Quickly Analyze Cancer Data with Data from UCSCXenaShiny

Shixiang Wang

Central South University
wangshx@csu.edu.cn

2025-09-22

library(bregr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

if (!requireNamespace("UCSCXenaShiny")) {
  install.packages("UCSCXenaShiny")
}
#> Loading required namespace: UCSCXenaShiny
library(UCSCXenaShiny)
#> =========================================================================================
#> UCSCXenaShiny version 2.1.0
#> Project URL: https://github.com/openbiox/UCSCXenaShiny
#> Usages: https://openbiox.github.io/UCSCXenaShiny/
#> 
#> If you use it in published research, please cite:
#>   Shixiang Wang, Yi Xiong, Longfei Zhao, Kai Gu, Yin Li, Fei Zhao, Jianfeng Li,
#>   Mingjie Wang, Haitao Wang, Ziyu Tao, Tao Wu, Yichao Zheng, Xuejun Li, Xue-Song Liu,
#>   UCSCXenaShiny: An R/CRAN Package for Interactive Analysis of UCSC Xena Data, 
#>   Bioinformatics, 2021;, btab561, https://doi.org/10.1093/bioinformatics/btab561.
#> =========================================================================================
#>                               --Enjoy it--

UCSCXenaShiny offers extensive builtin cancer datasets and data query functions to facilitate analysis and visualization.

Obtain Data

data <- inner_join(
  tcga_clinical_fine,
  tcga_surv |> select(sample, OS, OS.time),
  by = c("Sample" = "sample")
) |> filter(!is.na(Stage_ajcc), !is.na(Gender))
head(data)
#> # A tibble: 6 × 10
#>   Sample Cancer   Age Code  Gender Stage_ajcc Stage_clinical Grade    OS OS.time
#>   <chr>  <chr>  <dbl> <chr> <chr>  <chr>      <chr>          <chr> <dbl>   <dbl>
#> 1 TCGA-… ACC       58 TP    MALE   Stage II   <NA>           <NA>      1    1355
#> 2 TCGA-… ACC       44 TP    FEMALE Stage IV   <NA>           <NA>      1    1677
#> 3 TCGA-… ACC       23 TP    FEMALE Stage III  <NA>           <NA>      0    2091
#> 4 TCGA-… ACC       30 TP    MALE   Stage III  <NA>           <NA>      1     365
#> 5 TCGA-… ACC       29 TP    FEMALE Stage II   <NA>           <NA>      0    2703
#> 6 TCGA-… ACC       30 TP    FEMALE Stage III  <NA>           <NA>      1     490

Execute bregr Pipeline

Assessing the influence of AJCC Stage on overall survival can be done by analyzing data grouped by gender.

m <- br_pipeline(
  data = data,
  y = c("OS.time", "OS"),
  x = "Stage_ajcc", x2 = "Age",
  group_by = "Gender",
  method = "coxph"
)
#> exponentiate estimates of model(s) constructed from coxph method at default
m
#> an object of <breg> class with slots:
#> • y (response variable): OS.time and OS
#> • x (focal term): Stage_ajcc
#> • x2 (control term): Age
#> • group_by: Gender
#> • data: <tibble[,11]>
#> • config: <list: method = "coxph", extra = "">
#> • models: <list: FEMALE_Stage_ajcc = <coxph>, MALE_Stage_ajcc = <coxph>, and
#> All_Stage_ajcc = <coxph>>
#> • results: <tibble[,22]> with colnames Group_variable, Focal_variable, term,
#> variable, var_label, var_class, var_type, var_nlevels, contrasts,
#> contrasts_type, reference_row, label, n_obs, n_ind, n_event, exposure,
#> estimate, std.error, …, conf.low, and conf.high
#> • results_tidy: <tibble[,9]> with colnames Group_variable, Focal_variable,
#> term, estimate, std.error, statistic, p.value, conf.low, and conf.high
#> 
#> Focal term(s) are injected into the model one by one,
#> while control term(s) remain constant across all models in the batch.
br_get_results(m, tidy = TRUE) |>
  knitr::kable()
Group_variable Focal_variable term estimate std.error statistic p.value conf.low conf.high
FEMALE Stage_ajcc Stage_ajccStage II 1.273682 0.0959397 2.521499 0.0116856 1.055351 1.537181
FEMALE Stage_ajcc Stage_ajccStage III 2.149478 0.0967063 7.912875 0.0000000 1.778347 2.598062
FEMALE Stage_ajcc Stage_ajccStage IV 5.178744 0.1081300 15.209118 0.0000000 4.189710 6.401251
FEMALE Stage_ajcc Age 1.035787 0.0025991 13.528430 0.0000000 1.030524 1.041077
MALE Stage_ajcc Stage_ajccStage II 1.768884 0.0833656 6.841537 0.0000000 1.502237 2.082861
MALE Stage_ajcc Stage_ajccStage III 2.235855 0.0811756 9.912137 0.0000000 1.906983 2.621443
MALE Stage_ajcc Stage_ajccStage IV 3.379213 0.0849960 14.325886 0.0000000 2.860664 3.991759
MALE Stage_ajcc Age 1.029710 0.0023311 12.559176 0.0000000 1.025016 1.034425
All Stage_ajcc Stage_ajccStage II 1.468986 0.0628836 6.115624 0.0000000 1.298647 1.661668
All Stage_ajcc Stage_ajccStage III 2.193520 0.0621347 12.642015 0.0000000 1.942014 2.477597
All Stage_ajcc Stage_ajccStage IV 4.014838 0.0665694 20.880417 0.0000000 3.523741 4.574377
All Stage_ajcc Age 1.033083 0.0017248 18.869895 0.0000000 1.029596 1.036581

Generate Visualizations

For example:

m <- br_rename_models(m, c("Female", "Male", "All"))
#> rename model names from "FEMALE_Stage_ajcc", "MALE_Stage_ajcc", and
#> "All_Stage_ajcc" to "Female", "Male", and "All"

br_show_forest_ggstats(m)

Explore Further

Besides, using tcga_surv_get(), you can efficiently retrieve values for a specified gene (c("mRNA", "miRNA", "methylation", "transcript", "protein", "mutation", "cnv")) from the TCGA cohort.

For more comprehensive guidance on querying various omics data from different databases/cohorts, refer to the Molecular Data Query section of the UCSCXenaShiny tutorial book.