Prism 7

Assessment of the coexpression genes of PGR was performed using the cBioPortal database (http://www.cbioportal.org). The data obtained were RNA-Seq data from TCGA database that included 549 endometrial cancer tissues. Pearson’s correlation score and Spearman score (≥ 0.3 was considered positively correlated and ≤ − 0.3 was considered negatively correlated with PGR) were used to select PGR coexpressed genes. To predict the target genes that were changed in IshikawaPR, we use FunRich to identify the overlapping genes between DEGs and PGR coexpressed genes.
For validation of target genes, the gene expression profile result, GSE17025, deposited by Day et al. [19 (link)] was used. The gene expression profile has 91 Stage I endometrial cancer patients and 12 postmenopausal healthy tissues. Then we calculated Pearson and Spearman score between target genes and PGR and compared the expression of target genes among healthy and different type of endometrial cancer tissues (p < 0.05 as cut-off criterion) using GraphPad Prism 7.0 and SPSS 22.0 software. Kaplan–Meier curves for target genes were generated with the online tool Kaplan–Meier Plotter (http://www.kmplot.com/). A total of 542 RNA-seq data samples of uterine corpus endometrial carcinoma were interrogated. The patients were split into 2 groups (high vs. low) based on the expression level.

All the statistical analyses using GraphPad Prism 7.0 and SPSS 22.0. Data are presented as the mean ± standard deviation. Differences between the indicated groups were compared using the Student’s t-test and one-way analysis of variance (ANOVA) followed by Fisher’s least significant difference (LSD) test. Correlations were evaluated by Pearson correlation analysis. The log-rank test was used to evaluate significance in the cumulative overall survival (OS) rates calculated using the Kaplan–Meier method. For animal survival studies, blinding during analysis was used for all the in vivo experiments. Animals were randomly assigned to treatment group. A value of p < 0.05 was considered to be significant.

All data were analyzed using GraphPad Prism 7.0 software or SPSS 17.0 for Windows. Results were presented as means ± standard deviation (SD). Student’s t test and one-way analysis of variance (ANOVA) followed by Tukey’s post hoc multiple comparison test was used for data analyses. A p value < 0.05 was considered statistically significant.

GraphPad Prism 7.0 and SPSS 23.0 were used for statistical analyses. Continuous and categorical data were presented as mean ± SD and number (%), respectively. The comparison between the normo-uricemia group and the hyperuricemia group using the parametric t-test or Mann–Whitney. The overall renal survival rate of IgAN was presented using the Kaplan–Meier curve and the difference between the two groups was compared using the log-rank test. The p < 0.05 was regarded as statistical significance in all tests.

GraphPad Prism 7.0 and SPSS 23.0 were used for data analysis. A one-way analysis of variance at a significance level α = 0.05 was used to assess significant differences among groups.

Statistical analysis was performed using GraphPad Prism 7.0 and SPSS 16.0 software.
The Wilcoxon test was employed to assess the differences between two groups in the context of bioinformatics data analysis. Group differences in both in vitro and in vivo experiments were analyzed using a one-way analysis of variance (ANOVA) followed by Dunnett’s post hoc test. Prior to analysis, the normality of data distribution was assessed using the Kolmogorov–Smirnov test, and the homogeneity of variances was assessed using Levene’s test. The results indicated that all data from different groups satisfied the normality and variance homogeneity assumptions. Data are presented as mean ± SD. A p-value less than 0.05 was considered statistically significant.