Rms package

Pearson’s chi-squared test or Fisher’s exact test was applied for categorical variables. Student’s t test was applied for the analysis of continuous variables. Survival curves were established on the basis of the Kaplan–Meier methodology and the significance of differences between survival curves was calculated by means of the log-rank test. Multivariate analysis was constructed through the Cox proportional hazards regression model. Statistical analysis was performed with the help of SPSS Statistics 21.0 (SPSS Inc., Chicago, IL). The nomogram analysis and calibration plot were produced via the R software version 3.0.2 and the ‘rms’ package (R Foundation for Statistical Computing, Vienna, Austria). Harrell’s index of concordance (C-index) and Akaike information criterion (AIC) were both calculated in order to compare and validate the accuracy of the predictive models. All statistical tests were two sided and P < 0.05 was considered statistically significant.

Statistical evaluation was performed using IBM SPSS statistics Version 22 (SPSS Inc; IBM Corporation Software Group, Somers, NY). The Chi-square test or Fisher exact test was utilized in exploratorily comparing baseline characteristics. For pairwise analysis in continuous variables, the correlation with blood leukocyte and tumor infiltration cells was calculated by spearman. OS and DFS were compared with the Kaplan-Meier plotter and the log-rank (Mantel-Cox) test to identify the independent survival beneficial subgroups. Uni- and multivariate analysis of prognostic factors for survival were conducted by cox proportional hazard models to investigate the effect on survival among covariates including the diagnostic age, sex, disease stage, histological type, differential grade and adjuvant treatment. Hazard ratios (HRs) and 95% confidence intervals (CIs) for multivariate analyses were computed using the Cox proportional hazards regression models. Receiver operating characteristics (ROC) analyses were performed to identify the performance of blood leukocytosis. The R software version 3.4.3 and the “rms” package (R Foundation for Statistical Computing) were applied to perform the nomogram analysis and calibration plot. All statistical tests were performed 2-sided, and P-values < 0.05 were considered to be statistically significant.

X-tile plot analysis was conducted to select the optimum cutoff of the H-score to dichotomize the patients into low and high groups36 (link). Comparisons between SLC1A5 expression and clinicopathologic variables were evaluated using Student’s t test, χ2–test and Wilcoxon rank-sum test, as appropriate. Survival curves were conducted by Kaplan-Meier method and compared by log-rank test. Univariate and multivariate Cox proportional-hazard models were exploited to evaluate the hazard ratios and 95% confidence intervals of clinicopathologic variables. Nomogram was set to construct the prognostic model. Calibration plot was used to evaluate the prognostic accuracy of the models. Receiver operating characteristic analysis was conducted to compare the sensitivity and specificity for the prediction of OS by the prognostic models. All statistical tests were two-tailed and differences were considered significant at level of <0.05. Data were analyzed using X-tile software v3.6.1 (Yale University, New Haven, CT, USA), IBM SPSS Statistics 21.0 (IBM Corp, Armonk, New York), MedCalc Software 11.4.2.0 (MedCalc, Mariakerke, Belgium) and R software 3.0.2 with the “rms” package (R Foundation for Statistical Computing, Vienna, Austria).

Analysis was performed with SPSS 21.0 (IBM Corporation, Armonk, NY, USA) and R software version 3.0.2 and the “rms” package (R Foundation for Statistical Computing, Vienna, Austria). Pearson χ² test was used to compare categorical variables, and continuous variables were analyzed by Student's t test. The Kaplan-Meier method with log-rank test was used to compare survival curves. The Cox proportional hazards regression model was applied to perform univariate and multivariate analyses and those variables that achieved statistical significance in the univariate analysis were entered into the multivariable analysis. Furthermore, a nomogram was created by R software using “rms” package. Calibration plots were generated to examine the performance characteristics of the predictive nomogram. The Harrell's Concordance index (C-index) was used to quantify the predictive accuracy. All statistical tests were two-sided and performed at a significance level of 0.05.

MedCalc and Stata 12.0 were used for statistical analysis. Categorical data were analyzed using the Fisher exact or chi-square test. Numerical data were analyzed by the Student t-test. Subgroup OS curves were calculated by the Kaplan-Meier method and compared by log rank test. We used univariate and multivariate Cox proportional hazard models to evaluate the HR and 95% CI. The accuracy of the prognostic factors were evaluated by Harrell's concordance index (C-index). Furthermore, the Akaike information criterion (AIC) value was calculated to evaluate the discriminatory ability of prognostic models, and smaller AIC values present a better predicting ability. We applied the R software and the “rms” package (R Foundation for Statistical Computing, Vienna, Austria) to perform the nomogram analysis and calibration plot. All statistical tests were two sided and P<0.05 was considered statistically significant.