This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 14047 genes and 6 clinical features across 574 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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181 genes correlated to 'AGE'.
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PCDHGA1__4 , PCDHGA10__3 , PCDHGA2__4 , PCDHGA3__4 , PCDHGA4__4 , ...
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59 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.
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ZMYM5 , ZCCHC11 , CCNF , FOXN2 , SUV39H2 , ...
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1324 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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NID2 , ALDH6A1 , LIN52 , SLC25A17 , HSD17B14 , ...
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1 gene correlated to 'COMPLETENESS.OF.RESECTION'.
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ADAD2
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No genes correlated to 'Time to Death', and 'KARNOFSKY.PERFORMANCE.SCORE'.
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=181 | older | N=71 | younger | N=110 |
PRIMARY SITE OF DISEASE | ANOVA test | N=59 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=1324 | yes | N=1253 | no | N=71 |
COMPLETENESS OF RESECTION | ANOVA test | N=1 |
Time to Death | Duration (Months) | 0.3-180.2 (median=28.5) |
censored | N = 263 | |
death | N = 295 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 59.79 (12) |
Significant markers | N = 181 | |
pos. correlated | 71 | |
neg. correlated | 110 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
PCDHGA1__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA10__3 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA2__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA3__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA4__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA5__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA6__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA7__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA8__3 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA9__3 | 0.3019 | 4.094e-13 | 5.75e-09 |
PRIMARY.SITE.OF.DISEASE | Labels | N |
OMENTUM | 2 | |
OVARY | 560 | |
PERITONEUM OVARY | 2 | |
Significant markers | N = 59 |
ANOVA_P | Q | |
---|---|---|
ZMYM5 | 2.129e-68 | 2.99e-64 |
ZCCHC11 | 1.089e-53 | 1.53e-49 |
CCNF | 9.443e-48 | 1.33e-43 |
FOXN2 | 2.05e-44 | 2.88e-40 |
SUV39H2 | 2.262e-28 | 3.18e-24 |
RANBP1 | 1.023e-26 | 1.44e-22 |
TRMT2A | 1.023e-26 | 1.44e-22 |
DBN1 | 5.833e-26 | 8.19e-22 |
IRS2 | 1.286e-22 | 1.8e-18 |
INHA | 3.36e-21 | 4.72e-17 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.64 (13) |
Score | N | |
40 | 2 | |
60 | 20 | |
80 | 49 | |
100 | 7 | |
Significant markers | N = 0 |
1324 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 3 | |
YES | 571 | |
Significant markers | N = 1324 | |
Higher in YES | 1253 | |
Higher in NO | 71 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
NID2 | 27.74 | 7.984e-108 | 1.12e-103 | 0.9685 |
ALDH6A1 | 20.86 | 2.975e-72 | 4.18e-68 | 0.972 |
LIN52 | 20.86 | 2.975e-72 | 4.18e-68 | 0.972 |
SLC25A17 | 20.86 | 7.418e-68 | 1.04e-63 | 0.8578 |
HSD17B14 | 22.32 | 7.572e-68 | 1.06e-63 | 0.8616 |
PLEKHA4__1 | 22.32 | 7.572e-68 | 1.06e-63 | 0.8616 |
LYPD5 | 19.74 | 8.446e-66 | 1.19e-61 | 0.7274 |
PTPRN | 19.75 | 8.254e-65 | 1.16e-60 | 0.8961 |
THY1 | 20.36 | 9.25e-65 | 1.3e-60 | 0.8809 |
ACY1 | -18.88 | 3.911e-62 | 5.49e-58 | 0.7017 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 14 | |
R1 | 26 | |
R2 | 2 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
ADAD2 | 2.294e-09 | 3.22e-05 |
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Expresson data file = OV-TP.meth.by_min_clin_corr.data.txt
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Clinical data file = OV-TP.merged_data.txt
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Number of patients = 574
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Number of genes = 14047
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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 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 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 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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.