Correlation between gene methylation status and clinical features

Ovarian Serous Cystadenocarcinoma (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): Ovarian Serous Cystadenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C10P0X04

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 13024 genes and 4 clinical features across 261 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

13 genes correlated to 'AGE'.

C16ORF45 , PCDHGA3 , PCDHGA8 , PCDHGB3 , PCDHGB6 , ...

No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', and 'TUMOR.STAGE'.

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=0
AGE	Spearman correlation test	N=13	older	N=5	younger	N=8
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=0
TUMOR STAGE	Spearman correlation 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)	0.3-180.2 (median=28.2)
	censored	N = 112
	death	N = 147

	Significant markers	N = 0

Clinical variable #2: 'AGE'

13 genes related to 'AGE'.

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


AGE	Mean (SD)	58.95 (11)
	Significant markers	N = 13
	pos. correlated	5
	neg. correlated	8

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
C16ORF45	-0.3202	1.835e-07	0.00239
PCDHGA3	0.3176	2.317e-07	0.00302
PCDHGA8	0.3176	2.317e-07	0.00302
PCDHGB3	0.3176	2.317e-07	0.00302
PCDHGB6	0.3143	3.145e-07	0.00409
PCDHGB7	0.3143	3.145e-07	0.00409
KLK12	-0.3141	3.194e-07	0.00416
PDZK1	-0.3095	5.679e-07	0.00739
KCNIP1	-0.3031	8.536e-07	0.0111
VWA5B1	-0.2914	2.302e-06	0.03

Figure S1. Get High-res Image As an example, this figure shows the association of C16ORF45 to 'AGE'. P value = 1.83e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S4. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	74.29 (12)
	Score	N
	60	5
	80	8
	100	1

	Significant markers	N = 0

Clinical variable #4: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S5. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	3.06 (0.44)
		N
	Stage 2	18
	Stage 3	209
	Stage 4	33

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = OV-TP.meth.for_correlation.filtered_data.txt
Clinical data file = OV-TP.clin.merged.picked.txt
Number of patients = 261
Number of genes = 13024
Number of clinical features = 4

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

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] 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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