This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 12759 genes and 6 clinical features across 280 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.
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2 genes correlated to 'Time to Death'.
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RNF39 , TMEM106A
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126 genes correlated to 'AGE'.
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PRKCB , CYYR1 , KLF14 , ZNF540 , SRD5A2 , ...
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7 genes correlated to 'GENDER'.
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NUS1 , TLE1 , ZNF770 , BAG1 , GLUD1 , ...
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2 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.
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SORBS3 , STXBP6
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1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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ADCY8
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No genes correlated to 'NEOADJUVANT.THERAPY'
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=2 | shorter survival | N=0 | longer survival | N=2 |
AGE | Spearman correlation test | N=126 | older | N=110 | younger | N=16 |
GENDER | t test | N=7 | male | N=1 | female | N=6 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=2 | higher score | N=2 | lower score | N=0 |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=1 | yes | N=0 | no | N=1 |
NEOADJUVANT THERAPY | t test | N=0 |
Time to Death | Duration (Months) | 0.1-127.6 (median=10) |
censored | N = 72 | |
death | N = 208 | |
Significant markers | N = 2 | |
associated with shorter survival | 0 | |
associated with longer survival | 2 |
AGE | Mean (SD) | 57.55 (15) |
Significant markers | N = 126 | |
pos. correlated | 110 | |
neg. correlated | 16 |
SpearmanCorr | corrP | Q | |
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PRKCB | 0.4226 | 1.485e-13 | 1.89e-09 |
CYYR1 | 0.4156 | 4.066e-13 | 5.19e-09 |
KLF14 | 0.3958 | 6.176e-12 | 7.88e-08 |
ZNF540 | 0.3956 | 6.332e-12 | 8.08e-08 |
SRD5A2 | 0.3904 | 1.252e-11 | 1.6e-07 |
HOXD8 | 0.3887 | 1.553e-11 | 1.98e-07 |
SYT10 | 0.3859 | 2.253e-11 | 2.87e-07 |
HCN1 | 0.3755 | 8.292e-11 | 1.06e-06 |
ZNF560 | 0.3749 | 8.98e-11 | 1.15e-06 |
MACROD2 | 0.3711 | 1.425e-10 | 1.82e-06 |
GENDER | Labels | N |
FEMALE | 114 | |
MALE | 166 | |
Significant markers | N = 7 | |
Higher in MALE | 1 | |
Higher in FEMALE | 6 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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NUS1 | -19.8 | 7.234e-39 | 9.23e-35 | 0.9729 |
TLE1 | -11.83 | 4.131e-26 | 5.27e-22 | 0.8498 |
ZNF770 | -9.36 | 5.873e-18 | 7.49e-14 | 0.7976 |
BAG1 | 7.35 | 3.607e-12 | 4.6e-08 | 0.7828 |
GLUD1 | -6.02 | 7.549e-09 | 9.63e-05 | 0.7209 |
RAB9P1 | -5.49 | 9.499e-08 | 0.00121 | 0.7035 |
SERBP1 | -5.51 | 1.051e-07 | 0.00134 | 0.6823 |
2 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.35 (15) |
Significant markers | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 206 | |
YES | 74 | |
Significant markers | N = 1 | |
Higher in YES | 0 | |
Higher in NO | 1 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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ADCY8 | -4.99 | 1.212e-06 | 0.0155 | 0.5544 |
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Expresson data file = GBM.meth.for_correlation.filtered_data.txt
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Clinical data file = GBM.clin.merged.picked.txt
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Number of patients = 280
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Number of genes = 12759
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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 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.