Correlation between gene methylation status and clinical features

Prostate Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_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/C1542KN9

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 20063 genes and 4 clinical features across 153 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

7 genes correlated to 'LYMPH.NODE.METASTASIS'.

DYRK2 , DLEU2__2 , KCNJ2 , RRM2 , TDRKH , ...

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

WHAMM , CD36 , TGM4 , ZNF219

No genes correlated to 'AGE', and 'NUMBER.OF.LYMPH.NODES'.

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
AGE	Spearman correlation test	N=0
LYMPH NODE METASTASIS	t test	N=7	n1	N=0	n0	N=7
COMPLETENESS OF RESECTION	ANOVA test	N=4
NUMBER OF LYMPH NODES	Spearman correlation test	N=0

Clinical variable #1: 'AGE'

No gene related to 'AGE'.

Table S1. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	60.23 (6.8)
	Significant markers	N = 0

Clinical variable #2: 'LYMPH.NODE.METASTASIS'

7 genes related to 'LYMPH.NODE.METASTASIS'.

Table S2. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'


LYMPH.NODE.METASTASIS	Labels	N
	N0	120
	N1	15

	Significant markers	N = 7
	Higher in N1	0
	Higher in N0	7

List of 7 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S3. Get Full Table List of 7 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

	T(pos if higher in 'N1')	ttestP	Q	AUC
DYRK2	-6.73	3.523e-09	7.07e-05	0.745
DLEU2__2	-6.24	3.859e-08	0.000774	0.8217
KCNJ2	-5.67	9.209e-08	0.00185	0.7428
RRM2	-5.87	2.088e-07	0.00419	0.7717
TDRKH	-5.53	2.57e-07	0.00515	0.7023
NME1-NME2__2	-5.79	1.69e-06	0.0339	0.775
NME2__1	-5.79	1.69e-06	0.0339	0.775

Figure S1. Get High-res Image As an example, this figure shows the association of DYRK2 to 'LYMPH.NODE.METASTASIS'. P value = 3.52e-09 with T-test analysis.

Clinical variable #3: 'COMPLETENESS.OF.RESECTION'

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

Table S4. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	115
	R1	30
	RX	2

	Significant markers	N = 4

List of 4 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S5. Get Full Table List of 4 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

	ANOVA_P	Q
WHAMM	1.927e-20	3.87e-16
CD36	2.317e-08	0.000465
TGM4	1.32e-07	0.00265
ZNF219	1.173e-06	0.0235

Figure S2. Get High-res Image As an example, this figure shows the association of WHAMM to 'COMPLETENESS.OF.RESECTION'. P value = 1.93e-20 with ANOVA analysis.

Clinical variable #4: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S6. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'


NUMBER.OF.LYMPH.NODES	Mean (SD)	0.21 (0.74)
	Significant markers	N = 0

Methods & Data

Input

Expresson data file = PRAD-TP.meth.by_min_expr_corr.data.txt
Clinical data file = PRAD-TP.clin.merged.picked.txt
Number of patients = 153
Number of genes = 20063
Number of clinical features = 4

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] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)

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

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

[4] 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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