BackgroundThe neutrophil-to-lymphocyte ratio (NLR) is an inflammatory index calculated by the absolute neutrophil count dividing the absolute lymphocyte count, and its prognostic role in esophageal cancer (EC) patients with anti-PD-1 therapy remains unclear.MethodsA total of 140 unresectable or metastatic EC patients receiving PD-1 inhibitor treatment were included from Jan 2016 to Mar 2020. Kaplan–Meier method and log-rank test were used for comparing overall survival (OS) and progression-free survival (PFS) between groups. Multivariate Cox analysis was performed to assess the prognostic value of NLR.ResultsThe cutoff value of NLR was set at 5, and the median follow-up time was 20.0 months. Patients with pretreatment NLR <5 had higher ORR (46.7% vs. 12.1%; p < 0.001) and DCR (85.0% vs. 69.7%; p = 0.047) than those with NLR ≥5. Kaplan–Meier curves showed that pretreatment NLR <5 was associated with longer PFS (median: 10.0 vs. 3.5 months, p < 0.0001) and OS (median: 22.3 vs. 4.9 months, p < 0.0001). Multivariate analysis demonstrated that pretreatment NLR ≥5 independently and significantly increased the risk of disease progression (hazard ratio (HR), 1.77 (95% confidence interval (CI), 1.12–2.82); p = 0.015) and death (HR, 4.01 (95% CI, 2.28–7.06); p < 0.001). Subgroup analysis showed that pretreatment NLR ≥5 was associated with poor efficacy and survival in most subsets.ConclusionsOur findings showed that pretreatment NLR was independently and significantly associated with the efficacy and prognosis of EC patients treated with PD-1 inhibitors. NLR could serve as a convenient and useful prognostic biomarker for EC patients with anti-PD-1 therapy.

Background:Cervical cancer is still the major cause of cancer-related death among women. However, the prognosis of cervical cancer varies even in the same stage. Thus, exploring prognostic biomarkers that could reflect its biological heterogeneity may contribute to identify patients with a poor prognosis.Methods:Based on the ESTIMATE algorithm, we acquired the immune/stromal scores of cervical squamous cell carcinoma (CSCC) patients collected from The Cancer Genome Atlas (TCGA) dataset. Subsequently, we analyzed the DEGs between high- and low immune score groups using R package edgeR and performed K-M analysis to illustrate the relationship between differentially expressed genes (DEGs) and the overall survival to select survival-related DEGs. Then the LASSO regression model was constructed with the package “glmnet” in R to evaluate the riskscore of each patient. Finally, we developed a nomogram composing riskscore and clinicopathological characteristics to predict the overall survival (OS) of CSCC patients. The R software v3.6.1 was used for statistical analyses. All statistical tests were two-tailed.Results:We established a riskscore model composed of two genes including FOXP3 and ZAP70. The receiver operating characteristic (ROC) curve demonstrated a good potency of the riskscore model. Ultimately, we constructed a nomogram composing riskscore, age and stage to predict the overall survival (OS) of CSCC patients. The area under the ROC curve (AUC) of the nomogram for OS was 0.805, 0.723 and 0.748 for the first, third, and fifth years, respectively. The concordance index (C-index) was 0.746. The calibration curves also showed optimal accuracy of the nomogram for survival prediction. Conclusion:The nomogram based on riskscore could predict overall survival in CSCC and may benefit those patients through individualized immunotherapy.