Abstract

PurposeSialic acid-binding immunoglobulin-like lectins (siglecs) family has important functions in tumor progression. The purpose of our study is to figure out the correlation between the expression level of Siglec-8 and prognosis of patients with clear cell renal cell carcinoma (ccRCC), and then to predict the overall survival (OS) via a novel nomogram. Materials and methodsA group of patients (n = 267) histologically diagnosed with ccRCC from Zhongshan Hospital were included into our study. Immunohistochemistry of Siglec-8 was performed in the tissue microarray, and the staining intensity was divided into high/low according to the median value of the H-score grading. Survival analyses including Kaplan-Meier analyses and Cox regression analyses were performed to evaluate the association between Siglec-8 expression and the survival of patients in different risk groups. Stage, size, grade, and necrosis score and University of California Los Angeles Integrated Staging System score were used in the risk stratification. A nomogram incorporating Siglec-8 and several other clinical parameters was plotted for predicting the 5-year and 8-year OS. ResultsSiglec-8 was observed dominantly on the membrane of tumor cells. The enhanced expression level of Siglec-8 had significant correlation with adverse overall and disease-free survival of patients (P<0.0001 and P = 0.0186, respectively). The association was more significant in patients with lower risk. Cox regression analyses defined Siglec-8 as an independent prognostic factor of OS (P<0.001 for univariate analysis, P = 0.003 for multivariate analysis). The new nomogram integrating Siglec-8 with several traditional prognostic factors proved to be more accurate than conventional prognostic system using tumor node metastasis stage only (Harrell c-index: 0.801, 95% CI: 0.755–0.847 vs. 0.717, 95% CI: 0.662–0.772). ConclusionOur study has found that the elevated expression level of Siglec-8 was correlated with poor prognosis of patients with ccRCC. Siglec-8, incorporation with other clinical parameters, could perform better in prediction of patients׳ OS.

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