Inverse correlation between Naa10p and MMP-9 expression and the combined prognostic value in breast cancer patients

To analyze the expression profiles of N -a-acetyltransferase 10 protein (Naa10p/ARD1) and matrix metalloproteinase 9 (MMP-9) in human breast cancer and evaluate their possible prognostic values in breast cancer patients. Quantitative RT-PCR was used to evaluate mRNA expression of Naa10p and MMP-9 in 55 cases of fresh breast cancer tissues, and immunohistochemistry was performed for detecting Naa10p and MMP-9 proteins on breast cancer with tissue microarray containing 80 specimens. Furthermore, Naa10p and MMP-9 were measured in 253 breast cancer tissues from patients with up to 15-year follow-up. Survival curves were generated using the Kaplan–Meier method. Multivariate analysis was performed by using the Cox proportional hazard regression model to assess the prognostic values of Naa10p and MMP-9. Both Naa10p and MMP-9 expression in breast cancer tissues were significantly higher than those in the matched non-cancerous tissues ( p = 0.000 for both). There was an inverse correlation between Naa10p and MMP-9 expression at mRNA and protein levels ( p = 0.000 for both). Patients with MMP-9-positive expression had a poorer overall survival (OS) and disease-free survival (DFS) than those with MMP-9-negative expression ( p = 0.001 for both). However, patients with Naa10p-positive expression had better OS and DFS ( p = 0.000 for both). In addition, Naa10p-positive/MMP-9-negative patients had the best OS and DFS ( p = 0.000 for both). In multivariate survival analysis, TNM stage, Naa10p expression, MMP-9 expression, and combined expression status of Naa10p/MMP-9 were independent prognostic factors related to OS ( p = 0.000, 0.007, 0.012, and 0.000, respectively) and DFS ( p = 0.000, 0.002, 0.014, and 0.000, respectively).The expression level of Naa10p was inversely correlated with that of MMP-9 in human breast cancer samples. Combined analysis of Naa10p and MMP-9 had a significantly increased value for determining the prognosis of breast cancer patients.