1
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Feng J, Chen W, Dong X, Wang J, Mei X, Deng J, Yang S, Zhuo C, Huang X, Shao L, Zhang R, Guo J, Ma R, Liu J, Li F, Wu Y, Han L, He C. CSCD2: an integrated interactional database of cancer-specific circular RNAs. Nucleic Acids Res 2021; 50:D1179-D1183. [PMID: 34551437 PMCID: PMC8728299 DOI: 10.1093/nar/gkab830] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/06/2021] [Accepted: 09/09/2021] [Indexed: 12/19/2022] Open
Abstract
The significant function of circRNAs in cancer was recognized in recent work, so a well-organized resource is required for characterizing the interactions between circRNAs and other functional molecules (such as microRNA and RNA-binding protein) in cancer. We previously developed cancer-specific circRNA database (CSCD), a comprehensive database for cancer-specific circRNAs, which is widely used in circRNA research. Here, we updated CSCD to CSCD2 (http://geneyun.net/CSCD2 or http://gb.whu.edu.cn/CSCD2), which includes significantly more cancer-specific circRNAs identified from a large number of human cancer and normal tissues/cell lines. CSCD2 contains >1000 samples (825 tissues and 288 cell lines) and identifies a large number of circRNAs: 1 013 461 cancer-specific circRNAs, 1 533 704 circRNAs from only normal samples and 354 422 circRNAs from both cancer and normal samples. In addition, CSCD2 predicts potential miRNA–circRNA and RBP–circRNA interactions using binding motifs from >200 RBPs and 2000 microRNAs. Furthermore, the potential full-length and open reading frame sequence of these circRNAs were also predicted. Collectively, CSCD2 provides a significantly enhanced resource for exploring the function and regulation of circRNAs in cancer.
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Affiliation(s)
- Jing Feng
- School of Computer Science, Wuhan University, Wuhan430072, China
| | - Wenbo Chen
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Xin Dong
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Jun Wang
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Xiangfei Mei
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Jin Deng
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Siqi Yang
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Chenjian Zhuo
- College of Biomedicine and Health, Huazhong Agricultural University, Wuhan430070, China
| | - Xiaoyu Huang
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Lin Shao
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Rongyu Zhang
- College of Biomedicine and Health, Huazhong Agricultural University, Wuhan430070, China
| | - Jing Guo
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Ronghui Ma
- College of Biomedicine and Health, Huazhong Agricultural University, Wuhan430070, China
| | - Juan Liu
- School of Computer Science, Wuhan University, Wuhan430072, China
| | - Feng Li
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Ying Wu
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China
| | - Leng Han
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX77030, USA
| | - Chunjiang He
- School of Basic Medical Sciences, Wuhan University, Wuhan430071, China.,College of Biomedicine and Health, Huazhong Agricultural University, Wuhan430070, 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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Hyung D, Mallon AM, Kyung DS, Cho SY, Seong JK. TarGo: network based target gene selection system for human disease related mouse models. Lab Anim Res 2019; 35:23. [PMID: 32257911 PMCID: PMC7081697 DOI: 10.1186/s42826-019-0023-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 10/21/2019] [Indexed: 11/25/2022] Open
Abstract
Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction.
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Affiliation(s)
- Daejin Hyung
- 1National Cancer Center, 323 Ilsan-ro, Goyang-si, Kyeonggi-do 10408 Republic of Korea
| | - Ann-Marie Mallon
- 2MRC Harwell Institute, Mammalian Genetics Unit, Oxfordshire, OX11 0RD UK
| | - Dong Soo Kyung
- 3Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, and BK21 Plus Program for Creative Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, 08826 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea.,5Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX institute, Seoul National University, Seoul, 08826 Republic of Korea
| | - Soo Young Cho
- 1National Cancer Center, 323 Ilsan-ro, Goyang-si, Kyeonggi-do 10408 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea
| | - Je Kyung Seong
- 3Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, and BK21 Plus Program for Creative Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, 08826 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea.,5Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX institute, Seoul National University, Seoul, 08826 Republic of Korea
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4
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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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5
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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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6
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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: 9] [Impact Index Per Article: 1.5] [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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7
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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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8
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Bhalla S, Verma R, Kaur H, Kumar R, Usmani SS, Sharma S, Raghava GPS. CancerPDF: A repository of cancer-associated peptidome found in human biofluids. Sci Rep 2017; 7:1511. [PMID: 28473704 PMCID: PMC5431423 DOI: 10.1038/s41598-017-01633-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 04/03/2017] [Indexed: 01/28/2023] Open
Abstract
CancerPDF (Cancer Peptidome Database of bioFluids) is a comprehensive database of endogenous peptides detected in the human biofluids. The peptidome patterns reflect the synthesis, processing and degradation of proteins in the tissue environment and therefore can act as a gold mine to probe the peptide-based cancer biomarkers. Although an extensive data on cancer peptidome has been generated in the recent years, lack of a comprehensive resource restrains the facility to query the growing community knowledge. We have developed the cancer peptidome resource named CancerPDF, to collect and compile all the endogenous peptides isolated from human biofluids in various cancer profiling studies. CancerPDF has 14,367 entries with 9,692 unique peptide sequences corresponding to 2,230 unique precursor proteins from 56 high-throughput studies for ~27 cancer conditions. We have provided an interactive interface to query the endogenous peptides along with the primary information such as m/z, precursor protein, the type of cancer and its regulation status in cancer. To add-on, many web-based tools have been incorporated, which comprise of search, browse and similarity identification modules. We consider that the CancerPDF will be an invaluable resource to unwind the potential of peptidome-based cancer biomarkers. The CancerPDF is available at the web address http://crdd.osdd.net/raghava/cancerpdf/.
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Affiliation(s)
- Sherry Bhalla
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India
| | - Ruchi Verma
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India
| | - Harpreet Kaur
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India
| | - Rajesh Kumar
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India
| | - Salman Sadullah Usmani
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India
| | - Suresh Sharma
- Centre for Systems Biology and Bioinformatics, Panjab University, Sector 14, Chandigarh, 160014, India
| | - Gajendra P S Raghava
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India.
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9
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Zhang M, Wang B, Xu J, Wang X, Xie L, Zhang B, Li Y, Li J. CanProVar 2.0: An Updated Database of Human Cancer Proteome Variation. J Proteome Res 2017; 16:421-432. [PMID: 27977206 PMCID: PMC5515284 DOI: 10.1021/acs.jproteome.6b00505] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Identification and annotation of the mutations involved in oncogenesis and tumor progression are crucial for both cancer biology and clinical applications. Previously, we developed a public resource CanProVar, a human cancer proteome variation database for storing and querying single amino acid alterations in the human cancers. Since the publication of CanProVar, extensive cancer genomics efforts have revealed the enormous genomic complexity of various types of human cancers. Thus, there is an overwhelming need for comprehensive annotation of the genomic alterations at the protein level and making such knowledge easily accessible. Here, we describe CanProVar 2.0, a significantly expanded version of CanProVar, in which the amount of cancer-related variations and noncancer specific variations was increased by about 10-fold as compared to the previous version. To facilitate the interpretation of the variations, we added to the database functional data on potential impact of the cancer-related variations on 3D protein interaction and on the differential expression of the variant-bearing proteins between cancer and normal samples. The web interface allows for flexible queries based on gene or protein IDs, cancer types, chromosome locations, or pathways. An integrated protein sequence database containing variations that can be directly used for proteomics database searching can be downloaded.
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Affiliation(s)
- Menghuan Zhang
- Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China
| | - Bo Wang
- Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Xu
- Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaojing Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Suite NB100, Houston, TX 77030
- Lester & Sue Smith Breast Center, Baylor College of Medicine, One Baylor Plaza, Suite NB100, Houston, TX 77030
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China
| | - Bing Zhang
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Suite NB100, Houston, TX 77030
- Lester & Sue Smith Breast Center, Baylor College of Medicine, One Baylor Plaza, Suite NB100, Houston, TX 77030
| | - Yixue Li
- Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China
- Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jing Li
- Department of Bioinformatics & Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China
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10
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Percy AJ, Michaud SA, Jardim A, Sinclair NJ, Zhang S, Mohammed Y, Palmer AL, Hardie DB, Yang J, LeBlanc AM, Borchers CH. Multiplexed MRM-based assays for the quantitation of proteins in mouse plasma and heart tissue. Proteomics 2016; 17. [DOI: 10.1002/pmic.201600097] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2016] [Revised: 08/14/2016] [Accepted: 09/28/2016] [Indexed: 12/30/2022]
Affiliation(s)
- Andrew J. Percy
- University of Victoria-Genome British Columbia Proteomics Centre; , Vancouver Island Technology Park; Victoria BC Canada
| | - Sarah A. Michaud
- MRM Proteomics; , Vancouver Island Technology Park; Victoria BC Canada
| | - Armando Jardim
- Institute of Parasitology; McGill University; Montreal QC Canada
| | - Nicholas J. Sinclair
- University of Victoria-Genome British Columbia Proteomics Centre; , Vancouver Island Technology Park; Victoria BC Canada
| | - Suping Zhang
- MRM Proteomics; , Vancouver Island Technology Park; Victoria BC Canada
| | - Yassene Mohammed
- University of Victoria-Genome British Columbia Proteomics Centre; , Vancouver Island Technology Park; Victoria BC Canada
- Center for Proteomics and Metabolomics; Leiden University Medical Center; ZA Leiden Netherlands
| | - Andrea L. Palmer
- MRM Proteomics; , Vancouver Island Technology Park; Victoria BC Canada
| | - Darryl B. Hardie
- University of Victoria-Genome British Columbia Proteomics Centre; , Vancouver Island Technology Park; Victoria BC Canada
| | - Juncong Yang
- University of Victoria-Genome British Columbia Proteomics Centre; , Vancouver Island Technology Park; Victoria BC Canada
| | - Andre M. LeBlanc
- University of Victoria-Genome British Columbia Proteomics Centre; , Vancouver Island Technology Park; Victoria BC Canada
| | - Christoph H. Borchers
- University of Victoria-Genome British Columbia Proteomics Centre; , Vancouver Island Technology Park; Victoria BC Canada
- Department of Biochemistry and Microbiology; University of Victoria; Victoria BC Canada
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11
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Kanonidis EI, Roy MM, Deighton RF, Le Bihan T. Protein Co-Expression Analysis as a Strategy to Complement a Standard Quantitative Proteomics Approach: Case of a Glioblastoma Multiforme Study. PLoS One 2016; 11:e0161828. [PMID: 27571357 PMCID: PMC5003355 DOI: 10.1371/journal.pone.0161828] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 08/14/2016] [Indexed: 12/21/2022] Open
Abstract
Although correlation network studies from co-expression analysis are increasingly popular, they are rarely applied to proteomics datasets. Protein co-expression analysis provides a complementary view of underlying trends, which can be overlooked by conventional data analysis. The core of the present study is based on Weighted Gene Co-expression Network Analysis applied to a glioblastoma multiforme proteomic dataset. Using this method, we have identified three main modules which are associated with three different membrane associated groups; mitochondrial, endoplasmic reticulum, and a vesicle fraction. The three networks based on protein co-expression were assessed against a publicly available database (STRING) and show a statistically significant overlap. Each of the three main modules were de-clustered into smaller networks using different strategies based on the identification of highly connected networks, hierarchical clustering and enrichment of Gene Ontology functional terms. Most of the highly connected proteins found in the endoplasmic reticulum module were associated with redox activity while a core of the unfolded protein response was identified in addition to proteins involved in oxidative stress pathways. The proteins composing the electron transfer chain were found differently affected with proteins from mitochondrial Complex I being more down-regulated than proteins from Complex III. Finally, the two pyruvate kinases isoforms show major differences in their co-expressed protein networks suggesting roles in different cellular locations.
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Affiliation(s)
- Evangelos I. Kanonidis
- SynthSys and School of Biological Sciences, Waddington building, University of Edinburgh, Edinburgh, United Kingdom, EH9 3BF
| | - Marcia M. Roy
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh United Kingdom, EH16 4SB
| | - Ruth F. Deighton
- Edinburgh Medical School: Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom, EH8 9AG
| | - Thierry Le Bihan
- SynthSys and School of Biological Sciences, Waddington building, University of Edinburgh, Edinburgh, United Kingdom, EH9 3BF
- * E-mail:
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12
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Garg AD, Elsen S, Krysko DV, Vandenabeele P, de Witte P, Agostinis P. Resistance to anticancer vaccination effect is controlled by a cancer cell-autonomous phenotype that disrupts immunogenic phagocytic removal. Oncotarget 2016; 6:26841-60. [PMID: 26314964 PMCID: PMC4694957 DOI: 10.18632/oncotarget.4754] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Accepted: 07/10/2015] [Indexed: 12/22/2022] Open
Abstract
Immunogenic cell death (ICD) is a well-established instigator of ‘anti-cancer vaccination-effect (AVE)’. ICD has shown considerable preclinical promise, yet there remain subset of cancer patients that fail to respond to clinically-applied ICD inducers. Non-responsiveness to ICD inducers could be explained by the existence of cancer cell-autonomous, anti-AVE resistance mechanisms. However such resistance mechanisms remain poorly investigated. In this study, we have characterized for the first time, a naturally-occurring preclinical cancer model (AY27) that exhibits intrinsic anti-AVE resistance despite treatment with ICD inducers like mitoxantrone or hypericin-photodynamic therapy. Further mechanistic analysis revealed that this anti-AVE resistance was associated with a defect in exposing the important ‘eat me’ danger signal, surface-calreticulin (ecto-CRT/CALR). In an ICD setting, this defective ecto-CRT further correlated with severely reduced phagocytic clearance of AY27 cells as well as the failure of these cells to activate AVE. Defective ecto-CRT in response to ICD induction was a result of low endogenous CRT protein levels (i.e. CRTlow-phenotype) in AY27 cells. Exogenous reconstitution of ecto-rCRT (recombinant-CRT) improved the phagocytic removal of ICD inducer-treated AY27 cells, and importantly, significantly increased their AVE-activating ability. Moreover, we found that a subset of cancer patients of various cancer-types indeed possessed CALRlow or CRTlow-tumours. Remarkably, we found that tumoural CALRhigh-phenotype was predictive of positive clinical responses to therapy with ICD inducers (radiotherapy and paclitaxel) in lung and ovarian cancer patients, respectively. Furthermore, only in the ICD clinical setting, tumoural CALR levels positively correlated with the levels of various phagocytosis-associated genes relevant for phagosome maturation or processing. Thus, we reveal the existence of a cancer cell-autonomous, anti-AVE or anti-ICD resistance mechanism that has profound clinical implications for anticancer immunotherapy and cancer predictive biomarker analysis.
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Affiliation(s)
- Abhishek D Garg
- Cell Death Research & Therapy (CDRT) Unit, Department of Cellular and Molecular Medicine, KU Leuven University of Leuven, Leuven, Belgium
| | - Sanne Elsen
- Laboratory for Molecular Biodiscovery, Department of Pharmaceutical Sciences, KU Leuven, Leuven, Belgium
| | - Dmitri V Krysko
- Molecular Signaling and Cell Death Unit, Department for Molecular Biomedical Research, VIB, Ghent, Belgium.,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Peter Vandenabeele
- Molecular Signaling and Cell Death Unit, Department for Molecular Biomedical Research, VIB, Ghent, Belgium.,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Peter de Witte
- Laboratory for Molecular Biodiscovery, Department of Pharmaceutical Sciences, KU Leuven, Leuven, Belgium
| | - Patrizia Agostinis
- Cell Death Research & Therapy (CDRT) Unit, Department of Cellular and Molecular Medicine, KU Leuven University of Leuven, Leuven, Belgium
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13
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Zhang M, Li H, He Y, Sun H, Xia L, Wang L, Sun B, Ma L, Zhang G, Li J, Li Y, Xie L. Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks. J Proteome Res 2015; 14:2745-57. [PMID: 26006110 DOI: 10.1021/acs.jproteome.5b00249] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.
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Affiliation(s)
- Menghuan Zhang
- †Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.,‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Hong Li
- ‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.,§Key Laboratory of Systems Biology, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ying He
- ‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.,§Key Laboratory of Systems Biology, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Han Sun
- ‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.,§Key Laboratory of Systems Biology, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Xia
- ⊥Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Lishun Wang
- ⊥Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Bo Sun
- †Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.,‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Liangxiao Ma
- ‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Guoqing Zhang
- ‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Jing Li
- †Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yixue Li
- †Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.,‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.,§Key Laboratory of Systems Biology, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Lu Xie
- ‡Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
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14
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Lee JH, You S, Hyeon DY, Kang B, Kim H, Park KM, Han B, Hwang D, Kim S. Comprehensive data resources and analytical tools for pathological association of aminoacyl tRNA synthetases with cancer. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav022. [PMID: 25824651 PMCID: PMC4377328 DOI: 10.1093/database/bav022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Mammalian cells have cytoplasmic and mitochondrial aminoacyl-tRNA synthetases (ARSs) that catalyze aminoacylation of tRNAs during protein synthesis. Despite their housekeeping functions in protein synthesis, recently, ARSs and ARS-interacting multifunctional proteins (AIMPs) have been shown to play important roles in disease pathogenesis through their interactions with disease-related molecules. However, there are lacks of data resources and analytical tools that can be used to examine disease associations of ARS/AIMPs. Here, we developed an Integrated Database for ARSs (IDA), a resource database including cancer genomic/proteomic and interaction data of ARS/AIMPs. IDA includes mRNA expression, somatic mutation, copy number variation and phosphorylation data of ARS/AIMPs and their interacting proteins in various cancers. IDA further includes an array of analytical tools for exploration of disease association of ARS/AIMPs, identification of disease-associated ARS/AIMP interactors and reconstruction of ARS-dependent disease-perturbed network models. Therefore, IDA provides both comprehensive data resources and analytical tools for understanding potential roles of ARS/AIMPs in cancers. Database URL:http://ida.biocon.re.kr/, http://ars.biocon.re.kr/
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Affiliation(s)
- Ji-Hyun Lee
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Sungyong You
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Do Young Hyeon
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Byeongsoo Kang
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Hyerim Kim
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Kyoung Mii Park
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Byungwoo Han
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Daehee Hwang
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
| | - Sunghoon Kim
- Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea Medicinal Bioconvergence Research Center and Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 151-742, Republic of Korea, School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang 790-784, Republic of Korea, Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea and Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, Republic of Korea
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15
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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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16
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Rapid development of proteomics in China: from the perspective of the Human Liver Proteome Project and technology development. SCIENCE CHINA-LIFE SCIENCES 2014; 57:1162-71. [PMID: 25119674 DOI: 10.1007/s11427-014-4714-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Accepted: 07/01/2014] [Indexed: 12/17/2022]
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17
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 506] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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18
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Hanash S, Schliekelman M, Zhang Q, Taguchi A. Integration of proteomics into systems biology of cancer. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2012; 4:327-37. [PMID: 22407608 DOI: 10.1002/wsbm.1169] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Deciphering the complexity and heterogeneity of cancer, benefits from integration of proteomic level data into systems biology efforts. The opportunities available as a result of advances in proteomic technologies, the successes to date, and the challenges involved in integrating diverse datasets are addressed in this review.
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Affiliation(s)
- S Hanash
- Molecular Diagnostics Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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