Immune-related diseases such as psoriasis, rheumatoid arthritis, systemic lupus erythematosus, inflammatory bowel disease, multiple sclerosis, and systemic sclerosis are common yet complex chronic conditions that pose a major public health burden. Immunomodulatory therapies, including biologic inhibitors, biologics, and small-molecule targeted agents, have significantly improved treatment outcomes. However, they also bring new challenges, particularly the increased risks of severe infections (HBV, TB, HIV, EBV, CMV, and opportunistic fungal or treponemal infections) and therapy-associated malignancies (lymphoma, non-melanoma skin cancer, and solid tumors).
Current risk assessment methods rely mainly on clinical experience and traditional risk factors, lacking integration of systematic biomarkers or individualized prediction models. Preventive efficacy for viral reactivation and therapy-related tumorigenesis remains insufficiently validated. Moreover, most existing studies are based on small cohorts or retrospective analyses, leading to fragmented and inconsistent findings and a lack of large-scale real-world evidence.
This study aims to establish predictive models for infection and cancer risks by integrating multi-omics data, clinical characteristics, and treatment histories. The goal is to develop systematic and individualized risk assessment tools to support precision clinical decision-making, advancing infection prevention and cancer surveillance in immune-related diseases toward a new stage of precision medicine.
