protein biomarkers for clinical applications. In 2009, we published the first database of DEPs in human cancers (dbDEPCs). In this updated version of 2011, dbDEPC 2.0 has more than doubly expanded to over 4000 protein entries, curated from 331 experiments across 20 types of human cancers. This resource allows researchers to search whether their interested proteins have been reported changing in certain cancers, to compare their own proteomic discovery with previous studies, to picture selected protein expression heatmap across multiple cancers and to relate protein expression changes with aberrance in other genetic level. New important developments include addition of experiment design information, advanced filter tools for customer-specified analysis and a network analysis tool. We expect dbDEPC 2.0 to be a much more powerful tool than it was in its first release and can serve as reference to both proteomics and cancer researchers. dbDEPC 2.0 is available at http://lifecenter.sgst.cn/dbdepc/index.do.
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Affiliation(s)
- Ying He
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Menghuan Zhang
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Yuanhu Ju
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Zhonghao Yu
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Daqing Lv
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Han Sun
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Weilan Yuan
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Fei He
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Jianshe Zhang
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Hong Li
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Jing Li
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Rui Wang-Sattler
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Yixue Li
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Guoqing Zhang
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
| | - Lu Xie
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai 200031, Shanghai Center for Bioinformation Technology, Shanghai 200235, Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai 200240, P. R. China, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany and Biomedical Engineering for School of Life Sciences and Technology, Tongji University, Shanghai 200092, P. R. of China
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Lee L, Wang K, Li G, Xie Z, Wang Y, Xu J, Sun S, Pocalyko D, Bhak J, Kim C, Lee KH, Jang YJ, Yeom YI, Yoo HS, Hwang S. Liverome: a curated database of liver cancer-related gene signatures with self-contained context information. BMC Genomics 2011; 12 Suppl 3:S3. [PMID: 22369201 PMCID: PMC3333186 DOI: 10.1186/1471-2164-12-s3-s3] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. A number of molecular profiling studies have investigated the changes in gene and protein expression that are associated with various clinicopathological characteristics of HCC and generated a wealth of scattered information, usually in the form of gene signature tables. A database of the published HCC gene signatures would be useful to liver cancer researchers seeking to retrieve existing differential expression information on a candidate gene and to make comparisons between signatures for prioritization of common genes. A challenge in constructing such database is that a direct import of the signatures as appeared in articles would lead to a loss or ambiguity of their context information that is essential for a correct biological interpretation of a gene’s expression change. This challenge arises because designation of compared sample groups is most often abbreviated, ad hoc, or even missing from published signature tables. Without manual curation, the context information becomes lost, leading to uninformative database contents. Although several databases of gene signatures are available, none of them contains informative form of signatures nor shows comprehensive coverage on liver cancer. Thus we constructed Liverome, a curated database of liver cancer-related gene signatures with self-contained context information. Description Liverome’s data coverage is more than three times larger than any other signature database, consisting of 143 signatures taken from 98 HCC studies, mostly microarray and proteome, and involving 6,927 genes. The signatures were post-processed into an informative and uniform representation and annotated with an itemized summary so that all context information is unambiguously self-contained within the database. The signatures were further informatively named and meaningfully organized according to ten functional categories for guided browsing. Its web interface enables a straightforward retrieval of known differential expression information on a query gene and a comparison of signatures to prioritize common genes. The utility of Liverome-collected data is shown by case studies in which useful biological insights on HCC are produced. Conclusion Liverome database provides a comprehensive collection of well-curated HCC gene signatures and straightforward interfaces for gene search and signature comparison as well. Liverome is available at http://liverome.kobic.re.kr.
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Affiliation(s)
- Langho Lee
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea
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17
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Yang Z, Ren F, Liu C, He S, Sun G, Gao Q, Yao L, Zhang Y, Miao R, Cao Y, Zhao Y, Zhong Y, Zhao H. dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics 2010; 11 Suppl 4:S5. [PMID: 21143814 PMCID: PMC3005935 DOI: 10.1186/1471-2164-11-s4-s5] [Citation(s) in RCA: 189] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Background MicroRNAs (miRNAs) are small noncoding RNAs about 22 nt long that negatively regulate gene expression at the post-transcriptional level. Their key effects on various biological processes, e.g., embryonic development, cell division, differentiation and apoptosis, are widely recognized. Evidence suggests that aberrant expression of miRNAs may contribute to many types of human diseases, including cancer. Here we present a database of differentially expressed miRNAs in human cancers (dbDEMC), to explore aberrantly expressed miRNAs among different cancers. Results We collected the miRNA expression profiles of 14 cancer types, curated from 48 microarray data sets in peer-reviewed publications. The Significance Analysis of Microarrays method was used to retrieve the miRNAs that have dramatically different expression levels in cancers when compared to normal tissues. This database provides statistical results for differentially expressed miRNAs in each data set. A total of 607 differentially expressed miRNAs (590 mature miRNAs and 17 precursor miRNAs) were obtained in the current version of dbDEMC. Furthermore, low-throughput data from the same literature were also included in the database for validation. An easy-to-use web interface was designed for users. Annotations about each miRNA can be queried through miRNA ID or miRBase accession numbers, or can be browsed by different cancer types. Conclusions This database is expected to be a valuable source for identification of cancer-related miRNAs, thereby helping with the improvement of classification, diagnosis and treatment of human cancers. All the information is freely available through http://159.226.118.44/dbDEMC/index.html.
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Affiliation(s)
- Zhen Yang
- School of Life Science, Fudan University, Shanghai, China.
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Dabbous MK, Margaret Jefferson M, Haney L, Thomas EL. Biomarkers of metastatic potential in cultured adenocarcinoma clones. Clin Exp Metastasis 2010; 28:101-11. [DOI: 10.1007/s10585-010-9362-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2010] [Accepted: 11/09/2010] [Indexed: 12/19/2022]
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Chari R, Thu KL, Wilson IM, Lockwood WW, Lonergan KM, Coe BP, Malloff CA, Gazdar AF, Lam S, Garnis C, MacAulay CE, Alvarez CE, Lam WL. Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer. Cancer Metastasis Rev 2010; 29:73-93. [PMID: 20108112 DOI: 10.1007/s10555-010-9199-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Advances in high-throughput, genome-wide profiling technologies have allowed for an unprecedented view of the cancer genome landscape. Specifically, high-density microarrays and sequencing-based strategies have been widely utilized to identify genetic (such as gene dosage, allelic status, and mutations in gene sequence) and epigenetic (such as DNA methylation, histone modification, and microRNA) aberrations in cancer. Although the application of these profiling technologies in unidimensional analyses has been instrumental in cancer gene discovery, genes affected by low-frequency events are often overlooked. The integrative approach of analyzing parallel dimensions has enabled the identification of (a) genes that are often disrupted by multiple mechanisms but at low frequencies by any one mechanism and (b) pathways that are often disrupted at multiple components but at low frequencies at individual components. These benefits of using an integrative approach illustrate the concept that the whole is greater than the sum of its parts. As efforts have now turned toward parallel and integrative multidimensional approaches for studying the cancer genome landscape in hopes of obtaining a more insightful understanding of the key genes and pathways driving cancer cells, this review describes key findings disseminating from such high-throughput, integrative analyses, including contributions to our understanding of causative genetic events in cancer cell biology.
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Affiliation(s)
- Raj Chari
- Genetics Unit - Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada.
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