This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 17428 genes and 7 clinical features across 42 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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3 genes correlated to 'AGE'.
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HOXD8 , KCNK17 , BARHL2
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2 genes correlated to 'GENDER'.
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UTP14C , ACSM1
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26 genes correlated to 'HISTOLOGICAL.TYPE'.
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ZNF362 , SLMO1 , SLC16A1 , C1ORF50 , BVES , ...
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No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and '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=0 | ||||
AGE | Spearman correlation test | N=3 | older | N=3 | younger | N=0 |
GENDER | t test | N=2 | male | N=1 | female | N=1 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=26 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 | ||||
NEOADJUVANT THERAPY | t test | N=0 |
Time to Death | Duration (Months) | 1.2-211.2 (median=36) |
censored | N = 23 | |
death | N = 19 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 43 (12) |
Significant markers | N = 3 | |
pos. correlated | 3 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HOXD8 | 0.7005 | 2.413e-07 | 0.00421 |
KCNK17 | 0.6679 | 1.347e-06 | 0.0235 |
BARHL2 | 0.6546 | 2.565e-06 | 0.0447 |
GENDER | Labels | N |
FEMALE | 24 | |
MALE | 18 | |
Significant markers | N = 2 | |
Higher in MALE | 1 | |
Higher in FEMALE | 1 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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UTP14C | 11.26 | 1.11e-13 | 1.93e-09 | 0.9884 |
ACSM1 | -7.23 | 1.34e-08 | 0.000233 | 0.9236 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 89.6 (11) |
Score | N | |
50 | 1 | |
70 | 1 | |
80 | 3 | |
90 | 12 | |
100 | 8 | |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
ASTROCYTOMA | 12 | |
OLIGOASTROCYTOMA | 14 | |
OLIGODENDROGLIOMA | 16 | |
Significant markers | N = 26 |
ANOVA_P | Q | |
---|---|---|
ZNF362 | 1.488e-08 | 0.000259 |
SLMO1 | 2.821e-08 | 0.000492 |
SLC16A1 | 6.191e-08 | 0.00108 |
C1ORF50 | 6.422e-08 | 0.00112 |
BVES | 7.141e-08 | 0.00124 |
NRD1 | 1.397e-07 | 0.00243 |
XRCC1 | 2.205e-07 | 0.00384 |
PAFAH1B3 | 2.363e-07 | 0.00412 |
ZNF227 | 3.138e-07 | 0.00547 |
ZNF790 | 4.008e-07 | 0.00698 |
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 30 | |
YES | 12 | |
Significant markers | N = 0 |
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Expresson data file = LGG.meth.for_correlation.filtered_data.txt
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Clinical data file = LGG.clin.merged.picked.txt
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Number of patients = 42
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Number of genes = 17428
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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.