Correlation between gene methylation status and clinical features

Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1D21VZR

Overview

Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 20206 genes and 5 clinical features across 362 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

196 genes correlated to 'AGE'.

ANGPT4 , TUFT1 , GPR158 , LOC100271836 , HDAC11 , ...

2564 genes correlated to 'HISTOLOGICAL.TYPE'.

APBB1IP , SSTR1 , CRYAB , HSPB2 , CARD11 , ...

6 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

MAP2K2 , GPR89A , WBP4 , FZD8 , C22ORF13 , ...

18 genes correlated to 'COMPLETENESS.OF.RESECTION'.

MYRIP , TRIM37 , TNFRSF19 , BICC1 , TMEM145 , ...

No genes correlated to 'Time to Death'

Results

Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature	Statistical test	Significant genes	Associated with		Associated with
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=196	older	N=61	younger	N=135
HISTOLOGICAL TYPE	ANOVA test	N=2564
RADIATIONS RADIATION REGIMENINDICATION	t test	N=6	yes	N=6	no	N=0
COMPLETENESS OF RESECTION	ANOVA test	N=18

Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	0-191.8 (median=16.8)
	censored	N = 318
	death	N = 40

	Significant markers	N = 0

Clinical variable #2: 'AGE'

196 genes related to 'AGE'.

Table S2. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	63.92 (11)
	Significant markers	N = 196
	pos. correlated	61
	neg. correlated	135

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
ANGPT4	0.3543	4.394e-12	8.88e-08
TUFT1	-0.3429	2.263e-11	4.57e-07
GPR158	-0.3417	2.708e-11	5.47e-07
LOC100271836	-0.3413	2.863e-11	5.78e-07
HDAC11	-0.3351	6.787e-11	1.37e-06
TBC1D23	-0.3329	9.103e-11	1.84e-06
GDNF	-0.3299	1.381e-10	2.79e-06
SLFN14	0.3234	3.31e-10	6.69e-06
GPR142	0.3148	1.005e-09	2.03e-05
CACNB1	-0.314	1.123e-09	2.27e-05

Figure S1. Get High-res Image As an example, this figure shows the association of ANGPT4 to 'AGE'. P value = 4.39e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

2564 genes related to 'HISTOLOGICAL.TYPE'.

Table S4. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	267
	MIXED SEROUS AND ENDOMETRIOID	17
	SEROUS ENDOMETRIAL ADENOCARCINOMA	77

	Significant markers	N = 2564

List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
APBB1IP	1.405e-47	2.84e-43
SSTR1	3.603e-47	7.28e-43
CRYAB	1.414e-40	2.86e-36
HSPB2	1.414e-40	2.86e-36
CARD11	1.563e-40	3.16e-36
ADAMTS16	7.949e-40	1.61e-35
PRKCB	1.785e-38	3.61e-34
CGN	2.142e-38	4.33e-34
KCNA6	9.302e-38	1.88e-33
GRIK3	5.288e-37	1.07e-32

Figure S2. Get High-res Image As an example, this figure shows the association of APBB1IP to 'HISTOLOGICAL.TYPE'. P value = 1.4e-47 with ANOVA analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

6 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S6. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	83
	YES	279

	Significant markers	N = 6
	Higher in YES	6
	Higher in NO	0

List of 6 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7. Get Full Table List of 6 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
MAP2K2	5.67	7.412e-08	0.0015	0.6952
GPR89A	5.33	3.242e-07	0.00655	0.7156
WBP4	5.23	3.969e-07	0.00802	0.6592
FZD8	4.91	1.931e-06	0.039	0.669
C22ORF13	4.87	2.4e-06	0.0485	0.6528
SNRPD3	4.87	2.4e-06	0.0485	0.6528

Figure S3. Get High-res Image As an example, this figure shows the association of MAP2K2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.41e-08 with T-test analysis.

Clinical variable #5: 'COMPLETENESS.OF.RESECTION'

18 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S8. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	239
	R1	18
	R2	12
	RX	25

	Significant markers	N = 18

List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S9. Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

	ANOVA_P	Q
MYRIP	7.886e-12	1.59e-07
TRIM37	5.54e-11	1.12e-06
TNFRSF19	1.524e-09	3.08e-05
BICC1	1.665e-09	3.36e-05
TMEM145	4.332e-08	0.000875
CPPED1	5.198e-08	0.00105
FAM82A1	1.091e-07	0.0022
CEBPA	1.533e-07	0.0031
LOC80054	1.533e-07	0.0031
NDUFA12	2.533e-07	0.00512

Figure S4. Get High-res Image As an example, this figure shows the association of MYRIP to 'COMPLETENESS.OF.RESECTION'. P value = 7.89e-12 with ANOVA analysis.

Methods & Data

Input

Expresson data file = UCEC-TP.meth.by_min_expr_corr.data.txt
Clinical data file = UCEC-TP.clin.merged.picked.txt
Number of patients = 362
Number of genes = 20206
Number of clinical features = 5

Survival analysis

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

Correlation analysis

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

ANOVA analysis

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

Student's t-test analysis

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

Q value calculation

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.

Download Results

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.

References

[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)

[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)

[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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