Development and validation of an immunity-related classifier of nine chemokines for predicting recurrence in stage I-III patients with colorectal cancer after operation
Authors Xu G, Zhou Y, Zhou F
Received 17 May 2018
Accepted for publication 11 July 2018
Published 1 October 2018 Volume 2018:10 Pages 4051—4064
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Antonella D'Anneo
Guozeng Xu,1,2,* Yuehan Zhou,3,* Fuxiang Zhou1,2
1Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China; 2Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Wuhan, China; 3Department of Pharmacology, Guilin Medical University, Guilin 541004, Guangxi, China
*These authors contributed equally to this work
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.
Keywords: classifier, colorectal cancer, chemokine, survival analysis, risk classification, microarray
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