This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 17131 genes and 6 clinical features across 143 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
-
2 genes correlated to 'AGE'.
-
STL , PTX3
-
35 genes correlated to 'DISTANT.METASTASIS'.
-
SELT , RASA2 , LDHAL6B , CCNG1 , COL5A1 , ...
-
61 genes correlated to 'LYMPH.NODE.METASTASIS'.
-
CRYM , THNSL1 , NGLY1 , C6ORF162 , TERF1 , ...
-
2 genes correlated to 'NEOPLASM.DISEASESTAGE'.
-
ELP2 , SLC39A6
-
No genes correlated to 'Time to Death', and 'GENDER'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=2 | older | N=2 | younger | N=0 |
GENDER | t test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=35 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=61 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=2 |
Time to Death | Duration (Months) | 0.2-346 (median=47.5) |
censored | N = 69 | |
death | N = 70 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 56.02 (16) |
Significant markers | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
GENDER | Labels | N |
FEMALE | 53 | |
MALE | 90 | |
Significant markers | N = 0 |
DISTANT.METASTASIS | Labels | N |
M0 | 120 | |
M1 | 2 | |
M1A | 2 | |
M1C | 2 | |
Significant markers | N = 35 |
ANOVA_P | Q | |
---|---|---|
SELT | 6.099e-19 | 1.04e-14 |
RASA2 | 1.692e-18 | 2.9e-14 |
LDHAL6B | 8.774e-18 | 1.5e-13 |
CCNG1 | 1.455e-17 | 2.49e-13 |
COL5A1 | 2.872e-14 | 4.92e-10 |
FGFR2 | 5.653e-14 | 9.68e-10 |
AZI2 | 8.623e-13 | 1.48e-08 |
C9ORF140 | 1.767e-12 | 3.03e-08 |
ATG3 | 2.036e-11 | 3.49e-07 |
FHOD1 | 2.686e-11 | 4.6e-07 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 81 | |
N1 | 2 | |
N1A | 6 | |
N1B | 11 | |
N2 | 1 | |
N2A | 1 | |
N2B | 9 | |
N2C | 4 | |
N3 | 10 | |
NX | 2 | |
Significant markers | N = 61 |
ANOVA_P | Q | |
---|---|---|
CRYM | 3.736e-71 | 6.4e-67 |
THNSL1 | 2.447e-70 | 4.19e-66 |
NGLY1 | 1.711e-56 | 2.93e-52 |
C6ORF162 | 8.104e-37 | 1.39e-32 |
TERF1 | 3.052e-34 | 5.23e-30 |
LIMK2 | 8.15e-30 | 1.4e-25 |
AP2S1 | 6.059e-26 | 1.04e-21 |
HES7 | 1.93e-25 | 3.31e-21 |
C17ORF63 | 4.717e-23 | 8.08e-19 |
RNASEH2B | 6.882e-19 | 1.18e-14 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 17 | |
STAGE IA | 9 | |
STAGE IB | 11 | |
STAGE II | 18 | |
STAGE IIA | 7 | |
STAGE IIB | 8 | |
STAGE IIC | 6 | |
STAGE III | 6 | |
STAGE IIIA | 5 | |
STAGE IIIB | 14 | |
STAGE IIIC | 17 | |
STAGE IV | 5 | |
Significant markers | N = 2 |
-
Expresson data file = SKCM-TM.meth.for_correlation.filtered_data.txt
-
Clinical data file = SKCM-TM.clin.merged.picked.txt
-
Number of patients = 143
-
Number of genes = 17131
-
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.