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Updated: Nov 30, 2025

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
Published on: November 17, 2023
PCLiON: An Ontology for Data Standardization and Sharing of Prostate Cancer Associated Lifestyles
Yalan Chen1, Chunjiang Yu2, Xingyun Liu3
1Center for Systems Biology, Soochow University, Suzhou, China; Department of Medical Informatics, School of Medicine, Nantong University, Nantong, China.
Background:
Researches on Lifestyle medicine (LM) have emerged in recent years to garner wide attention. Prostate cancer (PCa) could be prevented and treated by positive lifestyles, but the association between lifestyles and PCa is always personalized.
Objectives:
In order to solve the heterogeneity and diversity of different data types related to PCa, establish a standardized lifestyle ontology, promote the exchange and sharing of disease lifestyle knowledge, and support text mining and knowledge discovery.
Methods:
The overall construction of PCLiON was created in accordance with the principles and methodology of ontology construction. Following the principles of evidence-based medicine, we screened and integrated the lifestyles and their related attributes. Protégé was used to construct and validate the semantic framework. All annotations in PCLiON were based on SNOMED CT, NCI Thesaurus, the Cochrane Library and FooDB, etc. HTML5 and ASP.NET was used to develop the independent Web page platform and corresponding intelligent terminal application. The PCLiON also uploaded to the National Center for Biomedical Ontology BioPortal.
Results:
PCLiON integrates 397 lifestyles and lifestyle-related factors associated with PCa, and is the first of its kind for a specific disease. It contains 320 attribute annotations and 11 object attributes. The logical relationship and completeness meet the ontology requirements. Qualitative analysis was carried out for 329 terms in PCLiON, including factors which are protective, risk or associated but functional unclear, etc. PCLiON is publicly available both at http://pcaontology.net/PCaLifeStyleDefault.aspx and https://bioportal.bioontology.org/ontologies/PCALION.
Conclusions:
Through the bilingual online platforms, complex lifestyle research data can be transformed into standardized, reliable and responsive knowledge, which can promote the shared-decision making (SDM) on lifestyle intervention and assist patients in lifestyle self-management toward the goal of PCa targeted prevention.
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