This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 20177 genes and 9 clinical features across 71 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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88 genes correlated to 'PATHOLOGY.M.STAGE'.
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DUOX2 , DUOXA2 , RASSF10 , CHERP__1 , PFKM__1 , ...
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835 genes correlated to 'HISTOLOGICAL.TYPE'.
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ANO6 , PLEKHA9 , HORMAD1 , CIDEA , DGCR5 , ...
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2 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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CTF1 , TFAP2E
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No genes correlated to 'Time to Death', 'AGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'NUMBERPACKYEARSSMOKED', and 'NUMBER.OF.LYMPH.NODES'.
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 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | t test | N=0 | ||||
PATHOLOGY M STAGE | ANOVA test | N=88 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=835 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=2 | yes | N=2 | no | N=0 |
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 |
Time to Death | Duration (Months) | 0.1-177 (median=6.8) |
censored | N = 57 | |
death | N = 12 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 49.17 (13) |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 1.42 (0.68) |
N | ||
1 | 44 | |
2 | 20 | |
3 | 1 | |
4 | 2 | |
Significant markers | N = 0 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 44 | |
class1 | 22 | |
Significant markers | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 44 | |
M1 | 2 | |
MX | 20 | |
Significant markers | N = 88 |
ANOVA_P | Q | |
---|---|---|
DUOX2 | 4.151e-17 | 8.38e-13 |
DUOXA2 | 4.151e-17 | 8.38e-13 |
RASSF10 | 1.049e-12 | 2.12e-08 |
CHERP__1 | 1.085e-12 | 2.19e-08 |
PFKM__1 | 1.578e-12 | 3.18e-08 |
CHRNA7 | 5.258e-12 | 1.06e-07 |
DPF3 | 1.717e-11 | 3.46e-07 |
ATP6V1G1 | 2.939e-11 | 5.93e-07 |
SOCS2 | 3.274e-11 | 6.6e-07 |
MTUS2 | 5.316e-11 | 1.07e-06 |
HISTOLOGICAL.TYPE | Labels | N |
CERVICAL SQUAMOUS CELL CARCINOMA | 61 | |
ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE | 1 | |
ENDOCERVICAL TYPE OF ADENOCARCINOMA | 8 | |
ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX | 1 | |
Significant markers | N = 835 |
ANOVA_P | Q | |
---|---|---|
ANO6 | 4.107e-58 | 8.29e-54 |
PLEKHA9 | 4.107e-58 | 8.29e-54 |
HORMAD1 | 1.521e-55 | 3.07e-51 |
CIDEA | 6.486e-48 | 1.31e-43 |
DGCR5 | 1.298e-46 | 2.62e-42 |
MRGPRX3 | 4.542e-46 | 9.16e-42 |
RFPL4B | 7.286e-45 | 1.47e-40 |
OSCAR | 1.88e-44 | 3.79e-40 |
FAM71D | 8.415e-40 | 1.7e-35 |
PTGIR | 3.499e-37 | 7.06e-33 |
2 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 17 | |
YES | 54 | |
Significant markers | N = 2 | |
Higher in YES | 2 | |
Higher in NO | 0 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
CTF1 | 5.35 | 1.234e-06 | 0.0249 | 0.7712 |
TFAP2E | 5.2 | 2.139e-06 | 0.0432 | 0.6993 |
NUMBERPACKYEARSSMOKED | Mean (SD) | 19.19 (13) |
Significant markers | N = 0 |
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Expresson data file = CESC-TP.meth.by_min_expr_corr.data.txt
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Clinical data file = CESC-TP.clin.merged.picked.txt
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Number of patients = 71
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Number of genes = 20177
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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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.