Combined features based on MT1-MMP expression, CD11b + immunocytes density and LNR predict clinical outcomes of gastric cancer
1 Department of Oncology, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District Wuhan 430071, People’s Republic China
2 Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District Wuhan 430071, People’s Republic China
3 Department of Pathology, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District Wuhan, 430071, People’s Republic, China
4 Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), College of Chemistry and Molecular Sciences, and State Key Laboratory of Virology, Wuhan University, Wuhan, 430072, People’s Republic China
Journal of Translational Medicine 2013, 11:153 doi:10.1186/1479-5876-11-153Published: 20 June 2013
Given the complexity of tumor microenvironment, no single marker from cancer cells could adequately predict the clinical outcomes of gastric cancer (GC). The objective of this study was to evaluate the prognostic role of combined features including conventional pathology, proteinase and immune data in GC.
In addition to pathological studies, immunohistochemistry was used to assess membrane-type 1 matrix metalloproteinase (MT1-MMP) expression and CD11b + immunocytes density in three independent GC tissue microarrays containing 184 GC tissues. Separate and combined features were evaluated for their impact on overall survival (OS).
We found that traditional factors including tumor size, histological grade, lymph node status, serosa invasion and TNM stage were associated with OS (P < 0.05 for all). Moreover, statistically significant differences in OS were found among lymph node ratio (LNR) subgroups (P < 0.001), MT1-MMP subgroups (P = 0.015), and CD11b + immunocytes density subgroups (P = 0.031). Most importantly, combined feature (MT1-MMP positive, low CD11b + immunocytes density and high LNR) was found by multivariate analysis to be an independent prognostic factors for OS after excluding other confounding factors (HR = 3.818 [95%CI: 2.223-6.557], P < 0.001). In addition, this combined feature had better performance in predicting clinical outcomes after surgery long before recurrence had occurred (Area under the curve: 0.689 [95%CI: 0.609-0.768], P < 0.001).
These findings indicate that better information on GC prognosis could be obtained from combined clinico-pathological factors, tumor cells and the tumor microenvironment.