Prism five

Data are presented as the mean ± S.E.M of 3−5 independent experiments. Statistical analyses were performed using GraphPad Prism five software (GraphPad Software, La Jolla, CA, United States). One-way analysis of variance (ANOVA) followed by Bonferroni’s post-comparison test (>two groups) or two sample t-test (two groups) was used to compare the mean differences among the groups. Data were considered to be statistically significant at (*p < 0.05, **p < 0.01, ***p < 0.001, or ****p < 0.0001).

All assays were performed in at least three independent experiments (unless specified). GraphPad Prism five software (GraphPad Software) was used for statistical analyses. Statistical tests used are described in each result section.

Bioluminescence data were analyzed with the Actimetrics LumiCycle analysis software (Actimetrics LTD, Wilmette, IL). RNA-seq data and qPCR data analysis were performed using RStudio, GraphPad Prism five and Excel 2016. Panther analyses were performed using the PANTHER version 12.0 released on 10.07.2017. Statistical analyses were performed using Student’s t-test. Differences were considered significant for (*) p-value <0.05, (**) p-value <0.01, and (***) p-value <0.001. Exact p-values and raw data for Figures 2 and 3 are listed in Supplementary file 2.

R software 3.6.3 (https://www.R-project.org/) and GraphPad Prism five were used for statistical analysis. Student’s t test was applied to calculate the p-value of continuous variables. When data were not normally distributed, the Wilcoxon test was used. Fisher’s exact test was performed to compare categorical variables. Log-rank test was used for survival analysis. Pearson correlation coefficients were used to compare the relationship of two variables. A two-sided p-value < 0.05 was considered statistically significant unless otherwise specified. The R package pheatmap (v1.0.12) was used to scale and visualize the expression of ac4C-DEGs between tumor and normal tissues, and stemness-related genes and score of immune-related pathways between the ac4Cscore-high and ac4Cscore-low groups. The R package ggalluvial (v0.12.3) was used to visualize the attribution of immune subtype to individual patients in different ac4Cscore groups. The R package corrplot (v0.84) was applied to draw the correlation of ac4Cscore and immune signatures. The R package circlize (v0.4.14) was utilized for ring heatmap (Gu et al., 2014 (link)). The R package ggradar (v0.2) was used to display the distribution of immune cells. The R package timeROC (v0.4) was used for receiver operating characteristic analysis and to quantify the area under the curve.

Results were expressed as mean ± standard deviation. All data were analyzed using one way analysis of variance followed by Tukey’s test. Values of p < 0.05 were considered as significant. All the statistical analyses were performed using graph pad prism five software (Graph Pad Software, Inc., San Diego, CA, United States).