Existing epidemiologic and clinical studies have demonstrated that obesity is associated with the risk of a variety of cancers. In recent years, an increasing number of experimental and clinical studies have unraveled the complex relationship between obesity and cancer risk and the underlying mechanisms. Obesity-induced abnormalities in immunity and biochemical metabolism, including chronic inflammation, hormonal disorders, dysregulation of adipokines, and microbial dysbiosis, may be important contributors to cancer development and progression. These contributors play different roles in cancer development and progression at different sites. Lifestyle changes, weight loss medications, and bariatric surgery are key approaches for weight-centered, obesity-related cancer prevention. Treatment of obesity-related inflammation and hormonal or metabolic dysregulation with medications has also shown promise in preventing obesity-related cancers. In this review, we summarize the mechanisms through which obesity affects the risk of cancer at different sites and explore intervention strategies for the prevention of obesity-associated cancers, concluding with unresolved questions and future directions regarding the link between obesity and cancer. The aim is to provide valuable theoretical foundations and insights for the in-depth exploration of the complex relationship between obesity and cancer risk and its clinical applications.
Humans ; Adipokines/metabolism* ; Bariatric Surgery ; Inflammation/therapy* ; Neoplasms/prevention & control* ; Obesity/therapy* ; Risk Factors

It is necessary to provide a SPARQL query tool for biomedical researchers due to the massive data of bi-omedical ontology. However, manual input of SPARQL query statements is quite complicated and easy to produce errors. A Cytospace-based parameterized SPARQL query system was thus developed for biomedical ontology, which can generate a module with SPARQL query conditions used as parameters. Biomedical researchers can thus rapidly, easily and directly retrieve the data in several biomedical ontology knowledge libraries instead of manual input of SPARQL query statements.

It is of great significance to provide the policy and legal support for the health of whole people by clarifying the various medical policies and laws through the knowledge management model. A knowledge base of medical poli-cies and laws was thus developed using the ontology technology based on the basic structure of medical policies and laws, which is consisted of a front-end management system and a back-end management system with excellent knowledge management functions, and can thus help the decision makers to work out their policies by making full use of its medial and legal knowledge.