Development and validation of an immunity-related classifier of nine chemokines for predicting recurrence in stage I–III patients with colorectal cancer after operation

INTRODUCTION: Chemokines are closely related with tumor immunity, progression, and metastasis. We aimed to construct a multi-RNA classifier of chemokine family genes for predicting tumor recurrence in stage I–III patients with colorectal cancer (CRC) after operation. PATIENTS AND METHODS: By analyzing microarray data, the Cox regression analysis was conducted to determine survival-related chemokine family genes and develop a multi-RNA classifier in the training set. The prognostic value of this multi-RNA classifier was further validated in the internal validation and external independent sets. Receiver operating characteristic curves were used to compare the prediction ability of the combined model of this multi-RNA classifier and stage, and this multi-RNA classifier and stage alone. RESULTS: Nine survival-related chemokines were identified in the training set. We identified a nine-chemokine classifier and classified the patients as high-risk or low-risk. Compared with CRC patients with high-risk scores, CRC patients with low-risk scores had longer disease-free survival in the training (HR=2.353, 95% CI=1.480–3.742, P<0.001), internal validation (HR=2.389, 95% CI=1.428–3.996, P<0.001), and external independent (HR=3.244, 95% CI=1.813–5.807, P<0.001) sets. This nine-chemokine classifier was an independent prognostic factor in these datasets (P<0.05). The combined model of this nine-chemokine classifier and tumor stage may tend to have higher accuracy than stage alone in the training (area under curve 0.727 vs 0.626, P<0.01), internal validation (0.668 vs 0.584, P=0.03), and external independent (0.704 vs 0.678, P>0.05) sets. This nine-chemokine classifier may only be applied in Marisa’s C2, C5, and C6 subtypes patients. CONCLUSION: Our nine-chemokine classifier is a reliable prognostic tool for some specific biological subtypes of CRC patients. It might contribute to guide the personalized treatment for high-risk patients.