Secretome proteomics for the discovery of cancer biomarkers holds great potential to improve early cancer diagnosis. In this context, a knowledge‐based approach relying on mechanistic criteria related to the type of cancer should help to identify candidates from available “omics” information. Numerous bioinformatics tools, databases and “omics” datasets are available, but are often widely disseminated. In addition biomedical researchers with little programming experience or no in‐house bioinformatics support can find these tools difficult to access and use. With the aim of accelerating the discovery process for novel biomarkers, we have developed a set of tools we made available via a Galaxy‐based instance thereby providing a centralized access to a unified framework to assist end‐users biologists. These tools we implemented 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 produced 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. Further refinement of this list allowed us 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 for researchers who wish to reuse them in their own quests for biomarker discovery.