This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 20238 genes and 5 clinical features across 313 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.
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1 gene correlated to 'Time to Death'.
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ACOT8
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138 genes correlated to 'AGE'.
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ZNF574 , SLFN14 , MIR1307 , MIR1249 , FBXO43 , ...
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1556 genes correlated to 'HISTOLOGICAL.TYPE'.
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UBE2T , SSTR1 , NPBWR2 , KCNA1 , CRYAB , ...
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48 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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AP3M1 , PCYT1A , CCDC159 , PPP1R11 , RNMTL1 , ...
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6 genes correlated to 'NEOADJUVANT.THERAPY'.
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SASS6 , LOC400927 , YIF1A , SOCS3 , GADD45G , ...
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=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=138 | older | N=79 | younger | N=59 |
HISTOLOGICAL TYPE | ANOVA test | N=1556 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=48 | yes | N=48 | no | N=0 |
NEOADJUVANT THERAPY | t test | N=6 | yes | N=6 | no | N=0 |
Time to Death | Duration (Months) | 0-187.1 (median=12.8) |
censored | N = 279 | |
death | N = 30 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
ACOT8 | 310000000000001 | 1.727e-06 | 0.035 | 0.69 |
AGE | Mean (SD) | 63.49 (11) |
Significant markers | N = 138 | |
pos. correlated | 79 | |
neg. correlated | 59 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
ZNF574 | -0.3618 | 4.415e-11 | 8.93e-07 |
SLFN14 | 0.3577 | 7.515e-11 | 1.52e-06 |
MIR1307 | -0.355 | 1.066e-10 | 2.16e-06 |
MIR1249 | 0.3432 | 4.738e-10 | 9.59e-06 |
FBXO43 | -0.3397 | 7.27e-10 | 1.47e-05 |
CLDN16 | -0.3344 | 1.373e-09 | 2.78e-05 |
FBXL5 | -0.3316 | 1.918e-09 | 3.88e-05 |
POLR2J | -0.3289 | 2.624e-09 | 5.31e-05 |
TREM1 | 0.3283 | 2.832e-09 | 5.73e-05 |
OTOR | 0.3277 | 3.023e-09 | 6.11e-05 |
HISTOLOGICAL.TYPE | Labels | N |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | 229 | |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) | 6 | |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) | 1 | |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) | 3 | |
MIXED SEROUS AND ENDOMETRIOID | 16 | |
SEROUS ENDOMETRIAL ADENOCARCINOMA | 58 | |
Significant markers | N = 1556 |
ANOVA_P | Q | |
---|---|---|
UBE2T | 4.831e-42 | 9.78e-38 |
SSTR1 | 7.116e-34 | 1.44e-29 |
NPBWR2 | 3.718e-33 | 7.52e-29 |
KCNA1 | 1.888e-31 | 3.82e-27 |
CRYAB | 2.548e-31 | 5.15e-27 |
HSPB2 | 3.63e-31 | 7.34e-27 |
SFMBT1 | 8.92e-28 | 1.8e-23 |
GRIA4 | 1.274e-26 | 2.58e-22 |
IDO1 | 2.62e-26 | 5.3e-22 |
LOC400578 | 2.664e-25 | 5.39e-21 |
48 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 79 | |
YES | 234 | |
Significant markers | N = 48 | |
Higher in YES | 48 | |
Higher in NO | 0 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
AP3M1 | 6.09 | 8.254e-09 | 0.000167 | 0.7076 |
PCYT1A | 5.93 | 1.842e-08 | 0.000373 | 0.6969 |
CCDC159 | 5.76 | 2.853e-08 | 0.000577 | 0.6667 |
PPP1R11 | 5.5 | 1.095e-07 | 0.00222 | 0.7095 |
RNMTL1 | 5.49 | 1.302e-07 | 0.00264 | 0.6808 |
YKT6 | 5.48 | 1.497e-07 | 0.00303 | 0.6868 |
SSRP1 | 5.45 | 1.61e-07 | 0.00326 | 0.6714 |
RFC2 | 5.53 | 1.698e-07 | 0.00344 | 0.6997 |
PLEKHA3 | 5.33 | 2.32e-07 | 0.00469 | 0.6712 |
NEK11 | 5.28 | 3.415e-07 | 0.00691 | 0.6956 |
NEOADJUVANT.THERAPY | Labels | N |
NO | 70 | |
YES | 243 | |
Significant markers | N = 6 | |
Higher in YES | 6 | |
Higher in NO | 0 |
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Expresson data file = UCEC.meth.for_correlation.filtered_data.txt
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Clinical data file = UCEC.clin.merged.picked.txt
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Number of patients = 313
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Number of genes = 20238
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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.