(All_Primary 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 5 clinical features across 12 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 'AGE'.
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MAP2K1|MEK1_PS217_S221-R-V
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No genes correlated to 'Time to Death', 'PRIMARY.SITE.OF.DISEASE', 'LYMPH.NODE.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
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 | ||
|---|---|---|---|---|---|---|
| Time to Death | Cox regression test | N=0 | ||||
| AGE | Spearman correlation test | N=1 | older | N=0 | younger | N=1 |
| PRIMARY SITE OF DISEASE | ANOVA test | N=0 | ||||
| LYMPH NODE METASTASIS | ANOVA test | N=0 | ||||
| NEOPLASM DISEASESTAGE | ANOVA test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
| Time to Death | Duration (Months) | 0-27 (median=5.6) |
| censored | N = 5 | |
| death | N = 4 | |
| Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
| AGE | Mean (SD) | 56.4 (16) |
| Significant markers | N = 1 | |
| pos. correlated | 0 | |
| neg. correlated | 1 |
Table S3. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test
| SpearmanCorr | corrP | Q | |
|---|---|---|---|
| MAP2K1|MEK1_PS217_S221-R-V | -0.9147 | 0.0002092 | 0.0366 |
Figure S1. Get High-res Image As an example, this figure shows the association of MAP2K1|MEK1_PS217_S221-R-V to 'AGE'. P value = 0.000209 with Spearman correlation analysis. The straight line presents the best linear regression.
Table S4. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'
| PRIMARY.SITE.OF.DISEASE | Labels | N |
| DISTANT METASTASIS | 2 | |
| PRIMARY TUMOR | 8 | |
| REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) | 2 | |
| Significant markers | N = 0 |
Table S5. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'
| LYMPH.NODE.METASTASIS | Labels | N |
| N0 | 3 | |
| N1A | 1 | |
| N2A | 1 | |
| N2B | 3 | |
| N3 | 2 | |
| NX | 1 | |
| Significant markers | N = 0 |
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Expresson data file = SKCM-All_Primary.rppa.txt
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Clinical data file = SKCM-All_Primary.clin.merged.picked.txt
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Number of patients = 12
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Number of genes = 175
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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 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.