Abstract
Oxidative stress has been implicated as a pivotal factor in the pathogenesis of numerous malignancies. However, the association between oxidative stress and the prognosis of patients with colorectal cancer liver metastasis (CRCLM) is not well elucidated. We conducted a retrospective analysis of 424 patients with CRCLM who underwent primary resection at the Union Hospital, Tongji Medical College, Wuhan, between July 2013 and September 2018. Patients were randomly divided into a training set (n = 300) and a test set (n = 124) in a 7:3 ratio.To develop the CRCLM-integrated Oxidative Stress Score (CLIOSS) for CRCLM, we utilized individual oxidative stress markers. The overall survival (OS) and disease-free survival (DFS) were estimated using the Kaplan-Meier method, and the log-rank test was employed to assess prognostic factors.To validate the predictive performance of CLIOSS, we constructed Receiver Operating Characteristic (ROC) curves and calculated the Area Under the Curve (AUC) values. Additionally, Decision Curve Analysis (DCA) and calibration plots were used to evaluate the clinical utility and predictive consistency of the model. The CLIOSS prognostic model was constructed based on the following variables and their corresponding β coefficients: 0.042 × total bilirubin (TBIL, µmol/L) + 0.032 × blood urea nitrogen (BUN, mmol/L) - 0.001 × uric acid (UA, µmol/L). Higher CLIOSS were associated with poorer OS (2.934;95% CI 2.167-3.974;P < 0.001) and DFS(2.707; 95% CI 2.000-3.664; P < 0.001. The 3-year OS AUC values were 0.803 in the training set and 0.851 in the testset, while the 3-year DFS AUC values were 0.892 and 0.898, respectively. Decision curve analysis demonstrated that both predictive models have significant clinical utility in practice. CLIOSS is a comprehensive prognostic index derived from oxidative stress markers, designed to predict long-term survival in patients with CRCLM. Our study shows that higher CLIOSS are significantly associated with poorer outcomes, making it an important tool for assessing patient risk.