This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 175 genes and 7 clinical features across 109 samples, statistically thresholded by Q value < 0.05, 3 clinical features 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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1 gene correlated to 'DISTANT.METASTASIS'.
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C12ORF5|TIGAR-R-V
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2 genes correlated to 'LYMPH.NODE.METASTASIS'.
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PGR|PR-R-V , ESR1|ER-ALPHA-R-V
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No genes correlated to 'AGE', 'PRIMARY.SITE.OF.DISEASE', 'GENDER', and 'NEOPLASM.DISEASESTAGE'.
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=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
PRIMARY SITE OF DISEASE | ANOVA test | N=0 | ||||
GENDER | t test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=1 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=2 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=0 |
Time to Death | Duration (Months) | 2.6-357.4 (median=47.5) |
censored | N = 50 | |
death | N = 58 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
GSK3A GSK3B|GSK3-ALPHA-BETA-M-V | 9.1 | 0.0002674 | 0.047 | 0.624 |
AGE | Mean (SD) | 56.4 (17) |
Significant markers | N = 0 |
PRIMARY.SITE.OF.DISEASE | Labels | N |
DISTANT METASTASIS | 13 | |
PRIMARY TUMOR | 1 | |
REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) | 15 | |
REGIONAL LYMPH NODE | 80 | |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 44 | |
MALE | 65 | |
Significant markers | N = 0 |
DISTANT.METASTASIS | Labels | N |
M0 | 90 | |
M1 | 1 | |
M1B | 2 | |
M1C | 2 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
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C12ORF5|TIGAR-R-V | 0.0001783 | 0.0312 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 56 | |
N1 | 2 | |
N1A | 3 | |
N1B | 13 | |
N2A | 2 | |
N2B | 5 | |
N2C | 4 | |
N3 | 9 | |
NX | 2 | |
Significant markers | N = 2 |
ANOVA_P | Q | |
---|---|---|
PGR|PR-R-V | 3.618e-08 | 6.33e-06 |
ESR1|ER-ALPHA-R-V | 8.673e-08 | 1.51e-05 |
NEOPLASM.DISEASESTAGE | Labels | N |
I OR II NOS | 3 | |
STAGE I | 11 | |
STAGE IA | 8 | |
STAGE IB | 8 | |
STAGE II | 10 | |
STAGE IIA | 5 | |
STAGE IIB | 5 | |
STAGE IIC | 3 | |
STAGE III | 6 | |
STAGE IIIA | 2 | |
STAGE IIIB | 11 | |
STAGE IIIC | 13 | |
STAGE IV | 4 | |
Significant markers | N = 0 |
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Expresson data file = SKCM-TM.rppa.txt
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Clinical data file = SKCM-TM.clin.merged.picked.txt
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Number of patients = 109
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Number of genes = 175
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Number of clinical features = 7
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.