Correlation between gene methylation status and clinical features

Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1B56HJJ

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 20386 genes and 7 clinical features across 380 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

513 genes correlated to 'AGE'.

ANGPT4 , ALS2CL , GPR158 , ITPK1 , NCRNA00203 , ...

3991 genes correlated to 'HISTOLOGICAL.TYPE'.

APBB1IP , SSTR1 , RASGRF1 , ADAMTS16 , ITPK1 , ...

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

RMND5B , FIZ1 , ZNF524 , ZNF831 , MAP2K2 , ...

30 genes correlated to 'RACE'.

C18ORF54 , LOC253039 , PSMD5 , ZNF525 , NCRNA00174 , ...

No genes correlated to 'Time to Death', 'COMPLETENESS.OF.RESECTION', and 'ETHNICITY'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature	Statistical test	Significant genes	Associated with		Associated with
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=513	older	N=167	younger	N=346
HISTOLOGICAL TYPE	Kruskal-Wallis test	N=3991
RADIATIONS RADIATION REGIMENINDICATION	Wilcoxon test	N=66	yes	N=66	no	N=0
COMPLETENESS OF RESECTION	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis test	N=30
ETHNICITY	Wilcoxon test	N=0

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=18)
	censored	N = 333
	death	N = 45

	Significant markers	N = 0

Clinical variable #2: 'AGE'

513 genes related to 'AGE'.

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


AGE	Mean (SD)	63.85 (11)
	Significant markers	N = 513
	pos. correlated	167
	neg. correlated	346

List of top 10 genes differentially expressed by 'AGE'

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

	SpearmanCorr	corrP	Q
ANGPT4	0.352	1.712e-12	3.49e-08
ALS2CL	-0.3468	3.785e-12	7.72e-08
GPR158	-0.3449	4.99e-12	1.02e-07
ITPK1	-0.3291	5.021e-11	1.02e-06
NCRNA00203	-0.3291	5.021e-11	1.02e-06
HDAC11	-0.3281	5.787e-11	1.18e-06
SLFN14	0.328	5.862e-11	1.19e-06
ADAMTS15	-0.3273	6.492e-11	1.32e-06
PLA2G15	-0.3226	1.257e-10	2.56e-06
EPB41L1	-0.3196	1.908e-10	3.89e-06

Clinical variable #3: 'HISTOLOGICAL.TYPE'

3991 genes related to 'HISTOLOGICAL.TYPE'.

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


HISTOLOGICAL.TYPE	Labels	N
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	278
	MIXED SEROUS AND ENDOMETRIOID	19
	SEROUS ENDOMETRIAL ADENOCARCINOMA	83

	Significant markers	N = 3991

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	5.856e-33	1.19e-28
SSTR1	1.774e-30	3.62e-26
RASGRF1	4.694e-30	9.57e-26
ADAMTS16	1.614e-29	3.29e-25
ITPK1	3.85e-29	7.85e-25
NCRNA00203	3.85e-29	7.85e-25
LRRC36__1	1.167e-28	2.38e-24
KCNA5	1.211e-28	2.47e-24
CREB5	1.279e-28	2.61e-24
ATP8A2	2.09e-28	4.26e-24

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

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

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


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	87
	YES	293

	Significant markers	N = 66
	Higher in YES	66
	Higher in NO	0

List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	W(pos if higher in 'YES')	wilcoxontestP	Q	AUC
RMND5B	17999	5.25e-09	0.000107	0.7061
FIZ1	17946	7.464e-09	0.000152	0.704
ZNF524	17946	7.464e-09	0.000152	0.704
ZNF831	17853	1.373e-08	0.00028	0.7004
MAP2K2	17695	3.774e-08	0.000769	0.6942
C14ORF93	17678	4.2e-08	0.000856	0.6935
GIPR	17649	5.037e-08	0.00103	0.6924
SAP130	17624	5.887e-08	0.0012	0.6914
GOLGA3	17606	6.583e-08	0.00134	0.6907
TMEM125	17574	8.022e-08	0.00163	0.6894

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

No gene related to 'COMPLETENESS.OF.RESECTION'.

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


COMPLETENESS.OF.RESECTION	Labels	N
	R0	253
	R1	18
	R2	13
	RX	28

	Significant markers	N = 0

Clinical variable #6: 'RACE'

30 genes related to 'RACE'.

Table S9. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	2
	ASIAN	7
	BLACK OR AFRICAN AMERICAN	68
	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	7
	WHITE	275

	Significant markers	N = 30

List of top 10 genes differentially expressed by 'RACE'

Table S10. Get Full Table List of top 10 genes differentially expressed by 'RACE'

	ANOVA_P	Q
C18ORF54	3.28e-13	6.69e-09
LOC253039	1.223e-11	2.49e-07
PSMD5	1.223e-11	2.49e-07
ZNF525	2.738e-11	5.58e-07
NCRNA00174	3.436e-11	7e-07
GRAPL	2.638e-10	5.38e-06
GRAP	1.197e-09	2.44e-05
CS	1.262e-08	0.000257
CN5H6.4	1.764e-08	0.00036
GTSE1	1.764e-08	0.00036

Clinical variable #7: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S11. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	10
	NOT HISPANIC OR LATINO	275

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = UCEC-TP.meth.by_min_clin_corr.data.txt
Clinical data file = UCEC-TP.merged_data.txt
Number of patients = 380
Number of genes = 20386
Number of clinical features = 7

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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