Correlation between gene methylation status and clinical features

Brain Lower Grade Glioma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

doi:10.7908/C19021TS

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/C19021TS

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 20213 genes and 6 clinical features across 203 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

408 genes correlated to 'Time to Death'.

HS3ST4 , SSTR1 , GALNT14 , RAB6C , HPD , ...

158 genes correlated to 'AGE'.

CD163L1 , HOXD8 , LOC150786 , ADAMTSL3 , PAX9 , ...

15 genes correlated to 'GENDER'.

ALG11__1 , UTP14C , POLDIP3 , RNU12 , FAM35A , ...

911 genes correlated to 'HISTOLOGICAL.TYPE'.

BVES , REST , SNAPC2 , MAPKAP1 , SLC2A4RG , ...

No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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=408	shorter survival	N=65	longer survival	N=343
AGE	Spearman correlation test	N=158	older	N=126	younger	N=32
GENDER	t test	N=15	male	N=6	female	N=9
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=0
HISTOLOGICAL TYPE	ANOVA test	N=911
RADIATIONS RADIATION REGIMENINDICATION	t test	N=0

Clinical variable #1: 'Time to Death'

408 genes related to 'Time to Death'.

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


Time to Death	Duration (Months)	0-211.2 (median=13.3)
	censored	N = 157
	death	N = 45

	Significant markers	N = 408
	associated with shorter survival	65
	associated with longer survival	343

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
HS3ST4	191	3.757e-12	7.6e-08	0.771
SSTR1	131	4.744e-11	9.6e-07	0.782
GALNT14	121	6.344e-11	1.3e-06	0.793
RAB6C	1701	9.804e-11	2e-06	0.8
HPD	0	1.005e-10	2e-06	0.291
ZNF492	76	1.119e-10	2.3e-06	0.675
ATF3	0	1.236e-10	2.5e-06	0.276
LPAR3	161	1.417e-10	2.9e-06	0.751
CD274	0.01	2.172e-10	4.4e-06	0.283
TLK1	0.02	5.083e-10	1e-05	0.279

Figure S1. Get High-res Image As an example, this figure shows the association of HS3ST4 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 3.76e-12 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

158 genes related to 'AGE'.

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


AGE	Mean (SD)	42.88 (13)
	Significant markers	N = 158
	pos. correlated	126
	neg. correlated	32

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
CD163L1	0.5388	1.106e-16	2.24e-12
HOXD8	0.5203	1.77e-15	3.58e-11
LOC150786	0.519	2.138e-15	4.32e-11
ADAMTSL3	0.4994	3.358e-14	6.79e-10
PAX9	0.498	4.052e-14	8.19e-10
SLC18A2	0.4886	1.417e-13	2.86e-09
GALNT14	0.4805	4.034e-13	8.15e-09
RAB6C	0.4757	7.395e-13	1.49e-08
SSTR4	0.4709	1.34e-12	2.71e-08
HOXD11	0.4625	3.748e-12	7.57e-08

Figure S2. Get High-res Image As an example, this figure shows the association of CD163L1 to 'AGE'. P value = 1.11e-16 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

15 genes related to 'GENDER'.

Table S5. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	88
	MALE	115

	Significant markers	N = 15
	Higher in MALE	6
	Higher in FEMALE	9

List of top 10 genes differentially expressed by 'GENDER'

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

	T(pos if higher in 'MALE')	ttestP	Q	AUC
ALG11__1	21.3	4.404e-41	8.9e-37	0.977
UTP14C	21.3	4.404e-41	8.9e-37	0.977
POLDIP3	-14.87	1.692e-30	3.42e-26	0.9379
RNU12	-14.87	1.692e-30	3.42e-26	0.9379
FAM35A	-10.91	5.274e-22	1.07e-17	0.8348
GLUD1	-10.91	5.274e-22	1.07e-17	0.8348
WBP11P1	8.78	1.634e-15	3.3e-11	0.8367
TFDP1	-7.35	6.298e-12	1.27e-07	0.8641
KIF4B	-6.92	1.284e-10	2.59e-06	0.7491
ZNF839	-6.06	8.311e-09	0.000168	0.7737

Figure S3. Get High-res Image As an example, this figure shows the association of ALG11__1 to 'GENDER'. P value = 4.4e-41 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S7. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	88.43 (11)
	Significant markers	N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

911 genes related to 'HISTOLOGICAL.TYPE'.

Table S8. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	ASTROCYTOMA	60
	OLIGOASTROCYTOMA	55
	OLIGODENDROGLIOMA	87

	Significant markers	N = 911

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

Table S9. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
BVES	9.613e-17	1.94e-12
REST	5.037e-16	1.02e-11
SNAPC2	1.844e-15	3.73e-11
MAPKAP1	1.856e-15	3.75e-11
SLC2A4RG	1.927e-15	3.89e-11
GLIS3	7.083e-15	1.43e-10
TMEM51	1.125e-14	2.27e-10
CBX2	2.836e-14	5.73e-10
S100PBP__1	4.039e-14	8.16e-10
EMP1	4.181e-14	8.45e-10

Figure S4. Get High-res Image As an example, this figure shows the association of BVES to 'HISTOLOGICAL.TYPE'. P value = 9.61e-17 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S10. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	76
	YES	127

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = LGG-TP.meth.by_min_expr_corr.data.txt
Clinical data file = LGG-TP.clin.merged.picked.txt
Number of patients = 203
Number of genes = 20213
Number of clinical features = 6

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

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

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

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

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

[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)

Made with Nozzle