R software 3

R software 3.63, SPSS software 25.0, and Graphpad Software 9.0 were used to assess data. Data are presented as the mean ± standard error of the mean (SEM). Student's t-test was used to compare the differences between two groups. Correlations between two variations were analyzed by Pearson's correlation. Kaplan-Meier survival curves and log-rank test were employed to depict overall survival (OS). The differences of tumor volume among groups were analyzed by analysis of variance (ANOVA), followed by two-tailed Student's t test. The statistical significances of groups are represented as *p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.

The R software 3.6.3 and IBM SPSS software 25.0 were used for all statistical analyses. The data were presented as mean ± standard deviation. The Wilcoxon signed-rank test was used to compare the PCR results of tumor and paracancerous tissues. The Mann Witney Wilcoxon test was used to compare gene expression and clinicopathological characteristics, and Pearson’s coefficient was used to examine the correlation between hub genes. K-M survival curves were constructed to analyze OS between the high-expression and the low-expression groups. Cox regression was performed to screen the related risk factors for BLCA prognosis. A p-value of less than 0.05 was considered statistically significant.

Levene's test and Kolmogorov–Smirnov test were used to assess the normality and homogeneity of variance. For parametric variables, Student's t-test or one-way analysis of variance (ANOVA) was used for continuous variables and the chi-square test or Fisher exact test for categorical variables. The Mann–Whitney and Wilcoxon tests were used for nonparametric variables. Survival curves were generated and analyzed by the Kaplan–Meier method using the log-rank test. The Cox proportional hazards regression model was applied to assess the prognostic value of each parameter for BCR. Time-dependent ROC analysis was used to measure the predictive power with the “survivalROC” R packages, and the areas under the ROC curve (AUC) of each variable at different time nodes were compared. Meta-analysis (I² <50%, fixed-effect model) was performed to evaluate the prognostic value in the pooled cohort. The Z-score method was used to normalize the risk scores in each cohort. All statistical analyses were performed using IBM SPSS Statistics 24.0 and R software 3.6.3. A two-tailed P < 0.05 was considered to be statistically significant for all statistical analyses.

Descriptive statistics were used to summarize the clinical and molecular characteristics of the patients. Datasets were described by median and/or range. Between‐group comparisons of numerical and categorical data were performed by the Mann‐Whitney U test and the chi‐square test, respectively. Primary endpoints were event‐free survival (EFS) and overall survival (OS). The former was defined as the time from diagnosis to the first event including relapse, death, failure to achieve complete remission, or was censored at the last follow‐up. The latter was the time from diagnosis to death from any cause, or was censored at the last follow‐up. Between‐group comparisons of OS and EFS were performed by the Kaplan‐Meier method and the log‐rank test. Multivariate Cox proportional hazard models were constructed for OS and EFS using a limited backward elimination procedure. Spearman rank correlation was used to determine the associations between gene expression profile and DDIT4 expression. Multiple testing errors were assessed by false discovery rate (FDR). Gene Ontology (GO) enrichment analysis was conducted to assess enrichment of gene expression products associated with DDIT4. All tests were two‐tailed. Statistical significance was defined as P < .05. All statistical analyses were performed by R software 3.5.0, SPSS software 24.0 and GraphPad Prism software 7.0.