Overexpression of CXCR2 predicts poor prognosis in patients with colorectal cancer

Abstract

Colorectal cancer is a heterogeneous disease. Although many risk factors are used to predict colorectal cancer patients' prognosis after surgical resection, new prognostic factors are still needed to be defined to promote predictive efficacy of prognosis and further guide therapies. Herein, we identified the prognostic significance of CXCR2 in colorectal cancer patients. We retrospectively analysed 134 patients with colorectal cancer who underwent minimally invasive surgery between 2010 and 2011. The overall cohort was divided into a training set (n = 78) and a validation set (n = 56). We detected CXCR2 expression using immunohistochemical staining and defined the cut-off value using X-tile program. Next, we analysed the association between CXCR2 expression and clinicopathologic features in training and validation sets. High expression of CXCR2 was associated with Dukes stage (P = 0.018), tumor invasion (P = 0.018) and liver metastasis (P = 0.047). Multivariate COX regression analyses confirmed that high CXCR2 level was an independent prognostic risk factor for both overall survival and disease free survival. Kaplan-Meier survival analysis demonstrated that patients with high expression of CXCR2 had a poor overall survival and disease free survival even in low-risk group (I + II). This indicated that CXCR2 can help to refine individual risk stratification. In addition, we established Nomograms of all significant factors to predict 3- or 5-years overall survival and disease free survival. Moreover, we found the combination of CXCR2 and its ligand CXCL5 had more significant value in predicting the prognosis than single CXCR2 factor.

Keywords: CXCL5; CXCR2; colorectal cancer; prognosis.

Figure 1. Increased expression and location of CXCR2 in CRC tissue

(A and B) Immunohistochemical results showing high expression of CXCR2 in CRC tissues, the difference between tumor and peritumoral normal tissues is statistically significant (***P < 0.001). (C) Immunofluorescence images showing the location of CXCR2 and αSMA in CRC specimens.

Figure 2. Kaplan-Meier survival analysis according to the expression of CXCR2

(A and C) Comparison of overal survival in CXCR2^high and CXCR2^low groups in both traning set (A) and validation set (C). (B and D) Comparison of disease free survival in CXCR2^high and CXCR2^low groups in both traning set (B) and validation set (D). (E and F) Kaplan-Meier subgroup analysis to evaluate prognostic value of CXCR2 by Dukes stage. OS and DFS in I + II group. (G and H) Kaplan-Meier subgroup analysis to evaluate prognostic value of CXCR2 by Dukes stage. OS and DFS in III + IV group.

Figure 3. Nomogram model for the prediction of prognosis in CRC patients

(A, B and C) prediction and calibration plots for OS; (D, E and F) prediction and calibration plots for DFS.

Figure 4. Combination of CXCL5 and CXCR2 to predict prognosis on CRC patients

(A) expression of CXCL5 in CRC tissues; (B and C) comparison of OS (B) and DFS (C) in CXCL5^high/CXCR2^high group, CXCL5^high/CXCR2^low or CXCL5^low/CXCR2^high group, and CXCL5^low/CXCR2^low group.

Figure 5. Combination of CXCL1 and CXCR2 to predict prognosis on CRC patients

(A) expression of CXCL1 in CRC tissues; (B and C) comparison of OS (B) and DFS (C) in CXCL1^high/CXCR2^high group, CXCL1^high/CXCR2^low or CXCL1^low/CXCR2^high group, and CXCL1^low/CXCR2^low group.

Figure 6. X-tile analysis of survival data in CRC patients reveals a continuous distribution based on CXCR2 staining score

The plot shows the χ2 log-rank values produced when dividing the cohort with one cut-point, producing high, and low subsets. The X-axis represents all potential cut-points from low to high (left to right) that defines a low subset, whereas the Y-axis represents cut-points from high to low (top to bottom), that defines a high subset. Red coloration of cut-point indicates an inverse correlation with survival, whereas green coloration represents direct associations (A and D, for OS and DFS respectively). The optimal cut-point occurs at the brightest pixel (red). The cut-point highlighted by the white circle in A and D is shown on a histogram of the entire cohort (B and E, for OS and DFS respectively), and a Kaplan-Meier plot (C and F, for OS and DFS respectively, low subset grey, high subset light green).