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.

Inclusion Criteria:

• Pathological diagnosis of ccRCC;
• Availability of complete clinical, pathological, and follow-up data;
• Sufficient preoperative serum available for collection;
• Well-preserved pathological slides for subsequent immunohistochemical (chip) analysis;
• At least one post-treatment follow-up/efficacy evaluation.

Exclusion Criteria:

• Therapeutic contraindication cohort: Individuals presenting with severe comorbidities rendering them medically ineligible for therapeutic interventions;
• Oncological multiplicity: Subjects with either (a) antecedent therapeutic regimens targeting non-index malignancies or (b) concurrent diagnosis of untreated active malignancies;
• Biospecimen integrity violation: Cases demonstrating serum hemolysis or compromised specimen integrity;
• Data insufficiency cohort: Patients exhibiting incomplete clinical/pathological records or insufficient longitudinal follow-up data for comprehensive analysis.

Patients with histologically confirmed renal cell carcinoma