This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 20103 genes and 6 clinical features across 233 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.
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955 genes correlated to 'Time to Death'.
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ATF3 , UMODL1 , HS3ST4 , ISM1 , HPD , ...
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307 genes correlated to 'AGE'.
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TRIM58 , SHISA2 , SLC22A16 , LOC150786 , HOXD8 , ...
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15 genes correlated to 'GENDER'.
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ALG11__1 , UTP14C , POLDIP3 , RNU12 , FAM35A , ...
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1151 genes correlated to 'HISTOLOGICAL.TYPE'.
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CCDC88C , SLC2A4RG , MAPKAP1 , BVES , REST , ...
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16 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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JAK2 , EFCAB7__2 , ITGB3BP__1 , ZMYM4 , HSPA13 , ...
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No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'
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=955 | shorter survival | N=125 | longer survival | N=830 |
AGE | Spearman correlation test | N=307 | older | N=181 | younger | N=126 |
GENDER | t test | N=15 | male | N=6 | female | N=9 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=1151 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=16 | yes | N=10 | no | N=6 |
Time to Death | Duration (Months) | 0-211.2 (median=14.4) |
censored | N = 184 | |
death | N = 49 | |
Significant markers | N = 955 | |
associated with shorter survival | 125 | |
associated with longer survival | 830 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
ATF3 | 0 | 4.896e-14 | 9.8e-10 | 0.238 |
UMODL1 | 0 | 2.389e-13 | 4.8e-09 | 0.266 |
HS3ST4 | 191 | 1.686e-12 | 3.4e-08 | 0.782 |
ISM1 | 6401 | 1.785e-12 | 3.6e-08 | 0.719 |
HPD | 0 | 1.973e-12 | 4e-08 | 0.252 |
ZNF492 | 100 | 3.315e-12 | 6.7e-08 | 0.71 |
NID2 | 0 | 4.38e-12 | 8.8e-08 | 0.22 |
TLK1 | 0.01 | 5.349e-12 | 1.1e-07 | 0.252 |
CARD6 | 0.01 | 6.224e-12 | 1.3e-07 | 0.296 |
CD274 | 0.01 | 6.727e-12 | 1.4e-07 | 0.248 |
AGE | Mean (SD) | 42.81 (13) |
Significant markers | N = 307 | |
pos. correlated | 181 | |
neg. correlated | 126 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
TRIM58 | 0.5442 | 2.3e-19 | 4.62e-15 |
SHISA2 | 0.5327 | 1.757e-18 | 3.53e-14 |
SLC22A16 | 0.5209 | 1.309e-17 | 2.63e-13 |
LOC150786 | 0.5163 | 2.814e-17 | 5.66e-13 |
HOXD8 | 0.5085 | 1.003e-16 | 2.02e-12 |
GALNT14 | 0.4993 | 4.301e-16 | 8.64e-12 |
ADAMTSL3 | 0.4915 | 1.418e-15 | 2.85e-11 |
HOXD11 | 0.4914 | 1.445e-15 | 2.9e-11 |
EPHA6 | 0.4906 | 1.635e-15 | 3.29e-11 |
PAX9 | 0.4803 | 7.484e-15 | 1.5e-10 |
GENDER | Labels | N |
FEMALE | 106 | |
MALE | 127 | |
Significant markers | N = 15 | |
Higher in MALE | 6 | |
Higher in FEMALE | 9 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
ALG11__1 | 24.37 | 3.497e-51 | 7.03e-47 | 0.9799 |
UTP14C | 24.37 | 3.497e-51 | 7.03e-47 | 0.9799 |
POLDIP3 | -16.76 | 9.925e-38 | 2e-33 | 0.9401 |
RNU12 | -16.76 | 9.925e-38 | 2e-33 | 0.9401 |
FAM35A | -11.6 | 1.109e-24 | 2.23e-20 | 0.8358 |
GLUD1 | -11.6 | 1.109e-24 | 2.23e-20 | 0.8358 |
WBP11P1 | 9.97 | 1.897e-19 | 3.81e-15 | 0.8468 |
TFDP1 | -8.76 | 5.124e-16 | 1.03e-11 | 0.882 |
KIF4B | -7.81 | 4.566e-13 | 9.18e-09 | 0.7546 |
ZNF839 | -6.94 | 4.68e-11 | 9.4e-07 | 0.7827 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 88.17 (11) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
ASTROCYTOMA | 68 | |
OLIGOASTROCYTOMA | 68 | |
OLIGODENDROGLIOMA | 96 | |
Significant markers | N = 1151 |
ANOVA_P | Q | |
---|---|---|
CCDC88C | 7.965e-19 | 1.6e-14 |
SLC2A4RG | 2.376e-17 | 4.78e-13 |
MAPKAP1 | 2.896e-17 | 5.82e-13 |
BVES | 4.095e-17 | 8.23e-13 |
REST | 5.869e-17 | 1.18e-12 |
CBX2 | 1.226e-16 | 2.46e-12 |
EMP1 | 4.039e-16 | 8.12e-12 |
TMEM51 | 4.235e-16 | 8.51e-12 |
NCKAP5 | 1.14e-15 | 2.29e-11 |
GLIS3 | 1.656e-15 | 3.33e-11 |
16 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 85 | |
YES | 148 | |
Significant markers | N = 16 | |
Higher in YES | 10 | |
Higher in NO | 6 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
JAK2 | -5.84 | 2.603e-08 | 0.000523 | 0.706 |
EFCAB7__2 | 5.69 | 3.908e-08 | 0.000786 | 0.7181 |
ITGB3BP__1 | 5.69 | 3.908e-08 | 0.000786 | 0.7181 |
ZMYM4 | 5.61 | 5.972e-08 | 0.0012 | 0.7077 |
HSPA13 | 5.52 | 9.131e-08 | 0.00184 | 0.6548 |
ZNF567 | -5.3 | 4.462e-07 | 0.00897 | 0.6998 |
FLRT3 | 5.13 | 6.065e-07 | 0.0122 | 0.659 |
MACROD2 | 5.13 | 6.065e-07 | 0.0122 | 0.659 |
ELMOD3 | -5.07 | 1.205e-06 | 0.0242 | 0.6869 |
RETSAT__1 | -5.07 | 1.205e-06 | 0.0242 | 0.6869 |
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Expresson data file = LGG-TP.meth.by_min_expr_corr.data.txt
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Clinical data file = LGG-TP.clin.merged.picked.txt
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Number of patients = 233
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Number of genes = 20103
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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.
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.