Do inflammatory markers predict prognosis in patients with synchronous colorectal cancer?. Issue 17 (April 2017) - He, Wanbin; Wei, Mingtian; Yang, Xuyang; Chen, Bingchen; Wu, Qingbin; Zheng, Erliang; Deng, XiangBing; Wang, Ziqiang
Abstract : Abstract: Systematic inflammatory response markers are considered as the most informative prognostic factors in many types of cancer. However, in synchronous colorectal cancer (synCRC), the prognostic value of inflammatory markers, including prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (d-NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), had rarely been evaluated. Thus, this present study reviewed our consecutive patients with synCRC to investigate the prognostic value of those factors. The primary endpoint was overall survival (OS), and disease-free survival (DFS) was considered as the secondary endpoint. Receiver-operating characteristic curve analysis was conducted to determine optimal cutoff levels for the 5 markers. Kaplan–Meier survival curves and Cox proportional hazards models were applied to assess the relationship between OS, DFS, and inflammatory markers. In total, 114 patients with pathologically confirmed synCRC at initial diagnosis were identified among 5742 patients who underwent surgery for colorectal cancer from October 2009 to May 2013. In the multivariate analysis, elevated postoperative NLR (≥10.50) was confirmed as an independent prognostic factor for 3-year OS ( P = .001; hazard ratio [HR] 4.123, 95% confidence interval [CI] 1.750–9.567) and DFS ( P = .001; HR 3.342, 95% CI 1.619–6.898). In addition, for 3-year OS, both tumor grade and pN stage were confirmed as independent prognostic factors. And pN stage was confirmed as an independent prognostic factor for 3-year DFS. In conclusion, this study identified elevated postoperative NLR is associated with a poor prognosis in patients with synCRC underwent surgery resection, and the NLR provides improved accuracy for predicting clinical outcomes to stratify patients into different risk categories.