(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 17318 genes and 4 clinical features across 527 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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1 gene correlated to 'Time to Death'.
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CDC73
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131 genes correlated to 'AGE'.
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KIF15 , MEX3C , LGALS8 , EGR2 , C10ORF35 , ...
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191 genes correlated to 'GENDER'.
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ALDOC , ZNF486 , CRIP1 , DNAJC15 , NMNAT3 , ...
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224 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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CCDC86 , PRR7 , TUBA4B , CCDC85B , HS1BP3 , ...
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=1 | shorter survival | N=0 | longer survival | N=1 |
AGE | Spearman correlation test | N=131 | older | N=119 | younger | N=12 |
GENDER | t test | N=191 | male | N=42 | female | N=149 |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=224 | yes | N=165 | no | N=59 |
Time to Death | Duration (Months) | 0-223.4 (median=18.1) |
censored | N = 441 | |
death | N = 58 | |
Significant markers | N = 1 | |
associated with shorter survival | 0 | |
associated with longer survival | 1 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
CDC73 | 0 | 1.411e-06 | 0.024 | 0.355 |
AGE | Mean (SD) | 57.6 (13) |
Significant markers | N = 131 | |
pos. correlated | 119 | |
neg. correlated | 12 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
KIF15 | 0.3161 | 1.139e-13 | 1.97e-09 |
MEX3C | 0.2858 | 2.412e-11 | 4.18e-07 |
LGALS8 | -0.2843 | 3.097e-11 | 5.36e-07 |
EGR2 | 0.2835 | 3.52e-11 | 6.09e-07 |
C10ORF35 | 0.2816 | 4.795e-11 | 8.3e-07 |
RPL13A | 0.2785 | 7.92e-11 | 1.37e-06 |
FASN | 0.2741 | 1.625e-10 | 2.81e-06 |
RPL27A | 0.2665 | 5.321e-10 | 9.21e-06 |
RPL7A | 0.2637 | 8.18e-10 | 1.42e-05 |
CACNA2D1 | 0.2624 | 9.868e-10 | 1.71e-05 |
GENDER | Labels | N |
FEMALE | 521 | |
MALE | 6 | |
Significant markers | N = 191 | |
Higher in MALE | 42 | |
Higher in FEMALE | 149 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
ALDOC | -25.53 | 1.209e-92 | 2.09e-88 | 0.8669 |
ZNF486 | -18.31 | 4.367e-58 | 7.56e-54 | 0.818 |
CRIP1 | -16.85 | 3.091e-51 | 5.35e-47 | 0.872 |
DNAJC15 | -13.79 | 8.918e-36 | 1.54e-31 | 0.7335 |
NMNAT3 | -13.17 | 6.929e-34 | 1.2e-29 | 0.6916 |
LOC400043 | -13.08 | 5.536e-31 | 9.58e-27 | 0.6022 |
RND2 | -13.18 | 1.514e-28 | 2.62e-24 | 0.7927 |
EML1 | -11.41 | 7.128e-27 | 1.23e-22 | 0.6081 |
SPC25 | -12.19 | 2.831e-26 | 4.9e-22 | 0.7521 |
HSPC157 | -12.91 | 7.833e-25 | 1.36e-20 | 0.6312 |
224 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 130 | |
YES | 397 | |
Significant markers | N = 224 | |
Higher in YES | 165 | |
Higher in NO | 59 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
CCDC86 | 7.77 | 1.517e-13 | 2.63e-09 | 0.7058 |
PRR7 | 7.25 | 3.485e-12 | 6.04e-08 | 0.6855 |
TUBA4B | 7.14 | 4.371e-12 | 7.57e-08 | 0.6624 |
CCDC85B | 7.19 | 5.453e-12 | 9.44e-08 | 0.6828 |
HS1BP3 | 7.04 | 1.148e-11 | 1.99e-07 | 0.682 |
TFAP4 | 6.96 | 1.633e-11 | 2.83e-07 | 0.6602 |
ERP29 | 6.84 | 2.699e-11 | 4.67e-07 | 0.7015 |
MAP1LC3B2 | -6.99 | 2.976e-11 | 5.15e-07 | 0.6877 |
DDX54 | 6.91 | 3.021e-11 | 5.23e-07 | 0.6728 |
NDUFB4 | 6.91 | 3.406e-11 | 5.9e-07 | 0.6827 |
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Expresson data file = BRCA-TP.meth.for_correlation.filtered_data.txt
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Clinical data file = BRCA-TP.clin.merged.picked.txt
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Number of patients = 527
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Number of genes = 17318
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Number of clinical features = 4
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 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.