An Oxidative Stress Index-Based Score for Prognostic Prediction in Colorectal Cancer Patients Undergoing Surgery

Abstract

Oxidative stress plays an important role in the development of colorectal cancer (CRC). This study is aimed at developing and validating a novel scoring system, based on oxidative stress indexes, for prognostic prediction in CRC patients. A retrospective analysis of 1422 CRC patients who underwent surgical resection between January 2013 and December 2017 was performed. These patients were randomly assigned to the training set (n = 1022) or the validation set (n = 400). Cox regression model was used to analyze the laboratory parameters. The CRC-Integrated Oxidative Stress Score (CIOSS) was developed from albumin (ALB), direct bilirubin (DBIL), and blood urea nitrogen (BUN), which were significantly associated with survival in CRC patients. Furthermore, a survival nomogram was generated by combining the CIOSS with other beneficial clinical characteristics. The CIOSS generated was as follows: 0.074 × albumin (g/L), -0.094 × bilirubin (μmol/L), and -0.099 × blood urea nitrogen (mmol/L), based on the multivariable Cox regression analysis. Using 50% (0.1025) and 85% (0.481) of CIOSS as cutoff values, three prognostically distinct groups were formed. Patients with high CIOSS experienced worse overall survival (OS) (hazard ratio [HR] = 4.33; 95% confidence interval [CI], 2.80-6.68; P < 0.001) and worse disease-free survival (DFS) (HR = 3.02; 95% CI, 1.96-4.64; P < 0.001) compared to those with low CIOSS. This predictive nomogram had good calibration and discrimination. ROC analyses showed that the CIOSS possessed excellent performance (AUC = 0.818) in predicting DFS. The AUC of the OS nomogram based on CIOSS, TNM stage, T stage, and chemotherapy was 0.812, while that of the DFS nomogram based on CIOSS, T stage, and TNM stage was 0.855. Decision curve analysis showed that these two prediction models were clinically useful. CIOSS is a CRC-specific prognostic index based on the combination of available oxidative stress indexes. High CIOSS is a powerful indicator of poor prognosis. The CIOSS also showed better predictive performance compared to TNM stage in CRC patients.

Figure 1

Strategies for selecting patients to be included in the study.

Figure 2

Kaplan-Meier survival plots comparing OS and DFS stratified by CRC-Integrated Oxidative Stress Score (CIOSS). OS: (a) training cohort (log rank P < 0.001) and (b) validation cohort (log rank P < 0.001); DFS: (c) training cohort (log rank P < 0.001) and (d) validation cohort (log rank P < 0.001).

Figure 3

Receiver operating characteristic (ROC) curves of CIOSS, TNM stage, and the nomogram. The prognostic significance and predictive performance of the CIOSS, TNM stage, and nomogram in predicting OS and DFS in the (a, b) training sets and (c, d) validation sets, respectively.

Figure 4

Evaluation of overall survival (OS) and disease-free survival- (DFS-) associated nomograms for resectable patients with colorectal cancer (CRC). (a) OS nomogram integrating the TNM stage, T stage, CIOSS, and chemotherapy for predicting 1-, 3-, and 5-year OS rates. (b) DFS nomogram integrating TNM stage, T stage, and CIOSS for predicting 1-, 3-, and 5-year DFS rates.

Figure 5

Nomograms of time-dependent receiver operating characteristic (ROC) curves associated with overall survival (OS) and disease-free survival (DFS). (a, c) Represent ROC curve of nomogram 1-, 3-, and 5-year OS rates for the training set and validation set; (b, d) represent ROC curve of nomogram 1-, 3-, and 5-year DFS rates for the training set and validation set.

Figure 6

Calibration curves for 1-, 3-, and 5-year nomogram predictions. The calibration curves for predicting overall survival (OS) in colorectal cancer (CRC) patients at (a) 1, (b) 3, and (c) 5 years in the training set and at (d) 1, (e) 3, and (f) 5 years in the validation set. The calibration curves for predicting disease-free survival (DFS) in CRC patients at (g) 1, (h) 3, and (i) 5 years in the training set and at (j) 1, (k) 3, and (l) 5 years in the validation set.