R software 4

The results were expressed as the mean ± standard deviation. Parameter test or rank-sum test was used for comparisons between groups. The Bonferroni test was used for multiple comparisons. Categorical data were analyzed by the chi-square test or Fisher's exact test. For survival analysis, the Kaplan-Meier method, log-rank test, and Cox regression analysis were used to test the prognostic value. All statistical tests were bilateral with p value < 0.05 being statistically significant. R software (4.1.1) and GraphPad Prism 7 were used for data analyses.

All statistical analyses were performed by R software 4.2.2 or GraphPad 8. p-value < 0.05 was deemed to be statistically significant unless noted otherwise. Ns, *, **, ***, and **** stand for p-value >0.05, p-value <=0.05, pvalue <=0.01, pvalue <=0.001, and pvalue <=0.0001, separately. Survival analysis was carried out using the R packages “survival” and “survminer”. We used the Wilcoxon test when comparing two groups and Kruskal-Wallis when comparing more than two groups.

Flow results in this study were analyzed and processed using Flowjo software. All data were statistically analyzed using R software 4.2.2 and GraphPad Prism 8.0. Survival analysis was performed by drawing KM curves, and the survival difference between the two groups was assessed using the log-rank test. The chi-square test was used for categorical variables, while the t-test or non-parametric test was used for continuous variables based on whether they followed a normal distribution. ANOVA analysis was used for comparing multiple groups. p<0.05 indicates a statistically significant difference.

Analyses were performed with R^® software 4.2.0 and GraphPad^® Prism 8.3. The assessment of normality of the data distribution was performed using the Shapiro test and the homogeneity of variances with Levene’s test. When data were normally distributed, two-way analysis of variance (ANOVAs with repeated measures or not) were used for group comparisons, and data are expressed as mean + standard error of the mean (SEM). Otherwise, data are expressed in median ± interquartile. To assess drugs effects, paired t-tests (for data normally distributed) or Wilcoxon paired tests (for data not normally distributed) were used. A significant difference was admitted in all cases when p was below 0.05.

All statistical analyses in this study were performed using the R software 4.1.2 or GraphPad Prism 9. For quantitative data, normally distributed variables were analyzed using the Student’s t-test, and non-normally distributed variables were analyzed using the Wilcoxon rank-sum test. Statistical significant differences between three and more data sets were analyzed using the Kruskal–Wallis method for non-parametric statistical tests and one-way ANOVA for parametric statistical tests. We calculated correlation coefficients using Spearman and distance correlation analysis. Survival analyses were conducted using the Kaplan–Meier method and log-rank tests, while Cox proportional risk regression models were used to analyze the relationship between SUMOylation patterns and regulatory genes and prognosis. All statistical comparisons in this study were two-sided with α = 0.05 and the Benjamini-Hochberg method was employed to control the false discovery rate (FDR) for multiple hypothesis testing. *p < 0.05, **p < 0.01, ***p < 0.001.

Statistical analysis was performed using R software 4.1.2 and GraphPad Prism version 8.4.1. Wilcoxon test and t-test were used to screen DEGs and others (14 (link)). We selected the prognosis genes or factors with univariate Cox regression, and LASSO Cox regression was used for construct the risk model (15 (link)). The method of how to calculate C-index could refer to this literature (16 (link)). Kaplan–Meier method was used for survival analysis (17 (link)). Quantitative values were expressed as mean ± standard deviation. p<0.05 was considered to indicate a statistically significant difference.