This project aims to construct a multicenter retrospective study by retrospectively collecting clinical, serological, and pathological data from patients. A comprehensive data management system will be established to facilitate the integration and analysis of multicenter data, alongside antibody profiling characteristics. A predictive model based on serological autoantibody profiles will be developed and validated using both internal and external cohorts. This model will predict clinical prognostic factors in renal carcinoma and identify patient populations likely to respond to immunotherapy. By enabling personalized treatment decisions and minimizing unnecessary treatment risks, the model aims to improve patient quality of life and overall prognosis.
Study Type
OBSERVATIONAL
Enrollment
400
first hospital affiliated of Fujian medical university
Fuzhou, Fujian, China
serum autoantibodies
After incubating the serum on the HuProt array, autoantibody signals were detected, standardized, and quantified. For the selection of candidate proteins, three criteria must be met when comparing ccRCC with healthy controls: (1) a p-value ≤ 0.05 obtained from the t-test; (2) a fold change (FC) ≥ 1.2; (3) a positivity rate ≥ 10% (ccRCC positive reactivity is defined as values greater than the average of the healthy control group plus 2× SD. The positivity rate is calculated as the ratio of ccRCC positive responses to the total number of responses).
Time frame: Prior to any treatment or surgery, 5 mL of venous blood was collected from each individual and allowed to stand at room temperature (RT) for 1 hour to facilitate coagulation.
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