Prognostic Value of NRAS Gene for Survival of Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Abstract

Introduction: NRAS gene is associated with malignant proliferation and metastasis of colorectal cancer (CRC). But its prognostic value on CRC is still unknown. The objective of this study is to perform a meta-analysis to obtain its prognostic value on survival of CRC patients. Methods: The systematic review and meta-analysis was designed, undertaken and reported using items from the PRISMA statement. Relevant articles were identified through PubMed (containing Medline), Embase, Web of Science databases and Google scholar search engines from their inception up to October 3, 2016. The articles about NRAS on prognosis of CRC patients were enrolled. The association between NRAS and CRC survival time (including overall survival [OS], progression-free survival [PFS], and disease-free survival [DFS]) was evaluated using hazard ratio (HR) with its corresponding 95% confidence interval (CI). Results: A total of fifteen articles were included. High-expression of NRAS was significantly associated with poor OS (HR: 1.36, 95% CI: 1.15–1.61), and poor PFS (HR: 1.75, 95% CI: 1.04–2.94). The combined HR of NRAS on DFS was 0.87 (95% CI: 0.37–2.03). Subgroup analysis showed that NRAS was significantly associated with poor OS for patients from Western countries (HR: 1.38, 95% CI: 1.09–1.73), but not for those from Asian countries. Conclusions: This meta-analysis demonstrate that NRAS gene could predict the poor prognosis for the CRC patients. More large-sample cohort studies are needed to further confirm this conclusion.

Keywords: NRAS gene; colorectal cancer; prognosis; meta-analysis.

Figure 1

Flow Chart of the Search Strategy

Figure 2

Forest Plot Evaluating the Combined HRs between NRAS and OS

Figure 3

Forest Plot Evaluating the Combined HRs between NRAS and PFS

Figure 4

Forest Plot Evaluating the Combined HRs between NRAS and DFS

Figure 5

Begg’s Funnel and Sensitivity Analysis Plot (A, Begg’s funnel for OS; B, sensitivity analysis for OS; C, Begg’s funnel for PFS; D, sensitivity analysis for PFS; E, Begg’s funnel for DFS; F, sensitivity analysis for DFS)