(primary solid tumor cohort)
This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 17621 genes and 5 clinical features across 256 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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TMEM84 , SLC38A8
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202 genes correlated to 'AGE'.
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SLFN14 , PLA2G15 , ALS2CL , ACAA2 , SCARNA17 , ...
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2178 genes correlated to 'HISTOLOGICAL.TYPE'.
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CARD11 , NCRNA00203 , ABCB6 , KCNIP2 , SSTR1 , ...
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2 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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GRIK3 , FOXG1
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33 genes correlated to 'COMPLETENESS.OF.RESECTION'.
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DNASE1L2 , TNFRSF19 , HIST1H2BO , MYRIP , PRRG2 , ...
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=2 | shorter survival | N=0 | longer survival | N=2 |
AGE | Spearman correlation test | N=202 | older | N=67 | younger | N=135 |
HISTOLOGICAL TYPE | ANOVA test | N=2178 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=2 | yes | N=2 | no | N=0 |
COMPLETENESS OF RESECTION | ANOVA test | N=33 |
Time to Death | Duration (Months) | 0-187.1 (median=13.4) |
censored | N = 233 | |
death | N = 21 | |
Significant markers | N = 2 | |
associated with shorter survival | 0 | |
associated with longer survival | 2 |
AGE | Mean (SD) | 63.44 (11) |
Significant markers | N = 202 | |
pos. correlated | 67 | |
neg. correlated | 135 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
SLFN14 | 0.4397 | 1.577e-13 | 2.78e-09 |
PLA2G15 | -0.4152 | 4.325e-12 | 7.62e-08 |
ALS2CL | -0.3959 | 4.875e-11 | 8.59e-07 |
ACAA2 | -0.3856 | 1.686e-10 | 2.97e-06 |
SCARNA17 | -0.3856 | 1.686e-10 | 2.97e-06 |
C19ORF55 | -0.3813 | 2.782e-10 | 4.9e-06 |
SCGBL | 0.375 | 5.694e-10 | 1e-05 |
ADAMTS15 | -0.372 | 7.997e-10 | 1.41e-05 |
AHSP | 0.3694 | 1.068e-09 | 1.88e-05 |
CYP1A2 | 0.3694 | 1.07e-09 | 1.88e-05 |
HISTOLOGICAL.TYPE | Labels | N |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | 207 | |
MIXED SEROUS AND ENDOMETRIOID | 9 | |
SEROUS ENDOMETRIAL ADENOCARCINOMA | 40 | |
Significant markers | N = 2178 |
ANOVA_P | Q | |
---|---|---|
CARD11 | 9.062e-40 | 1.6e-35 |
NCRNA00203 | 1.667e-36 | 2.94e-32 |
ABCB6 | 4.747e-36 | 8.36e-32 |
KCNIP2 | 2.182e-34 | 3.84e-30 |
SSTR1 | 2.235e-34 | 3.94e-30 |
CRYAB | 6.25e-34 | 1.1e-29 |
HSPB2 | 1.204e-33 | 2.12e-29 |
SH3BP2 | 2.58e-32 | 4.55e-28 |
LASP1 | 4.22e-32 | 7.43e-28 |
ASAP2 | 5.793e-32 | 1.02e-27 |
2 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 70 | |
YES | 186 | |
Significant markers | N = 2 | |
Higher in YES | 2 | |
Higher in NO | 0 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
GRIK3 | 5.07 | 7.589e-07 | 0.0134 | 0.6538 |
FOXG1 | 5.07 | 9.327e-07 | 0.0164 | 0.6659 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 173 | |
R1 | 15 | |
R2 | 8 | |
RX | 20 | |
Significant markers | N = 33 |
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Expresson data file = UCEC-TP.meth.for_correlation.filtered_data.txt
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Clinical data file = UCEC-TP.clin.merged.picked.txt
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Number of patients = 256
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Number of genes = 17621
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Number of clinical features = 5
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 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 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.