# Quickly Analyze Cancer Data with Data from UCSCXenaShiny

#### Shixiang Wang

#### 2025-12-17

    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 2.2.0
    #> Project URL: https://github.com/openbiox/UCSCXenaShiny
    #> Usages: https://openbiox.github.io/UCSCXenaShiny/
    #>
    #> If you use it in published research, please cite:
    #>   Shensuo Li, Yuzhong Peng, Minjun Chen, Yankun Zhao, Yi Xiong, Jianfeng Li, Peng Luo,
    #>   Haitao Wang, Fei Zhao, Qi Zhao, Yanru Cui, Sujun Chen, Jian-Guo Zhou, Shixiang Wang,
    #>   Facilitating integrative and personalized oncology omics analysis with UCSCXenaShiny,
    #>   Communications Biology, 1200 (2024),  https://doi.org/10.1038/s42003-024-06891-2
    #> =========================================================================================
    #>                               --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.