This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 17311 genes and 8 clinical features across 47 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 'GENDER'.
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NARFL
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33 genes correlated to 'PATHOLOGY.T'.
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INSM1 , DLEU2 , GNG11 , GNASAS , CDC42BPG , ...
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25 genes correlated to 'PATHOLOGICSPREAD(M)'.
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RFX7 , DGKI , LOC100132247 , ELMOD2 , MYADML , ...
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29 genes correlated to 'TUMOR.STAGE'.
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INSM1 , WDR16 , GNG11 , LYPD3 , DLEU2 , ...
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No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', and 'PATHOLOGY.N'.
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=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=1 | male | N=0 | female | N=1 |
KARNOFSKY PERFORMANCE SCORE | t test | N=0 | ||||
PATHOLOGY T | Spearman correlation test | N=33 | higher pT | N=22 | lower pT | N=11 |
PATHOLOGY N | Spearman correlation test | N=0 | ||||
PATHOLOGICSPREAD(M) | ANOVA test | N=25 | ||||
TUMOR STAGE | Spearman correlation test | N=29 | higher stage | N=23 | lower stage | N=6 |
Time to Death | Duration (Months) | 1-123.6 (median=21.6) |
censored | N = 36 | |
death | N = 11 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 61.15 (13) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 16 | |
MALE | 31 | |
Significant markers | N = 1 | |
Higher in MALE | 0 | |
Higher in FEMALE | 1 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
NARFL | -6.08 | 3.996e-07 | 0.00692 | 0.8891 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Labels | N |
class100 | 4 | |
class90 | 3 | |
Significant markers | N = 0 |
PATHOLOGY.T | Mean (SD) | 1.91 (1) |
N | ||
T1 | 25 | |
T2 | 1 | |
T3 | 21 | |
Significant markers | N = 33 | |
pos. correlated | 22 | |
neg. correlated | 11 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
INSM1 | 0.7366 | 3.58e-09 | 6.2e-05 |
DLEU2 | -0.7329 | 4.697e-09 | 8.13e-05 |
GNG11 | -0.7008 | 4.128e-08 | 0.000715 |
GNASAS | -0.6876 | 9.348e-08 | 0.00162 |
CDC42BPG | 0.6794 | 1.519e-07 | 0.00263 |
LYPD3 | 0.6761 | 1.84e-07 | 0.00318 |
KIR2DS4 | -0.6725 | 2.256e-07 | 0.0039 |
VPS33B | 0.6705 | 2.524e-07 | 0.00437 |
CCDC64B | 0.6635 | 3.726e-07 | 0.00645 |
NSD1 | 0.6621 | 4.005e-07 | 0.00693 |
PATHOLOGY.N | Mean (SD) | 0.54 (0.72) |
N | ||
N0 | 14 | |
N1 | 7 | |
N2 | 3 | |
Significant markers | N = 0 |
PATHOLOGICSPREAD(M) | Labels | N |
M0 | 33 | |
M1 | 4 | |
MX | 9 | |
Significant markers | N = 25 |
ANOVA_P | Q | |
---|---|---|
RFX7 | 2.652e-09 | 4.59e-05 |
DGKI | 1.111e-08 | 0.000192 |
LOC100132247 | 2.308e-08 | 4e-04 |
ELMOD2 | 1.321e-07 | 0.00229 |
MYADML | 1.342e-07 | 0.00232 |
CMTM5 | 1.661e-07 | 0.00288 |
FAM183B | 2.98e-07 | 0.00516 |
FBXL19 | 3.827e-07 | 0.00662 |
GDPD4 | 4.301e-07 | 0.00744 |
ZNF329 | 4.403e-07 | 0.00762 |
TUMOR.STAGE | Mean (SD) | 2.07 (1.2) |
N | ||
Stage 1 | 24 | |
Stage 2 | 1 | |
Stage 3 | 15 | |
Stage 4 | 6 | |
Significant markers | N = 29 | |
pos. correlated | 23 | |
neg. correlated | 6 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
INSM1 | 0.7593 | 9.561e-10 | 1.66e-05 |
WDR16 | 0.6959 | 7.929e-08 | 0.00137 |
GNG11 | -0.694 | 8.897e-08 | 0.00154 |
LYPD3 | 0.6937 | 9.044e-08 | 0.00157 |
DLEU2 | -0.6711 | 3.32e-07 | 0.00575 |
ZNF177 | 0.6707 | 3.395e-07 | 0.00588 |
NSD1 | 0.6691 | 3.7e-07 | 0.0064 |
MYT1L | -0.6663 | 4.322e-07 | 0.00748 |
CDC42BPG | 0.6649 | 4.668e-07 | 0.00808 |
GNASAS | -0.6631 | 5.133e-07 | 0.00888 |
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Expresson data file = KIRP.meth.for_correlation.filtered_data.txt
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Clinical data file = KIRP.clin.merged.picked.txt
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Number of patients = 47
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Number of genes = 17311
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Number of clinical features = 8
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.