This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 16935 genes and 8 clinical features across 29 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.
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1 gene correlated to 'GENDER'.
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UTP14C
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29 genes correlated to 'PATHOLOGICSPREAD(M)'.
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WDR76 , UBL5 , SLC30A7 , MAP6D1 , KLF15 , ...
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No genes correlated to 'Time to Death', 'AGE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', and 'NEOADJUVANT.THERAPY'.
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=0 | ||||
GENDER | t test | N=1 | male | N=1 | female | N=0 |
PATHOLOGY T | Spearman correlation test | N=0 | ||||
PATHOLOGY N | Spearman correlation test | N=0 | ||||
PATHOLOGICSPREAD(M) | ANOVA test | N=29 | ||||
TUMOR STAGE | Spearman correlation test | N=0 | ||||
NEOADJUVANT THERAPY | t test | N=0 |
Time to Death | Duration (Months) | 1-71.7 (median=13.9) |
censored | N = 22 | |
death | N = 3 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 64.28 (14) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 16 | |
MALE | 13 | |
Significant markers | N = 1 | |
Higher in MALE | 1 | |
Higher in FEMALE | 0 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
UTP14C | 7.67 | 1.095e-06 | 0.0185 | 1 |
PATHOLOGY.T | Mean (SD) | 2.79 (0.77) |
N | ||
T1 | 2 | |
T2 | 6 | |
T3 | 17 | |
T4 | 4 | |
Significant markers | N = 0 |
PATHOLOGY.N | Mean (SD) | 0.45 (0.63) |
N | ||
N0 | 18 | |
N1 | 9 | |
N2 | 2 | |
Significant markers | N = 0 |
PATHOLOGICSPREAD(M) | Labels | N |
M0 | 22 | |
M1 | 5 | |
M1A | 1 | |
Significant markers | N = 29 |
ANOVA_P | Q | |
---|---|---|
WDR76 | 3.114e-38 | 5.27e-34 |
UBL5 | 1.831e-20 | 3.1e-16 |
SLC30A7 | 4.39e-16 | 7.43e-12 |
MAP6D1 | 1.79e-15 | 3.03e-11 |
KLF15 | 4.634e-14 | 7.85e-10 |
DERL2 | 1.589e-12 | 2.69e-08 |
MIS12 | 1.589e-12 | 2.69e-08 |
UGDH | 3.331e-11 | 5.64e-07 |
HMG20B | 6.915e-11 | 1.17e-06 |
SEPP1 | 2.748e-09 | 4.65e-05 |
TUMOR.STAGE | Mean (SD) | 2.36 (1.1) |
N | ||
Stage 1 | 6 | |
Stage 2 | 9 | |
Stage 3 | 5 | |
Stage 4 | 5 | |
Significant markers | N = 0 |
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Expresson data file = COAD.meth.for_correlation.filtered_data.txt
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Clinical data file = COAD.clin.merged.picked.txt
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Number of patients = 29
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Number of genes = 16935
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Number of clinical features = 8
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.