This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 20188 genes and 9 clinical features across 134 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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11 genes correlated to 'GENDER'.
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KIF4B , B3GNT1 , MTMR12 , ALG11__2 , UTP14C , ...
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3 genes correlated to 'DISTANT.METASTASIS'.
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TICAM1 , NBPF3 , SLC2A12
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1 gene correlated to 'LYMPH.NODE.METASTASIS'.
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FCHSD2
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No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', 'NUMBER.OF.LYMPH.NODES', and 'NEOPLASM.DISEASESTAGE'.
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=0 | ||||
GENDER | t test | N=11 | male | N=5 | female | N=6 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=3 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=1 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=0 |
Time to Death | Duration (Months) | 0.1-131.2 (median=6.9) |
censored | N = 94 | |
death | N = 33 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 67.63 (11) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 34 | |
MALE | 100 | |
Significant markers | N = 11 | |
Higher in MALE | 5 | |
Higher in FEMALE | 6 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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KIF4B | -10.62 | 1.779e-15 | 3.59e-11 | 0.9282 |
B3GNT1 | 7.52 | 2.216e-11 | 4.47e-07 | 0.85 |
MTMR12 | -7.98 | 3.231e-10 | 6.52e-06 | 0.8953 |
ALG11__2 | 7.61 | 7.952e-09 | 0.000161 | 0.9438 |
UTP14C | 7.61 | 7.952e-09 | 0.000161 | 0.9438 |
DKK4 | -5.72 | 6.797e-08 | 0.00137 | 0.7044 |
KRT79 | -5.87 | 8.205e-08 | 0.00166 | 0.7703 |
TCP10L | -5.39 | 3.334e-07 | 0.00673 | 0.7265 |
CATSPER3 | -5.44 | 4.623e-07 | 0.00933 | 0.7426 |
HIST1H2BH | 4.99 | 1.845e-06 | 0.0372 | 0.6162 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 78.21 (16) |
Significant markers | N = 0 |
NUMBERPACKYEARSSMOKED | Mean (SD) | 37.54 (23) |
Significant markers | N = 0 |
DISTANT.METASTASIS | Labels | N |
M0 | 77 | |
M1 | 5 | |
MX | 51 | |
Significant markers | N = 3 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 76 | |
N1 | 14 | |
N2 | 27 | |
N3 | 5 | |
NX | 9 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
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FCHSD2 | 1.915e-06 | 0.0387 |
NUMBER.OF.LYMPH.NODES | Mean (SD) | 1.9 (3.8) |
Significant markers | N = 0 |
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Expresson data file = BLCA-TP.meth.by_min_expr_corr.data.txt
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Clinical data file = BLCA-TP.clin.merged.picked.txt
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Number of patients = 134
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Number of genes = 20188
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Number of clinical features = 9
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.