(metastatic tumor cohort)
This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 175 genes and 3 clinical features across 97 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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1 gene correlated to 'Time to Death'.
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GSK3A GSK3B|GSK3-ALPHA-BETA-M-V
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No genes correlated to 'AGE', and 'GENDER'.
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 genes that are significantly associated with each clinical feature at Q value < 0.05.
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 2.6-346 (median=47.3) |
censored | N = 49 | |
death | N = 45 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
Table S2. Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
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GSK3A GSK3B|GSK3-ALPHA-BETA-M-V | 13 | 0.0001756 | 0.031 | 0.644 |
Figure S1. Get High-res Image As an example, this figure shows the association of GSK3A GSK3B|GSK3-ALPHA-BETA-M-V to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 0.000176 with univariate Cox regression analysis using continuous log-2 expression values.
![](V1ex.png)
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 56.66 (17) |
Significant markers | N = 0 |
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Expresson data file = SKCM-TM.rppa.txt
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Clinical data file = SKCM-TP.clin.merged.picked.txt
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Number of patients = 97
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Number of genes = 175
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Number of clinical features = 3
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-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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.