(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 6 clinical features across 100 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
-
1 gene correlated to 'Time to Death'.
-
GSK3A GSK3B|GSK3-ALPHA-BETA-M-V
-
2 genes correlated to 'DISTANT.METASTASIS'.
-
C12ORF5|TIGAR-R-V , PTK2|FAK-R-C
-
2 genes correlated to 'LYMPH.NODE.METASTASIS'.
-
PGR|PR-R-V , ESR1|ER-ALPHA-R-V
-
No genes correlated to 'AGE', 'GENDER', 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=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=2 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=2 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 2.6-346 (median=47.4) |
censored | N = 50 | |
death | N = 47 | |
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 | |
---|---|---|---|---|
GSK3A GSK3B|GSK3-ALPHA-BETA-M-V | 13 | 0.0001949 | 0.034 | 0.639 |
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 = 0.000195 with univariate Cox regression analysis using continuous log-2 expression values.
![](V1ex.png)
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 56.35 (17) |
Significant markers | N = 0 |
Table S4. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 42 | |
MALE | 58 | |
Significant markers | N = 0 |
Table S5. Basic characteristics of clinical feature: 'DISTANT.METASTASIS'
DISTANT.METASTASIS | Labels | N |
M0 | 80 | |
M1 | 1 | |
M1B | 1 | |
M1C | 2 | |
Significant markers | N = 2 |
Table S6. Get Full Table List of 2 genes differentially expressed by 'DISTANT.METASTASIS'
ANOVA_P | Q | |
---|---|---|
C12ORF5|TIGAR-R-V | 0.0001293 | 0.0226 |
PTK2|FAK-R-C | 0.0002578 | 0.0449 |
Figure S2. Get High-res Image As an example, this figure shows the association of C12ORF5|TIGAR-R-V to 'DISTANT.METASTASIS'. P value = 0.000129 with ANOVA analysis.
![](V4ex.png)
Table S7. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 51 | |
N1 | 2 | |
N1A | 3 | |
N1B | 10 | |
N2A | 1 | |
N2B | 5 | |
N2C | 4 | |
N3 | 7 | |
NX | 2 | |
Significant markers | N = 2 |
Table S8. Get Full Table List of 2 genes differentially expressed by 'LYMPH.NODE.METASTASIS'
ANOVA_P | Q | |
---|---|---|
PGR|PR-R-V | 3.734e-07 | 6.53e-05 |
ESR1|ER-ALPHA-R-V | 8.121e-07 | 0.000141 |
Figure S3. Get High-res Image As an example, this figure shows the association of PGR|PR-R-V to 'LYMPH.NODE.METASTASIS'. P value = 3.73e-07 with ANOVA analysis.
![](V5ex.png)
Table S9. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 11 | |
STAGE IA | 8 | |
STAGE IB | 7 | |
STAGE II | 10 | |
STAGE IIA | 5 | |
STAGE IIB | 4 | |
STAGE IIC | 3 | |
STAGE III | 4 | |
STAGE IIIA | 2 | |
STAGE IIIB | 10 | |
STAGE IIIC | 11 | |
STAGE IV | 3 | |
Significant markers | N = 0 |
-
Expresson data file = SKCM-TM.rppa.txt
-
Clinical data file = SKCM-TM.clin.merged.picked.txt
-
Number of patients = 100
-
Number of genes = 175
-
Number of clinical features = 6
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 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.