This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 166 genes and 5 clinical features across 200 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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22 genes correlated to 'HISTOLOGICAL.TYPE'.
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TP53|P53-R-V , AKT1 AKT2 AKT3|AKT_PS473-R-V , PGR|PR-R-V , CDH1|E-CADHERIN-R-V , ESR1|ER-ALPHA-R-V , ...
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1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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CHEK1|CHK1_PS345-R-C
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3 genes correlated to 'COMPLETENESS.OF.RESECTION'.
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TP53|P53-R-V , ESR1|ER-ALPHA-R-V , BAK1|BAK-R-C
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No genes correlated to 'Time to Death', and 'AGE'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=22 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=1 | yes | N=1 | no | N=0 |
COMPLETENESS OF RESECTION | ANOVA test | N=3 |
Time to Death | Duration (Months) | 0.6-185.8 (median=29.2) |
censored | N = 180 | |
death | N = 20 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 62.73 (11) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | 174 | |
MIXED SEROUS AND ENDOMETRIOID | 3 | |
SEROUS ENDOMETRIAL ADENOCARCINOMA | 23 | |
Significant markers | N = 22 |
ANOVA_P | Q | |
---|---|---|
TP53|P53-R-V | 3.531e-13 | 5.86e-11 |
AKT1 AKT2 AKT3|AKT_PS473-R-V | 7.868e-09 | 1.3e-06 |
PGR|PR-R-V | 1.195e-08 | 1.96e-06 |
CDH1|E-CADHERIN-R-V | 1.945e-08 | 3.17e-06 |
ESR1|ER-ALPHA-R-V | 2.978e-08 | 4.82e-06 |
CHEK2|CHK2_PT68-R-C | 1.102e-07 | 1.77e-05 |
CDC2|CDK1-R-V | 1.482e-07 | 2.37e-05 |
AKT1 AKT2 AKT3|AKT_PT308-R-V | 1.476e-06 | 0.000235 |
ESR1|ER-ALPHA_PS118-R-V | 3.174e-06 | 0.000501 |
PTEN|PTEN-R-V | 5.508e-06 | 0.000865 |
One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 80 | |
YES | 120 | |
Significant markers | N = 1 | |
Higher in YES | 1 | |
Higher in NO | 0 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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CHEK1|CHK1_PS345-R-C | 3.69 | 0.0002956 | 0.0491 | 0.631 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 141 | |
R1 | 9 | |
R2 | 5 | |
RX | 16 | |
Significant markers | N = 3 |
ANOVA_P | Q | |
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TP53|P53-R-V | 2.677e-06 | 0.000444 |
ESR1|ER-ALPHA-R-V | 0.0001445 | 0.0238 |
BAK1|BAK-R-C | 0.0002593 | 0.0425 |
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Expresson data file = UCEC-TP.rppa.txt
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Clinical data file = UCEC-TP.merged_data.txt
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Number of patients = 200
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Number of genes = 166
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Number of clinical features = 5
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 multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R
For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R
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.