(metastatic tumor cohort)
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 148 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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6 genes correlated to 'AGE'.
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STL , PTX3 , ACTA2 , FAS , SDC2 , ...
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645 genes correlated to 'DISTANT.METASTASIS'.
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C10ORF88 , FAM186A , LOC728758 , ZNF585A , MDM1 , ...
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43 genes correlated to 'LYMPH.NODE.METASTASIS'.
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CRYM , NGLY1 , C6ORF162 , LIMK2 , AP2S1 , ...
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2 genes correlated to 'NEOPLASM.DISEASESTAGE'.
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ELP2 , SLC39A6
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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=6 | older | N=6 | younger | N=0 |
GENDER | t test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=645 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=43 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=2 |
Time to Death | Duration (Months) | 0.2-346 (median=47.5) |
censored | N = 73 | |
death | N = 71 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 55.97 (16) |
Significant markers | N = 6 | |
pos. correlated | 6 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
STL | 0.4129 | 2.228e-07 | 0.00382 |
PTX3 | 0.4063 | 3.606e-07 | 0.00618 |
ACTA2 | 0.4016 | 5.05e-07 | 0.00865 |
FAS | 0.4016 | 5.05e-07 | 0.00865 |
SDC2 | 0.381 | 2.089e-06 | 0.0358 |
CTAGE5 | 0.376 | 2.913e-06 | 0.0499 |
GENDER | Labels | N |
FEMALE | 55 | |
MALE | 93 | |
Significant markers | N = 0 |
DISTANT.METASTASIS | Labels | N |
M0 | 124 | |
M1 | 2 | |
M1A | 2 | |
M1B | 1 | |
M1C | 2 | |
Significant markers | N = 645 |
ANOVA_P | Q | |
---|---|---|
C10ORF88 | 5.499e-70 | 9.42e-66 |
FAM186A | 1.28e-60 | 2.19e-56 |
LOC728758 | 3.019e-56 | 5.17e-52 |
ZNF585A | 2.898e-55 | 4.96e-51 |
MDM1 | 2.313e-54 | 3.96e-50 |
HSPA9 | 9.269e-49 | 1.59e-44 |
PIGX | 2.996e-48 | 5.13e-44 |
ZNF654 | 6.353e-47 | 1.09e-42 |
EFNB3 | 1.979e-46 | 3.39e-42 |
LOC646471 | 1.505e-45 | 2.58e-41 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 81 | |
N1 | 2 | |
N1A | 6 | |
N1B | 12 | |
N2 | 1 | |
N2A | 3 | |
N2B | 9 | |
N2C | 5 | |
N3 | 11 | |
NX | 2 | |
Significant markers | N = 43 |
ANOVA_P | Q | |
---|---|---|
CRYM | 2.634e-74 | 4.51e-70 |
NGLY1 | 6.917e-58 | 1.18e-53 |
C6ORF162 | 6.44e-35 | 1.1e-30 |
LIMK2 | 3.641e-31 | 6.24e-27 |
AP2S1 | 2.571e-26 | 4.4e-22 |
C17ORF63 | 5.478e-23 | 9.38e-19 |
CSRP2BP | 1.176e-16 | 2.01e-12 |
GPR44 | 3.897e-14 | 6.67e-10 |
ASAP3 | 4.317e-12 | 7.39e-08 |
ABI1 | 5.54e-12 | 9.49e-08 |
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 | 7 | |
STAGE IIIA | 5 | |
STAGE IIIB | 15 | |
STAGE IIIC | 17 | |
STAGE IV | 5 | |
Significant markers | N = 2 |
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Expresson data file = SKCM-TM.meth.for_correlation.filtered_data.txt
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Clinical data file = SKCM-TM.clin.merged.picked.txt
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Number of patients = 148
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Number of genes = 17131
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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.