Stata version 13.0

Patient demographic and clinical characteristics were compared according to SMAD4 mutational status. Comparisons across groups were done using chi-square tests and the Fisher’s exact test. Logistic regression analysis was performed to calculate the odds ratio for development of SMAD4-positive tumors according to various demographic and clinical characteristics. The Fisher exact test was also used to determine the association of SMAD4 and TGF-β protein mutations across the CMS classifications.
Also, survival analysis was performed to determine whether the SMAD4 mutation status plays a role in clinical outcomes of metastatic CRC. OS was defined as the time from diagnosis of CRC to death owing to any cause and analyzed using the Kaplan-Meier method. Log-rank testing was used to compare survival curves across mutational statuses. Univariate and multivariate Cox regression analyses were performed to determine the association of various factors with OS in metastatic CRC cases. Variables, which were associated with poor OS in the univariate analysis, were included in the multivariate Cox regression analysis. All p values were 2-sided, and statistical significance was set at p<0.05. Statistical analyses were performed using the Stata (version 13.0) and SPSS Windows (version 16.0) software programs (SPSS Inc, Chicago, IL, USA).

In this study, R version 3.6.4, Stata version 13.0, and SPSS version 22.0 were used for statistical analysis. The measurement data conforming to the normal distribution were expressed as mean ± standard deviation (X ± S), and the independent sample t-test was used for intergroup comparison. Non-normally distributed data were represented by the median (M) and interquartile ranges (M (P25, P75)), and nonparametric tests were used for intergroup comparison. For the assessment of normality, we used the Shapiro-Wilk test. The counting data were expressed as frequency and percentage (%), and the chi-square test was used for intergroup comparison. P < 0.05 indicates statistical significance. A nomogram was established according to the results of univariate and multivariate logistic regression analysis. The area under the receiver-operating characteristic (AUC-ROC) curve was used to verify the discrimination of the nomogram, and a bootstrap self-sampling method (B = 1000) was used to internally validate the model and plot calibration curves. Finally, decision curve analysis (DCA) was used to confirm the clinical benefit of this nomogram.