This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.
Testing the association between 2 variables and 13 clinical features across 193 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables.
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1 variable correlated to 'AGE'.
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MUTATIONRATE_SILENT
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1 variable correlated to 'GENDER'.
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MUTATIONRATE_SILENT
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No variables correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'AGE_mutation.rate', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', 'RACE', and 'ETHNICITY'.
Complete statistical result table is provided in Supplement Table 1
Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of variables that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.
| Clinical feature | Statistical test | Significant variables | Associated with | Associated with | ||
|---|---|---|---|---|---|---|
| DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=0 | ||||
| AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0 |
| AGE | Linear Regression Analysis | N=0 | ||||
| PATHOLOGIC_STAGE | Kruskal-Wallis test | N=0 | ||||
| PATHOLOGY_T_STAGE | Spearman correlation test | N=0 | ||||
| PATHOLOGY_N_STAGE | Wilcoxon test | N=0 | ||||
| PATHOLOGY_M_STAGE | Wilcoxon test | N=0 | ||||
| GENDER | Wilcoxon test | N=1 | male | N=1 | female | N=0 |
| RADIATION_THERAPY | Wilcoxon test | N=0 | ||||
| HISTOLOGICAL_TYPE | Kruskal-Wallis test | N=0 | ||||
| RESIDUAL_TUMOR | Kruskal-Wallis test | N=0 | ||||
| RACE | Kruskal-Wallis test | N=0 | ||||
| ETHNICITY | Wilcoxon test | N=0 |
No variable related to 'DAYS_TO_DEATH_OR_LAST_FUP'.
Table S1. Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'
| DAYS_TO_DEATH_OR_LAST_FUP | Duration (Months) | 0-114.3 (median=19.3) |
| censored | N = 105 | |
| death | N = 87 | |
| Significant variables | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
| AGE | Mean (SD) | 60.24 (14) |
| Significant variables | N = 1 | |
| pos. correlated | 1 | |
| neg. correlated | 0 |
Table S3. Get Full Table List of one variable significantly correlated to 'AGE' by Spearman correlation test
| SpearmanCorr | corrP | Q | |
|---|---|---|---|
| MUTATIONRATE_SILENT | 0.1505 | 0.03767 | 0.0753 |
Table S4. Basic characteristics of clinical feature: 'AGE'
| AGE | Mean (SD) | 60.24 (14) |
| Significant variables | N = 0 |
Table S5. Basic characteristics of clinical feature: 'PATHOLOGIC_STAGE'
| PATHOLOGIC_STAGE | Labels | N |
| STAGE I | 75 | |
| STAGE II | 44 | |
| STAGE III | 3 | |
| STAGE IIIA | 41 | |
| STAGE IIIB | 7 | |
| STAGE IIIC | 6 | |
| STAGE IV | 1 | |
| STAGE IVA | 1 | |
| STAGE IVB | 2 | |
| Significant variables | N = 0 |
Table S6. Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'
| PATHOLOGY_T_STAGE | Mean (SD) | 1.97 (0.96) |
| N | ||
| T1 | 79 | |
| T2 | 50 | |
| T3 | 51 | |
| T4 | 11 | |
| Significant variables | N = 0 |
Table S7. Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'
| PATHOLOGY_N_STAGE | Labels | N |
| N0 | 121 | |
| N1 | 3 | |
| Significant variables | N = 0 |
Table S8. Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'
| PATHOLOGY_M_STAGE | Labels | N |
| class0 | 142 | |
| class1 | 3 | |
| Significant variables | N = 0 |
Table S9. Basic characteristics of clinical feature: 'GENDER'
| GENDER | Labels | N |
| FEMALE | 69 | |
| MALE | 124 | |
| Significant variables | N = 1 | |
| Higher in MALE | 1 | |
| Higher in FEMALE | 0 |
Table S10. Get Full Table List of one variable differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.
| W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC | |
|---|---|---|---|---|
| MUTATIONRATE_SILENT | 5055 | 0.03681 | 0.0537 | 0.5908 |
Table S11. Basic characteristics of clinical feature: 'RADIATION_THERAPY'
| RADIATION_THERAPY | Labels | N |
| NO | 174 | |
| YES | 3 | |
| Significant variables | N = 0 |
Table S12. Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'
| HISTOLOGICAL_TYPE | Labels | N |
| FIBROLAMELLAR CARCINOMA | 1 | |
| HEPATOCELLULAR CARCINOMA | 189 | |
| HEPATOCHOLANGIOCARCINOMA (MIXED) | 3 | |
| Significant variables | N = 0 |
Table S13. Basic characteristics of clinical feature: 'RESIDUAL_TUMOR'
| RESIDUAL_TUMOR | Labels | N |
| R0 | 159 | |
| R1 | 10 | |
| RX | 17 | |
| Significant variables | N = 0 |
Table S14. Basic characteristics of clinical feature: 'RACE'
| RACE | Labels | N |
| AMERICAN INDIAN OR ALASKA NATIVE | 1 | |
| ASIAN | 53 | |
| BLACK OR AFRICAN AMERICAN | 14 | |
| WHITE | 117 | |
| Significant variables | N = 0 |
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Expresson data file = LIHC-TP.patients.counts_and_rates.txt
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Clinical data file = LIHC-TP.merged_data.txt
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Number of patients = 193
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Number of variables = 2
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Number of clinical features = 13
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Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.
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There are also useful links about clinical features.
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Survival time data
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Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.
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if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'
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if 'vital_status'==0(alive),
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if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'
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if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.
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if 'vital_status'==NA,excludes this case in survival analysis and report the case.
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cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .
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This analysis excluded clinical variables that has only NA values.
For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels
For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R
For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.
For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.