Ovarian Serous Cystadenocarcinoma: Correlation between gene methylation status and clinical features

Maintained by Juok Cho (Broad Institute)

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 12215 genes and 5 clinical features across 296 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

1 gene correlated to 'Time to Death'.

NOL3

12 genes correlated to 'AGE'.

SDR16C5 , KCNIP1 , PDZK1 , ATP2B2 , ITGBL1 , ...

No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', 'TUMOR.STAGE', and 'NEOADJUVANT.THERAPY'.

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=1	shorter survival	N=0	longer survival	N=1
AGE	Spearman correlation test	N=12	older	N=1	younger	N=11
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=0
TUMOR STAGE	Spearman correlation test	N=0
NEOADJUVANT THERAPY	t test	N=0

Clinical variable #1: 'Time to Death'

One 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=28.3)
	censored	N = 125
	death	N = 169

	Significant markers	N = 1
	associated with shorter survival	0
	associated with longer survival	1

List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
NOL3	0.02	3.794e-06	0.046	0.412

Figure S1. Get High-res Image As an example, this figure shows the association of NOL3 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 3.79e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

12 genes related to 'AGE'.

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


AGE	Mean (SD)	59.17 (11)
	Significant markers	N = 12
	pos. correlated	1
	neg. correlated	11

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
SDR16C5	-0.323	2.035e-08	0.000249
KCNIP1	-0.3053	1.259e-07	0.00154
PDZK1	-0.2867	7.455e-07	0.00911
ATP2B2	-0.2838	9.742e-07	0.0119
ITGBL1	-0.2833	1.021e-06	0.0125
NRM	-0.2821	1.137e-06	0.0139
GRM2	-0.2816	1.194e-06	0.0146
IL12RB2	-0.2783	1.603e-06	0.0196
PCDHGB7	0.2773	1.753e-06	0.0214
PDE4A	-0.2726	2.655e-06	0.0324

Figure S2. Get High-res Image As an example, this figure shows the association of SDR16C5 to 'AGE'. P value = 2.03e-08 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 S5. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	76 (14)
	Score	N
	60	5
	80	8
	100	2

	Significant markers	N = 0

Clinical variable #4: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S6. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	3.06 (0.42)
		N
	Stage 2	18
	Stage 3	240
	Stage 4	36

	Significant markers	N = 0

Clinical variable #5: 'NEOADJUVANT.THERAPY'

No gene related to 'NEOADJUVANT.THERAPY'.

Table S7. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	240
	YES	56

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = OV.meth.for_correlation.filtered_data.txt
Clinical data file = OV.clin.merged.picked.txt
Number of patients = 296
Number of genes = 12215
Number of clinical features = 5

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

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] 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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