Wang N, Li X, Luo H, Sun Y, Zheng X, Fan C, Wang H, Ye K, Ge H. Prognostic value of pretreatment inflammatory biomarkers in primary small cell carcinoma of the esophagus.
Thorac Cancer 2019;
10:1913-1918. [PMID:
31389159 PMCID:
PMC6775010 DOI:
10.1111/1759-7714.13164]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 07/23/2019] [Accepted: 07/24/2019] [Indexed: 12/24/2022] Open
Abstract
Background
Growing evidence indicates that several inflammatory biomarkers may predict survival in patients with malignant tumors. The aim of this study was to evaluate the prognostic value of pretreatment biomarkers in patients with primary small‐cell carcinoma of the esophagus (PSCCE).
Methods
A total of 73 PSCCE patients enrolled between January 2009 and December 2017 at the Affiliated Cancer Hospital of Zhengzhou University. The total lymphocyte counts (TLC), neutrophil‐to‐lymphocyte ratio (NLR) and platelet‐to‐lymphocyte ratio (PLR) prior to anticancer therapy were collected as inflammation biomarkers. The cutoff value was determined by Receiver operating characteristic (ROC). The Kaplan‐Meier method was utilized to analyze overall survival (OS). Cox proportional hazards regression was used to identify univariate and multivariate prognostic factors.
Results
Univariate analysis showed that high NLR group (hazard ratio [HR] = 1.685; 95% CI: 1.001–2.838; P = 0.047) and high PLR group (hazard ratio [HR] = 1.716; 95% CI: 1.039–2.834; P = 0.033) were associated with poor OS, and TLC was not correlated with OS. On multivariate analysis, high PLR (hazard ratio [HR] = 1.751; 95% CI: 1.042–2.945; P = 0.035) was an independent prognostic factor of unfavorable OS.
Conclusions
Pretreatment PLR and NLR are correlated with OS. These biomarkers are easily accessible, cost effective, and can serve as a marker to identify high‐risk patients for further designing personalized treatment and predicting treatment outcomes.
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