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, cu… Show more
“…Oncomine data were extended by drug treatment signatures and target/reference gene sets providing a network of Molecular Concepts Map (http://private.molecularconcepts.org; Rhodes et al, 2007b). Differentially expressed proteins in human cancers were catalogued in the dbDEPC database (http://lifecenter.sgst.cn/dbdepc/index.do; He et al, 2012). Gene expression profiles may be used for reverse engineering of cancer specific regulatory networks (Basso et al, 2005; Ergün et al, 2007).…”
Section: Four Examples Of Network Description and Analysis In Drugmentioning
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 gives 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 node/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. 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.
“…Oncomine data were extended by drug treatment signatures and target/reference gene sets providing a network of Molecular Concepts Map (http://private.molecularconcepts.org; Rhodes et al, 2007b). Differentially expressed proteins in human cancers were catalogued in the dbDEPC database (http://lifecenter.sgst.cn/dbdepc/index.do; He et al, 2012). Gene expression profiles may be used for reverse engineering of cancer specific regulatory networks (Basso et al, 2005; Ergün et al, 2007).…”
Section: Four Examples Of Network Description and Analysis In Drugmentioning
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 gives 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 node/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. 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.
“…The proteins for which the maximum numbers of peptides are found include FIBA_HUMAN, CO3_HUMAN, APOA1_HUMAN, CO1A1_HUMAN and A4_HUMAN (Table 2). Eight out of the top ten proteins with the highest number of identified peptides of CancerPDF are found to be differentially expressed in dbDEPC 2.0 38 , which is a database of differentially expressed proteins in cancer.Figure 1Architecture of CancerPDF database.Figure 2Distribution of peptides according to length ( A ), mass range ( B ), cancer tissue types ( C ) and biofluids ( D ) in CancerPDF database.
…”
CancerPDF (Cancer Peptidome Database of bioFluids) is a comprehensive database of endogenouspeptides 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/.Cancer is considered as the major public health concern worldwide and the second most health hazardous disease, causing deaths in the United States. Globally 14 million new cases and 8.2 million cancer-related deaths have been reported in 2012 1 . In 2017, there is an approximation of 63,990 new cases and 14,400 deaths from cancer in the United States 2 . Although the rate of survival has increased over the years, still it is meager. The lack of diagnosis at an early stage is one of the major hurdles in treating the cancer patients 3 . Cancer detection is often skewed due to the lack of accurate and non-invasive markers. Due to advances in genomics and proteomics, the probability to detect the cancer at an early stage has improved using peptide-based biomarkers 4 . In recent years, the peptide-based biomarkers have emerged as diagnostic tools in several foodborne diseases 5 , arthritis 6 , inflammatory disease 7 as well as cancer 8,9 . Thus it is imperative to understand the mechanism of action and processing of peptides in mammalian biofluids. Following are the few examples of peptide-based biomarkers; i) Insulin and C-peptide are used in case of diabetes 10 , ii) Calcitonin, and collagen fragments in case of osteoporosis 11-13 , iii) Pro-gastrin-releasing peptide for small cell lung carcinoma 14 , iv) β-amyloid 1-42 for Alzheimer's disease 15 and v) angiotensin II for hypertension 16 .In past, large number of peptide repositories and computational resources have been developed to explore full potential of peptides in medical sciences [17][18][19] . Pepbank is the generalized database of biologically relevant peptides containing nea...
“…Previously, we have developed dbDEPC, a database of differentially expressed proteins in human cancers. dbDEPC provides information on protein-level differentially expressed changes in cancers, curated from published mass spectrometry (MS) experiments 21 .…”
Section: Resultsmentioning
confidence: 99%
“…The protein differential expression profiles in human cancers were collected from our previously published dbDEPC 2.0 (http://lifecenter.sgst.cn/dbdepc/index.do) 21 . In dbDEPC 2.0, data collection underwent the following processes: first, an automated text mining of PubMed abstracts was applied using the names of cancer types in MeSH, MS-related words (MS, quantitative proteomic) and keywords describing expression changes (upregulated, downregulated and fold change); second, to control data quality, each deposited data set went through a rigorous manual review process.…”
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 non-cancer specific variations was increased by about ten folds 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 crVARs 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 databases containing variations that can be directly used for proteomics database searching can be downloaded.
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