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Construction of the Prediction Model for Locally Advanced Rectal Cancer Following Neoadjuvant Chemoradiotherapy Based on Pretreatment Tumor-Infiltrating Macrophage-Associated Biomarkers

Overview
Publisher Dove Medical Press
Specialty Oncology
Date 2021 Apr 21
PMID 33880038
Citations 5
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Abstract

Purpose: To assess the value of macrophage-related biomarkers (CD163, CD68, MCSF, and CCL2) for predicting the response to neo-chemoradiotherapy (NCRT) and the prognosis of locally advanced rectal cancer (LARC).

Methods: We enrolled 191 patients who underwent neoadjuvant chemoradiotherapy and radical resection between 2011 and 2015. Tumor tissues were collected before NCRT with a colonoscope and post-surgery and were subjected to immunohistochemical analysis.

Results: The expression levels of macrophage-related biomarkers (CD163, CD68, MCSF, and CCL2) were lower in the pathological complete response (pCR) group when compared with the non-pCR group (all P<0.05). Based on X-tile plots, we divided the tumors in two groups and found that lower pre-NCRT/post-surgical CD163, CD68, MCSF, CCL2 scores correlated with improved DFS. Cox regression analysis demonstrated that pre-NCRT CD163 (HR=1.008, 95% CI 1.003-1.013, =0.003) and MCSF (HR=2.187, 95% CI 1.343-3.564, =0.002) scores were independent predictors of DFS. Based on Cox multivariate analysis, we constructed a risk score model with a powerful ability to predict pCR in LARC patients. Moreover, COX regression analysis was performed to explore the role of the risk score in LARC patients. The results demonstrated that tumor size (HR=1.291, =0.041), worse pathological TNM stage (HR=1.789, =0.005, and higher risk score (HR=1.084, <0.001) were significantly associated with impaired disease-free survival. Based on the above results, a nomogram and decision curve analysis were generated.

Conclusion: The expression levels of macrophage-related biomarkers CD163, CD68, MCSF, and CCL2 were associated with chemoradiotherapy resistance and prognosis in LARC patients following NCRT. A risk score model was constructed which could be used to predict LARC outcome.

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