Correlation between gene methylation status and clinical features

Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Uterine Corpus Endometrioid Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1CV4FQD

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 17621 genes and 5 clinical features across 256 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

2 genes correlated to 'Time to Death'.

TMEM84 , SLC38A8

202 genes correlated to 'AGE'.

SLFN14 , PLA2G15 , ALS2CL , ACAA2 , SCARNA17 , ...

2178 genes correlated to 'HISTOLOGICAL.TYPE'.

CARD11 , NCRNA00203 , ABCB6 , KCNIP2 , SSTR1 , ...

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

GRIK3 , FOXG1

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

DNASE1L2 , TNFRSF19 , HIST1H2BO , MYRIP , PRRG2 , ...

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=2	shorter survival	N=0	longer survival	N=2
AGE	Spearman correlation test	N=202	older	N=67	younger	N=135
HISTOLOGICAL TYPE	ANOVA test	N=2178
RADIATIONS RADIATION REGIMENINDICATION	t test	N=2	yes	N=2	no	N=0
COMPLETENESS OF RESECTION	ANOVA test	N=33

Clinical variable #1: 'Time to Death'

2 genes related to 'Time to Death'.

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


Time to Death	Duration (Months)	0-187.1 (median=13.4)
	censored	N = 233
	death	N = 21

	Significant markers	N = 2
	associated with shorter survival	0
	associated with longer survival	2

List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
TMEM84	0	3.618e-08	0.00064	0.344
SLC38A8	0	1.92e-06	0.034	0.413

Figure S1. Get High-res Image As an example, this figure shows the association of TMEM84 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 3.62e-08 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

202 genes related to 'AGE'.

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


AGE	Mean (SD)	63.44 (11)
	Significant markers	N = 202
	pos. correlated	67
	neg. correlated	135

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
SLFN14	0.4397	1.577e-13	2.78e-09
PLA2G15	-0.4152	4.325e-12	7.62e-08
ALS2CL	-0.3959	4.875e-11	8.59e-07
ACAA2	-0.3856	1.686e-10	2.97e-06
SCARNA17	-0.3856	1.686e-10	2.97e-06
C19ORF55	-0.3813	2.782e-10	4.9e-06
SCGBL	0.375	5.694e-10	1e-05
ADAMTS15	-0.372	7.997e-10	1.41e-05
AHSP	0.3694	1.068e-09	1.88e-05
CYP1A2	0.3694	1.07e-09	1.88e-05

Figure S2. Get High-res Image As an example, this figure shows the association of SLFN14 to 'AGE'. P value = 1.58e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

2178 genes related to 'HISTOLOGICAL.TYPE'.

Table S5. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	207
	MIXED SEROUS AND ENDOMETRIOID	9
	SEROUS ENDOMETRIAL ADENOCARCINOMA	40

	Significant markers	N = 2178

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

Table S6. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
CARD11	9.062e-40	1.6e-35
NCRNA00203	1.667e-36	2.94e-32
ABCB6	4.747e-36	8.36e-32
KCNIP2	2.182e-34	3.84e-30
SSTR1	2.235e-34	3.94e-30
CRYAB	6.25e-34	1.1e-29
HSPB2	1.204e-33	2.12e-29
SH3BP2	2.58e-32	4.55e-28
LASP1	4.22e-32	7.43e-28
ASAP2	5.793e-32	1.02e-27

Figure S3. Get High-res Image As an example, this figure shows the association of CARD11 to 'HISTOLOGICAL.TYPE'. P value = 9.06e-40 with ANOVA analysis.

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

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

Table S7. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	70
	YES	186

	Significant markers	N = 2
	Higher in YES	2
	Higher in NO	0

List of 2 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8. Get Full Table List of 2 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
GRIK3	5.07	7.589e-07	0.0134	0.6538
FOXG1	5.07	9.327e-07	0.0164	0.6659

Figure S4. Get High-res Image As an example, this figure shows the association of GRIK3 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.59e-07 with T-test analysis.

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

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

Table S9. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	173
	R1	15
	R2	8
	RX	20

	Significant markers	N = 33

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

Table S10. Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

	ANOVA_P	Q
DNASE1L2	1.784e-10	3.14e-06
TNFRSF19	4.474e-08	0.000788
HIST1H2BO	7.921e-08	0.0014
MYRIP	1.159e-07	0.00204
PRRG2	1.218e-07	0.00215
PSMA1	1.998e-07	0.00352
SYT12	2.11e-07	0.00372
CCL2	2.316e-07	0.00408
AVL9	3.629e-07	0.00639
IGFBP3	5.195e-07	0.00915

Figure S5. Get High-res Image As an example, this figure shows the association of DNASE1L2 to 'COMPLETENESS.OF.RESECTION'. P value = 1.78e-10 with ANOVA analysis.

Methods & Data

Input

Expresson data file = UCEC-TP.meth.for_correlation.filtered_data.txt
Clinical data file = UCEC-TP.clin.merged.picked.txt
Number of patients = 256
Number of genes = 17621
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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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