scholarly journals Candidate List of yoUr Biomarker (CLUB): A Web-based Platform to Aid Cancer Biomarker Research

2008 ◽  
Vol 3 ◽  
pp. BMI.S467 ◽  
Author(s):  
Bernett T.K. Lee ◽  
Lailing Liew ◽  
Jiahao Lim ◽  
Jonathan K.L. Tan ◽  
Tze Chuen Lee ◽  
...  
Keyword(s):  
Interaction Network ◽  
Cancer Biomarkers ◽  
Cancer Biomarker ◽  
Candidate List ◽  
Web Based ◽  
Selection Procedures ◽  
Network Database ◽  
Molecular Interaction Networks ◽  
Biomarker Research ◽  
Validation Stage

CLUB (“Candidate List of yoUr Biomarkers”) is a freely available, web-based resource designed to support Cancer biomarker research. It is targeted to provide a comprehensive list of candidate biomarkers for various cancers that have been reported by the research community. CLUB provides tools for comparison of marker candidates from different experimental platforms, with the ability to filter, search, query and explore, molecular interaction networks associated with cancer biomarkers from the published literature and from data uploaded by the community. This complex and ambitious project is implemented in phases. As a first step, we have compiled from the literature an initial set of differentially expressed human candidate cancer biomarkers. Each candidate is annotated with information from publicly available databases such as Gene Ontology, Swiss-Prot database, National Center for Biotechnology Information's reference sequences, Biomolecular Interaction Network Database and IntAct interaction. The user has the option to maintain private lists of biomarker candidates or share and export these for use by the community. Furthermore, users may customize and combine commonly used sets of selection procedures and apply them as a stored workflow using selected candidate lists. To enable an assessment by the user before taking a candidate biomarker to the experimental validation stage, the platform contains the functionality to identify pathways associated with cancer risk, staging, prognosis, outcome in cancer and other clinically associated phenotypes. The system is available at http://club.bii.a-star.edu.sg .

Comparison between protocols and publications for prognostic and predictive cancer biomarker studies

2019 ◽  
Vol 17 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Adelaide Doussau ◽  
Esther Vinarov ◽  
Brianna Barsanti-Innes ◽  
Jonathan Kimmelman
Keyword(s):  
Cohort Studies ◽  
Retrospective Cohort ◽  
Cancer Biomarkers ◽  
Cancer Biomarker ◽  
Risk Of Bias ◽  
Clinical Endpoints ◽  
Study Protocols ◽  
Retrospective Cohort Studies ◽  
Biomarker Research ◽  
The Relationship

Background: Method prespecification in study protocols is important for controlling bias in reports. The primary goal of this study was to assess potential for discordance between study protocols and publications reporting predictive or prognostic cancer biomarker research. Secondary objectives included comparing characteristics of publications with accessible protocols compared to those without. Methods: Publications reporting predictive or prognostic cancer biomarker research were identified from 15 major journals, 2012–2015. Protocols were sought online or through repeated queries of corresponding authors. The following four items were extracted: (1) biomarkers, (2) biospecimen/assays, (3) sample size, (4) endpoints. We defined “explicit discordance” as the presence of major inconsistencies on these items. Results: Of 149 eligible publications, we obtained 19 eligible protocols online (13%). Out of a random sample of 103 publications where protocols were not available online, 12 protocols (12%) were furnished by corresponding authors; 8 (8% of authors) explicitly stated the absence of a protocol. Among 24 retrospective cohort studies, no protocol could be accessed. We found explicit discordance between publications and protocols for 18 studies (58%), in particular choice of biomarkers (36%), biospecimen/assays (6%), or endpoints (29%). Conclusion: Protocols are generally not accessible or not used for cancer biomarker studies. Publications were often explicitly discordant with protocols, particularly regarding biomarkers and endpoints. Our findings point to common unaddressed risk of bias in publications of major journals reporting the relationship between cancer biomarkers and clinical endpoints.

scholarly journals COSIFER: a Python package for the consensus inference of molecular interaction networks

2020 ◽  
Author(s):  
Matteo Manica ◽  
Charlotte Bunne ◽  
Roland Mathis ◽  
Joris Cadow ◽  
Mehmet Eren Ahsen ◽  
...  
Keyword(s):  
High Throughput ◽  
Network Inference ◽  
State Of The Art ◽  
Interaction Network ◽  
Molecular Networks ◽  
Supplementary Information ◽  
Web Based ◽  
Molecular Interaction Networks ◽  
Inference Methods ◽  
User Friendly

Abstract Summary The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks. Availability and implementation COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MiR-21 in the Cancers of the Digestive System and Its Potential Role as a Diagnostic, Predictive, and Therapeutic Biomarker

Biology ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 417
Author(s):  
Ha Thi Nguyen ◽  
Salah Eddine Oussama Kacimi ◽  
Truc Ly Nguyen ◽  
Kamrul Hassan Suman ◽  
Roselyn Lemus-Martin ◽  
...  
Keyword(s):  
Digestive System ◽  
Potential Role ◽  
Target Genes ◽  
Prognostic Biomarker ◽  
Cancer Biomarkers ◽  
Cancer Biomarker ◽  
Molecular Networks ◽  
Therapeutic Tool ◽  
Comprehensive Review ◽  
Non Coding Rnas

MicroRNAs (miRNAs) are small non-coding RNAs. They can regulate the expression of their target genes, and thus, their dysregulation significantly contributes to the development of cancer. Growing evidence suggests that miRNAs could be used as cancer biomarkers. As an oncogenic miRNA, the roles of miR-21 as a diagnostic and prognostic biomarker, and its therapeutic applications have been extensively studied. In this review, the roles of miR-21 are first demonstrated via its different molecular networks. Then, a comprehensive review on the potential targets and the current applications as a diagnostic and prognostic cancer biomarker and the therapeutic roles of miR-21 in six different cancers in the digestive system is provided. Lastly, a brief discussion on the challenges for the use of miR-21 as a therapeutic tool for these cancers is added.

Bioethics and Cancer Biomarker Research

2017 ◽  
pp. 277-282
Author(s):  
Nathan Nobis ◽  
William E Grizzle ◽  
Stephen Sodeke
Keyword(s):  
Cancer Biomarker ◽  
Biomarker Research

scholarly journals Mass spectrometry in cancer biomarker research: a case for immunodepletion of abundant blood-derived proteins from clinical tissue specimens

2014 ◽  
Vol 8 (2) ◽  
pp. 269-286 ◽  
Author(s):  
DaRue A Prieto ◽  
Donald J Johann ◽  
Bih-Rong Wei ◽  
Xiaoying Ye ◽  
King C Chan ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Cancer Biomarker ◽  
Tissue Specimens ◽  
Biomarker Research

scholarly journals STITCH 2: an interaction network database for small molecules and proteins

2009 ◽  
Vol 38 (suppl_1) ◽  
pp. D552-D556 ◽  
Author(s):  
Michael Kuhn ◽  
Damian Szklarczyk ◽  
Andrea Franceschini ◽  
Monica Campillos ◽  
Christian von Mering ◽  
...  
Keyword(s):  
Small Molecules ◽  
Interaction Network ◽  
Network Database

scholarly journals Cell Cycle Model System for Advancing Cancer Biomarker Research

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Iulia M. Lazar ◽  
Ina Hoeschele ◽  
Juliana de Morais ◽  
Milagros J. Tenga
Keyword(s):  
Cell Cycle ◽  
Model System ◽  
Cancer Biomarker ◽  
Cycle Model ◽  
Biomarker Research

scholarly journals OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data

2020 ◽  
pp. 210-220 ◽  
Author(s):  
Hayley M. Dingerdissen ◽  
Frederic Bastian ◽  
K. Vijay-Shanker ◽  
Marc Robinson-Rechavi ◽  
Amanda Bell ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Cancer Biomarkers ◽  
Cancer Biomarker ◽  
Research Network ◽  
Multidimensional Data ◽  
Literature Mining ◽  
Data Types ◽  
Cancer Mutation ◽  
Biomarker Data

PURPOSE The purpose of OncoMX 1 knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidimensional data types. METHODS Cancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database. RESULTS OncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs. CONCLUSION OncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.

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