(primary solid tumor cohort)
This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 16900 genes and 7 clinical features across 54 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.
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3 genes correlated to 'AGE'.
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HSD17B6 , HGF , PPTC7
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1 gene correlated to 'GENDER'.
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UTP14C
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10 genes correlated to 'DISTANT.METASTASIS'.
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SEPSECS , C5ORF42 , KDELC1 , GOLGA7 , SEPT2 , ...
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45 genes correlated to 'LYMPH.NODE.METASTASIS'.
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LDHAL6B , MORC1 , RAB20 , HECA , RFX2 , ...
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9 genes correlated to 'COMPLETENESS.OF.RESECTION'.
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SEPSECS , C5ORF42 , KDELC1 , GOLGA7 , BIK , ...
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38 genes correlated to 'NEOPLASM.DISEASESTAGE'.
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SEPSECS , MORC1 , LDHAL6B , RAB20 , HECA , ...
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No genes correlated to 'Time to Death'
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=3 | older | N=0 | younger | N=3 |
GENDER | t test | N=1 | male | N=1 | female | N=0 |
DISTANT METASTASIS | ANOVA test | N=10 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=45 | ||||
COMPLETENESS OF RESECTION | ANOVA test | N=9 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=38 |
Time to Death | Duration (Months) | 0.1-83.6 (median=14.3) |
censored | N = 29 | |
death | N = 20 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 59.38 (14) |
Significant markers | N = 3 | |
pos. correlated | 0 | |
neg. correlated | 3 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSD17B6 | -0.614 | 1.009e-06 | 0.0171 |
HGF | -0.6121 | 1.117e-06 | 0.0189 |
PPTC7 | -0.6 | 2.047e-06 | 0.0346 |
GENDER | Labels | N |
FEMALE | 20 | |
MALE | 34 | |
Significant markers | N = 1 | |
Higher in MALE | 1 | |
Higher in FEMALE | 0 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
UTP14C | 7.81 | 7.07e-08 | 0.00119 | 0.9441 |
DISTANT.METASTASIS | Labels | N |
M0 | 37 | |
M1 | 1 | |
MX | 16 | |
Significant markers | N = 10 |
ANOVA_P | Q | |
---|---|---|
SEPSECS | 4.423e-39 | 7.47e-35 |
C5ORF42 | 6.831e-13 | 1.15e-08 |
KDELC1 | 6.794e-11 | 1.15e-06 |
GOLGA7 | 2.405e-10 | 4.06e-06 |
SEPT2 | 9.037e-08 | 0.00153 |
C4ORF12 | 1.042e-07 | 0.00176 |
MGMT | 2.375e-07 | 0.00401 |
FBXL22 | 1.246e-06 | 0.021 |
FOLH1 | 1.944e-06 | 0.0328 |
VPS37B | 2.569e-06 | 0.0434 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 37 | |
N1 | 1 | |
NX | 15 | |
Significant markers | N = 45 |
ANOVA_P | Q | |
---|---|---|
LDHAL6B | 7.946e-31 | 1.34e-26 |
MORC1 | 1.776e-30 | 3e-26 |
RAB20 | 2.368e-26 | 4e-22 |
HECA | 5.923e-25 | 1e-20 |
RFX2 | 9.803e-24 | 1.66e-19 |
SYTL3 | 6.206e-23 | 1.05e-18 |
MOBKL1A | 1.186e-20 | 2e-16 |
FGF22 | 1.352e-19 | 2.28e-15 |
MAEL | 4.573e-18 | 7.72e-14 |
ASB14 | 2.287e-14 | 3.86e-10 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 40 | |
R1 | 4 | |
R2 | 1 | |
RX | 6 | |
Significant markers | N = 9 |
ANOVA_P | Q | |
---|---|---|
SEPSECS | 7.139e-36 | 1.21e-31 |
C5ORF42 | 1.186e-12 | 2e-08 |
KDELC1 | 1.33e-09 | 2.25e-05 |
GOLGA7 | 5.505e-09 | 9.3e-05 |
BIK | 9.031e-09 | 0.000153 |
IREB2 | 9.32e-08 | 0.00157 |
C4ORF12 | 2.445e-07 | 0.00413 |
MGMT | 5.602e-07 | 0.00946 |
SEPT2 | 2.129e-06 | 0.036 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 21 | |
STAGE II | 11 | |
STAGE IIIA | 10 | |
STAGE IIIB | 3 | |
STAGE IIIC | 1 | |
STAGE IVB | 1 | |
Significant markers | N = 38 |
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Expresson data file = LIHC-TP.meth.for_correlation.filtered_data.txt
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Clinical data file = LIHC-TP.clin.merged.picked.txt
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Number of patients = 54
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Number of genes = 16900
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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 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.