The prognostic significance of carcinoma-associated fibroblasts and tumor-associated macrophages in nasopharyngeal carcinoma

Abstract

Purpose: Tumor stroma cells play an important role in the carcinogenesis and progression of cancer. The aim of the present investigation was to explore the predictive role of carcinoma-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs) in nasopharyngeal carcinoma (NPC). Patients and methods: The densities of CAFs and TAMs were measured by immunohistochemistry staining for α-smooth muscle actin (α-SMA), CD68, and CD163 in two sets of tissue microarrays including 260 pretreatment NPC tissues, that is, a training test comprising of 152 patients and a validation set comprising of 108 patients. Chi-square tests were performed for comparisons among the groups. Survival rates were estimated by using the Kaplan–Meier method and compared with log-rank tests. Cox proportional hazards models were used to identify significant independent variables. Results: Patients older than 50 years showed a lower expression of CD68, and there was a positive relationship between the densities of CAFs and CD163 + TAMs ( p =0.001). In the multivariate analysis of the training test, both α-SMA and CD163 were independent prognostic factors for overall survival and progression-free survival (all p <0.05). Based on the expression levels of α-SMA and CD163, patients were categorized into three groups: high-risk, intermediate-risk, and low-risk groups according to both high, either high, and both low, respectively. Survival analysis and Cox multivariate analysis showed that the risk groups based on α-SMA and CD163 expression were independent predictors for the survival of patients with NPC in the training test, which was also confirmed by the validation test. Conclusion: A patient’s risk group based on the level of CD163 + TAMs and CAFs was an independent predictor of survival, which may facilitate patient counseling and individualized treatment. Keywords: CD68, CD163, α-SMA, risk groups, multivariate analysis