Correlation between gene methylation status and clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

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/C1Q23Z4S

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 19472 genes and 11 clinical features across 243 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

218 genes correlated to 'AGE'.

KCNS2 , RAB3D , CDCA7 , WTIP , AFAP1 , ...

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

HIST1H3G , PPP1R15A

79 genes correlated to 'GENDER'.

ALG11__1 , UTP14C , C17ORF73 , CCDC121__1 , GPN1__1 , ...

92 genes correlated to 'RACE'.

SCAMP5 , SERHL2 , NUP88__1 , RPAIN__1 , DHRS7 , ...

No genes correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', '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=218	older	N=124	younger	N=94
NEOPLASM DISEASESTAGE	Kruskal-Wallis test	N=0
PATHOLOGY T STAGE	Spearman correlation test	N=2	higher stage	N=1	lower stage	N=1
PATHOLOGY N STAGE	Wilcoxon test	N=0
PATHOLOGY M STAGE	Kruskal-Wallis test	N=0
GENDER	Wilcoxon test	N=79	male	N=79	female	N=0
HISTOLOGICAL TYPE	Kruskal-Wallis test	N=0
COMPLETENESS OF RESECTION	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis test	N=92
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)	0-113 (median=13.7)
	censored	N = 138
	death	N = 84

	Significant markers	N = 0

Clinical variable #2: 'AGE'

218 genes related to 'AGE'.

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


AGE	Mean (SD)	60.36 (14)
	Significant markers	N = 218
	pos. correlated	124
	neg. correlated	94

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
KCNS2	0.4197	1.173e-11	2.28e-07
RAB3D	0.4151	2.069e-11	4.03e-07
CDCA7	0.3873	5.186e-10	1.01e-05
WTIP	0.3725	2.574e-09	5.01e-05
AFAP1	0.37	3.346e-09	6.51e-05
QSOX1__1	0.3674	4.397e-09	8.56e-05
BAALC__1	0.3597	9.631e-09	0.000187
C8ORF56__1	0.3597	9.631e-09	0.000187
ARHGEF3__1	-0.3575	1.297e-08	0.000252
SPATA12	-0.3575	1.297e-08	0.000252

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S4. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	91
	STAGE II	57
	STAGE III	2
	STAGE IIIA	52
	STAGE IIIB	7
	STAGE IIIC	9
	STAGE IV	2
	STAGE IVA	1
	STAGE IVB	2

	Significant markers	N = 0

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

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

Table S5. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	1.96 (0.96)
		N
	0	1
	1	99
	2	63
	3	65
	4	13

	Significant markers	N = 2
	pos. correlated	1
	neg. correlated	1

List of 2 genes differentially expressed by 'PATHOLOGY.T.STAGE'

Table S6. Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
HIST1H3G	0.3321	1.292e-07	0.00252
PPP1R15A	-0.2959	2.935e-06	0.0571

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

No gene related to 'PATHOLOGY.N.STAGE'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'


PATHOLOGY.N.STAGE	Labels	N
	class0	153
	class1	4

	Significant markers	N = 0

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

No gene related to 'PATHOLOGY.M.STAGE'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'


PATHOLOGY.M.STAGE	Labels	N
	M0	171
	M1	4
	MX	68

	Significant markers	N = 0

Clinical variable #7: 'GENDER'

79 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	88
	MALE	155

	Significant markers	N = 79
	Higher in MALE	79
	Higher in FEMALE	0

List of top 10 genes differentially expressed by 'GENDER'

Table S10. Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
ALG11__1	13077	1.502e-32	2.92e-28	0.9587
UTP14C	13077	1.502e-32	2.92e-28	0.9587
C17ORF73	3400	8.41e-11	1.64e-06	0.7507
CCDC121__1	10119	3.769e-10	7.34e-06	0.7419
GPN1__1	10119	3.769e-10	7.34e-06	0.7419
FAM83A	3529	4.154e-10	8.09e-06	0.7413
LOC100131726	3529	4.154e-10	8.09e-06	0.7413
ALDH3A1	3707	3.418e-09	6.65e-05	0.7282
CLDND1	3738	4.878e-09	9.49e-05	0.726
CUX2	3768	6.86e-09	0.000134	0.7238

Clinical variable #8: 'HISTOLOGICAL.TYPE'

No gene related to 'HISTOLOGICAL.TYPE'.

Table S11. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	FIBROLAMELLAR CARCINOMA	2
	HEPATOCELLULAR CARCINOMA	235
	HEPATOCHOLANGIOCARCINOMA (MIXED)	6

	Significant markers	N = 0

Clinical variable #9: 'COMPLETENESS.OF.RESECTION'

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

Table S12. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	206
	R1	13
	R2	1
	RX	17

	Significant markers	N = 0

Clinical variable #10: 'RACE'

92 genes related to 'RACE'.

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


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	1
	ASIAN	71
	BLACK OR AFRICAN AMERICAN	15
	WHITE	147

	Significant markers	N = 92

List of top 10 genes differentially expressed by 'RACE'

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

	ANOVA_P	Q
SCAMP5	5.61e-14	1.09e-09
SERHL2	1.791e-13	3.49e-09
NUP88__1	7.74e-12	1.51e-07
RPAIN__1	7.74e-12	1.51e-07
DHRS7	9.111e-12	1.77e-07
SNX16	1.498e-11	2.92e-07
C15ORF53	1.931e-09	3.76e-05
FOXO1	4.968e-09	9.67e-05
WASH3P	6.699e-09	0.00013
C21ORF56	1.414e-08	0.000275

Clinical variable #11: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S15. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	9
	NOT HISPANIC OR LATINO	218

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = LIHC-TP.meth.by_min_clin_corr.data.txt
Clinical data file = LIHC-TP.merged_data.txt
Number of patients = 243
Number of genes = 19472
Number of clinical features = 11

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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