Bioinformatics Tools and Workflow to Select Blood Biomarkers for Early Cancer Diagnosis: An Application to Pancreatic Cancer. Issue 21 (10th October 2019)

• Record Type: Journal Article
• Authors: Vandenbrouck, Yves; Christiany, David; Combes, Florence; Loux, Valentin; Brun, Virginie
• Other Names: Hondermarck Hubert guestEditor.
• Is Part Of: Proteomics. Volume 19:Issue 21/22(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-10-10
• Subjects: bioinformatics -- computer application -- diagnosis -- Galaxy -- pancreatic cancer -- proteomics biomarker discovery -- secretome -- web server; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201800489
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: Secretome proteomics for the discovery of cancer biomarkers holds great potential to improve early cancer diagnosis. A knowledge‐based approach relying on mechanistic criteria related to the type of cancer should help to identify candidates from available "omics" information. With the aim of accelerating the discovery process for novel biomarkers, a set of tools is developed and made available via a Galaxy‐based instance to assist end‐users biologists. These implemented tools proceed by a step‐by‐step strategy to mine transcriptomics and proteomics databases for information relating to tissue specificity, allow the selection of proteins that are part of the secretome, and combine this information with proteomics datasets to rank the most promising candidate biomarkers for early cancer diagnosis. Using pancreatic cancer as a case study, this strategy produces a list of 24 candidate biomarkers suitable for experimental assessment by MS‐based proteomics. Among these proteins, three (SYCN, REG1B, and PRSS2) were previously reported as circulating candidate biomarkers of pancreatic cancer. Here, further refinement of this list allows to prioritize 14 candidate biomarkers along with their associated proteotypic peptides for further investigation, using targeted MS‐based proteomics. The bioinformatics tools and the workflow implementing this strategy for the selection of candidate biomarkers are freely accessible at http://www.proteore.org .