Journal
CANCER MEDICINE
Volume 7, Issue 8, Pages 3682-3694Publisher
WILEY
DOI: 10.1002/cam4.1663
Keywords
colorectal cancer; IL-37; neutrophils
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Interleukin (IL)-37 and neutrophils are considered to be involved in human cancer, but their prognostic significance in colorectal cancer (CRC) has not been elucidated. The aim of this study was to evaluate the prognostic value of IL-37 expression and neutrophil levels in CRC. We retrospectively analyzed IL-37 expression, CD66b(+) neutrophil levels, and mismatch repair (MMR) status in 337 paraffin-embedded CRC specimens from the training cohort by immunohistochemistry. Their prognostic values were assessed using Kaplan-Meier curves and multivariate Cox regression models. Moreover, several risk factors were used to form nomograms to evaluate survival, and the performance of the nomograms was assessed with respect to calibration, discrimination, and clinical usefulness. Further validation was performed in an independent cohort of 245 cases. Low IL-37 expression and high CD66b(+) neutrophil levels were significantly associated with diminished disease-free survival (DFS) and overall survival (OS), and patients with MMR-deficient CRC had better clinical outcomes. Furthermore, multivariate Cox analysis identified IL-37, CD66b(+) neutrophils, and MMR status as independent prognostic factors for DFS and OS. Two nomograms integrating the three markers with four clinicopathological risk factors were developed and validated for predicting DFS and OS with good calibration and discrimination (C-index: training cohort, 0.798 (95% confidence interval:0.7640.832) and 0.828 (0.796-0.860), respectively; validation cohort, 0.739 (0.696-0.783) and 0.761 (0.715-0.808), respectively). Decision curve analysis demonstrated that the nomograms were clinically useful. Intratumoral IL-37, CD66b(+) neutrophils, and MMR status were independent prognostic factors for CRC patients. Nomograms incorporating these biomarkers and clinicopathological features could be conveniently used to facilitate the individualized prediction of DFS and OS.
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