Correlation between gene methylation status and clinical features

Kidney Renal Clear Cell 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/C1610XPD

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 20201 genes and 9 clinical features across 285 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

481 genes correlated to 'Time to Death'.

RIOK3 , FLJ42289 , TLL2 , RPRD2 , CCL26 , ...

21 genes correlated to 'AGE'.

ELOVL2 , MRPS33 , TSPYL5 , DOK6 , ZYG11A , ...

544 genes correlated to 'NEOPLASM.DISEASESTAGE'.

KDR , OPRK1 , FAM38B , CDH8 , CRHBP , ...

837 genes correlated to 'PATHOLOGY.T.STAGE'.

KDR , OPRK1 , ACTA1 , DBX2 , SYN2 , ...

9 genes correlated to 'PATHOLOGY.N.STAGE'.

CARD16 , CASP1 , ZFP64 , TSPO , SFXN5 , ...

91 genes correlated to 'PATHOLOGY.M.STAGE'.

C20ORF112 , PLCD1 , HTR6 , OPRK1 , MUSK , ...

91 genes correlated to 'GENDER'.

ALG11__1 , UTP14C , CCBL2 , RBMXL1 , KIF4B , ...

No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', and 'NUMBERPACKYEARSSMOKED'.

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=481	shorter survival	N=285	longer survival	N=196
AGE	Spearman correlation test	N=21	older	N=17	younger	N=4
NEOPLASM DISEASESTAGE	ANOVA test	N=544
PATHOLOGY T STAGE	Spearman correlation test	N=837	higher stage	N=388	lower stage	N=449
PATHOLOGY N STAGE	t test	N=9	class1	N=1	class0	N=8
PATHOLOGY M STAGE	t test	N=91	m1	N=83	m0	N=8
GENDER	t test	N=91	male	N=10	female	N=81
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=0
NUMBERPACKYEARSSMOKED	Spearman correlation test	N=0

Clinical variable #1: 'Time to Death'

481 genes related to 'Time to Death'.

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


Time to Death	Duration (Months)	0.1-120.6 (median=29.1)
	censored	N = 190
	death	N = 95

	Significant markers	N = 481
	associated with shorter survival	285
	associated with longer survival	196

List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
RIOK3	15001	2.793e-13	5.6e-09	0.676
FLJ42289	0.03	6.262e-13	1.3e-08	0.312
TLL2	0.02	2.306e-12	4.7e-08	0.316
RPRD2	60	5.609e-12	1.1e-07	0.683
CCL26	0.06	1.934e-11	3.9e-07	0.351
GRIN2D	0	3.216e-11	6.5e-07	0.318
EVI2A	0.04	4.242e-11	8.6e-07	0.346
NF1__3	0.04	4.242e-11	8.6e-07	0.346
ARHGEF12	44	4.264e-11	8.6e-07	0.641
IGLL1	0.01	9.025e-11	1.8e-06	0.312

Figure S1. Get High-res Image As an example, this figure shows the association of RIOK3 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.79e-13 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

21 genes related to 'AGE'.

Table S3. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	61.53 (12)
	Significant markers	N = 21
	pos. correlated	17
	neg. correlated	4

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

Table S4. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
ELOVL2	0.4638	1.31e-16	2.65e-12
MRPS33	0.3371	5.272e-09	0.000106
TSPYL5	0.3261	1.746e-08	0.000353
DOK6	0.322	2.691e-08	0.000544
ZYG11A	0.3163	4.844e-08	0.000978
ME3	-0.3131	6.727e-08	0.00136
PVT1	-0.3063	1.319e-07	0.00266
RANBP17	0.3052	1.48e-07	0.00299
ADAMTS17	0.3034	1.765e-07	0.00356
PCOLCE2	-0.2952	3.882e-07	0.00784

Figure S2. Get High-res Image As an example, this figure shows the association of ELOVL2 to 'AGE'. P value = 1.31e-16 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

544 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S5. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	130
	STAGE II	28
	STAGE III	73
	STAGE IV	54

	Significant markers	N = 544

List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S6. Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

	ANOVA_P	Q
KDR	9.363e-20	1.89e-15
OPRK1	1.514e-18	3.06e-14
FAM38B	8.657e-16	1.75e-11
CDH8	5.007e-14	1.01e-09
CRHBP	8.798e-14	1.78e-09
FAM162B	1.105e-13	2.23e-09
PCDHGA1__6	1.348e-13	2.72e-09
PCDHGA10__4	1.348e-13	2.72e-09
PCDHGA11__3	1.348e-13	2.72e-09
PCDHGA2__6	1.348e-13	2.72e-09

Figure S3. Get High-res Image As an example, this figure shows the association of KDR to 'NEOPLASM.DISEASESTAGE'. P value = 9.36e-20 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

837 genes related to 'PATHOLOGY.T.STAGE'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	1.96 (0.98)
		N
	1	133
	2	37
	3	107
	4	8

	Significant markers	N = 837
	pos. correlated	388
	neg. correlated	449

List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S8. Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
KDR	0.4997	2.097e-19	4.24e-15
OPRK1	0.4724	3.011e-17	6.08e-13
ACTA1	0.4688	5.581e-17	1.13e-12
DBX2	0.445	2.877e-15	5.81e-11
SYN2	0.4436	3.609e-15	7.29e-11
SLC35F1	0.4432	3.834e-15	7.74e-11
NEUROD2	0.4418	4.795e-15	9.68e-11
RRM2	-0.4303	2.844e-14	5.74e-10
SOX17	0.4189	1.543e-13	3.11e-09
FAM38B	0.4187	1.583e-13	3.2e-09

Figure S4. Get High-res Image As an example, this figure shows the association of KDR to 'PATHOLOGY.T.STAGE'. P value = 2.1e-19 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N.STAGE'

9 genes related to 'PATHOLOGY.N.STAGE'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'


PATHOLOGY.N.STAGE	Labels	N
	class0	127
	class1	9

	Significant markers	N = 9
	Higher in class1	1
	Higher in class0	8

List of 9 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S10. Get Full Table List of 9 genes differentially expressed by 'PATHOLOGY.N.STAGE'

	T(pos if higher in 'class1')	ttestP	Q	AUC
CARD16	-6.6	3.144e-09	6.35e-05	0.6745
CASP1	-6.6	3.144e-09	6.35e-05	0.6745
ZFP64	-5.43	3.028e-07	0.00612	0.629
TSPO	-5.59	4.472e-07	0.00903	0.6325
SFXN5	-6.12	5.343e-07	0.0108	0.7019
VGF	-5.2	7.459e-07	0.0151	0.6527
CHCHD7__1	-5.09	1.193e-06	0.0241	0.776
PLAG1__1	-5.09	1.193e-06	0.0241	0.776
LOC150568	5.59	1.315e-06	0.0266	0.7434

Figure S5. Get High-res Image As an example, this figure shows the association of CARD16 to 'PATHOLOGY.N.STAGE'. P value = 3.14e-09 with T-test analysis.

Clinical variable #6: 'PATHOLOGY.M.STAGE'

91 genes related to 'PATHOLOGY.M.STAGE'.

Table S11. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'


PATHOLOGY.M.STAGE	Labels	N
	M0	233
	M1	52

	Significant markers	N = 91
	Higher in M1	83
	Higher in M0	8

List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

Table S12. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

	T(pos if higher in 'M1')	ttestP	Q	AUC
C20ORF112	7.96	3.528e-13	7.13e-09	0.7739
PLCD1	6.73	2.361e-10	4.77e-06	0.7195
HTR6	7.05	4.663e-10	9.42e-06	0.7646
OPRK1	6.93	6.028e-10	1.22e-05	0.7524
MUSK	6.52	1.426e-09	2.88e-05	0.7216
SESN1__1	6.47	1.647e-09	3.33e-05	0.7147
STK24	6.52	3.172e-09	6.41e-05	0.754
PDGFB	6.28	3.259e-09	6.58e-05	0.7221
CSDC2	6.59	3.618e-09	7.31e-05	0.7508
ASB4	6.25	4.217e-09	8.52e-05	0.6966

Figure S6. Get High-res Image As an example, this figure shows the association of C20ORF112 to 'PATHOLOGY.M.STAGE'. P value = 3.53e-13 with T-test analysis.

Clinical variable #7: 'GENDER'

91 genes related to 'GENDER'.

Table S13. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	96
	MALE	189

	Significant markers	N = 91
	Higher in MALE	10
	Higher in FEMALE	81

List of top 10 genes differentially expressed by 'GENDER'

Table S14. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
ALG11__1	18.71	9.907e-35	2e-30	0.9804
UTP14C	18.71	9.907e-35	2e-30	0.9804
CCBL2	13.68	3.031e-31	6.12e-27	0.8779
RBMXL1	13.68	3.031e-31	6.12e-27	0.8779
KIF4B	-11.76	5.928e-25	1.2e-20	0.8729
CCDC146__1	-10.91	3.153e-23	6.37e-19	0.8083
C5ORF27	-10.37	1.125e-20	2.27e-16	0.8165
LRRC41	10.26	1.547e-20	3.13e-16	0.7615
UQCRH	10.26	1.547e-20	3.13e-16	0.7615
DNAJB13	-10.1	1.567e-20	3.16e-16	0.794

Figure S7. Get High-res Image As an example, this figure shows the association of ALG11__1 to 'GENDER'. P value = 9.91e-35 with T-test analysis.

Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S15. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	92.5 (8)
	Score	N
	70	1
	80	3
	90	12
	100	12

	Significant markers	N = 0

Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S16. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'


NUMBERPACKYEARSSMOKED	Mean (SD)	34 (16)
	Value	N
	10	1
	40	2
	46	1

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = KIRC-TP.meth.by_min_expr_corr.data.txt
Clinical data file = KIRC-TP.clin.merged.picked.txt
Number of patients = 285
Number of genes = 20201
Number of clinical features = 9

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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