Correlation between gene methylation status and clinical features

Uterine Corpus Endometrioid 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/C1W094WV

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 20302 genes and 7 clinical features across 397 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

406 genes correlated to 'AGE'.

ANGPT4 , GPR158 , ALS2CL , EPB41L1 , SLFN14 , ...

4098 genes correlated to 'HISTOLOGICAL.TYPE'.

APBB1IP , RASGRF1 , SSTR1 , LRRC36__1 , KCNA5 , ...

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

RMND5B , ZNF831 , GOLGA3 , MAP2K2 , SAP130 , ...

46 genes correlated to 'RACE'.

C18ORF54 , GNB2L1 , SNORD95 , GRAPL , 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=406	older	N=149	younger	N=257
HISTOLOGICAL TYPE	Kruskal-Wallis test	N=4098
RADIATIONS RADIATION REGIMENINDICATION	Wilcoxon test	N=69	yes	N=69	no	N=0
COMPLETENESS OF RESECTION	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis test	N=46
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=18.2)
	censored	N = 350
	death	N = 45

	Significant markers	N = 0

Clinical variable #2: 'AGE'

406 genes related to 'AGE'.

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


AGE	Mean (SD)	64.01 (11)
	Significant markers	N = 406
	pos. correlated	149
	neg. correlated	257

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	0.3493	8.294e-13	1.68e-08
GPR158	-0.3313	1.346e-11	2.73e-07
ALS2CL	-0.3222	5.155e-11	1.05e-06
EPB41L1	-0.3186	8.636e-11	1.75e-06
SLFN14	0.3153	1.371e-10	2.78e-06
CTSG	0.3116	2.297e-10	4.66e-06
PLA2G15	-0.3112	2.435e-10	4.94e-06
CACNB1	-0.3111	2.477e-10	5.03e-06
ITPK1	-0.3088	3.371e-10	6.84e-06
NCRNA00203	-0.3088	3.371e-10	6.84e-06

Clinical variable #3: 'HISTOLOGICAL.TYPE'

4098 genes related to 'HISTOLOGICAL.TYPE'.

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


HISTOLOGICAL.TYPE	Labels	N
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	288
	MIXED SEROUS AND ENDOMETRIOID	20
	SEROUS ENDOMETRIAL ADENOCARCINOMA	89

	Significant markers	N = 4098

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	4.491e-35	9.12e-31
RASGRF1	5.691e-32	1.16e-27
SSTR1	1.065e-30	2.16e-26
LRRC36__1	2.351e-30	4.77e-26
KCNA5	3.873e-30	7.86e-26
ATP8A2	5.885e-30	1.19e-25
CARD11	5.888e-30	1.2e-25
DNAI1	8.802e-30	1.79e-25
PPIH	1.045e-29	2.12e-25
ADAMTS16	1.497e-29	3.04e-25

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

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

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


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	87
	YES	310

	Significant markers	N = 69
	Higher in YES	69
	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	18908	9.847e-09	2e-04	0.7011
ZNF831	18891	1.095e-08	0.000222	0.7004
GOLGA3	18880	1.172e-08	0.000238	0.7
MAP2K2	18773	2.263e-08	0.000459	0.6961
SAP130	18720	3.12e-08	0.000633	0.6941
C14ORF93	18639	5.067e-08	0.00103	0.6911
GIPR	18618	5.74e-08	0.00116	0.6903
TMEM125	18572	7.528e-08	0.00153	0.6886
GUSBL2	18530	9.624e-08	0.00195	0.6871
C11ORF2	18364	2.494e-07	0.00506	0.6809

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	265
	R1	18
	R2	13
	RX	29

	Significant markers	N = 0

Clinical variable #6: 'RACE'

46 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	82
	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	7
	WHITE	277

	Significant markers	N = 46

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	8.167e-16	1.66e-11
GNB2L1	3.074e-14	6.24e-10
SNORD95	3.074e-14	6.24e-10
GRAPL	1.605e-12	3.26e-08
NCRNA00174	3.242e-12	6.58e-08
ZNF525	1.001e-11	2.03e-07
GRAP	8.428e-11	1.71e-06
LOC253039	1.093e-10	2.22e-06
PSMD5	1.093e-10	2.22e-06
CN5H6.4	8.887e-10	1.8e-05

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	287

	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 = 397
Number of genes = 20302
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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