Correlation between gene methylation status and clinical features

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 12759 genes and 6 clinical features across 280 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

2 genes correlated to 'Time to Death'.

RNF39 , TMEM106A

126 genes correlated to 'AGE'.

PRKCB , CYYR1 , KLF14 , ZNF540 , SRD5A2 , ...

7 genes correlated to 'GENDER'.

NUS1 , TLE1 , ZNF770 , BAG1 , GLUD1 , ...

2 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

SORBS3 , STXBP6

1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

ADCY8

No genes correlated to '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=2	shorter survival	N=0	longer survival	N=2
AGE	Spearman correlation test	N=126	older	N=110	younger	N=16
GENDER	t test	N=7	male	N=1	female	N=6
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=2	higher score	N=2	lower score	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=1	yes	N=0	no	N=1
NEOADJUVANT THERAPY	t test	N=0

Clinical variable #1: 'Time to Death'

2 genes related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	0.1-127.6 (median=10)
	censored	N = 72
	death	N = 208

	Significant markers	N = 2
	associated with shorter survival	0
	associated with longer survival	2

List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
RNF39	0.12	1.59e-06	0.02	0.432
TMEM106A	0.18	3.435e-06	0.044	0.449

Figure S1. Get High-res Image As an example, this figure shows the association of RNF39 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.59e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

126 genes related to 'AGE'.

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


AGE	Mean (SD)	57.55 (15)
	Significant markers	N = 126
	pos. correlated	110
	neg. correlated	16

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
PRKCB	0.4226	1.485e-13	1.89e-09
CYYR1	0.4156	4.066e-13	5.19e-09
KLF14	0.3958	6.176e-12	7.88e-08
ZNF540	0.3956	6.332e-12	8.08e-08
SRD5A2	0.3904	1.252e-11	1.6e-07
HOXD8	0.3887	1.553e-11	1.98e-07
SYT10	0.3859	2.253e-11	2.87e-07
HCN1	0.3755	8.292e-11	1.06e-06
ZNF560	0.3749	8.98e-11	1.15e-06
MACROD2	0.3711	1.425e-10	1.82e-06

Figure S2. Get High-res Image As an example, this figure shows the association of PRKCB to 'AGE'. P value = 1.48e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

7 genes related to 'GENDER'.

Table S5. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	114
	MALE	166

	Significant markers	N = 7
	Higher in MALE	1
	Higher in FEMALE	6

List of 7 genes differentially expressed by 'GENDER'

Table S6. Get Full Table List of 7 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
NUS1	-19.8	7.234e-39	9.23e-35	0.9729
TLE1	-11.83	4.131e-26	5.27e-22	0.8498
ZNF770	-9.36	5.873e-18	7.49e-14	0.7976
BAG1	7.35	3.607e-12	4.6e-08	0.7828
GLUD1	-6.02	7.549e-09	9.63e-05	0.7209
RAB9P1	-5.49	9.499e-08	0.00121	0.7035
SERBP1	-5.51	1.051e-07	0.00134	0.6823

Figure S3. Get High-res Image As an example, this figure shows the association of NUS1 to 'GENDER'. P value = 7.23e-39 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

2 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S7. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	75.35 (15)
	Significant markers	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S8. Get Full Table List of 2 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

	SpearmanCorr	corrP	Q
SORBS3	0.3188	2.157e-06	0.0275
STXBP6	0.3115	3.557e-06	0.0454

Figure S4. Get High-res Image As an example, this figure shows the association of SORBS3 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 2.16e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S9. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	206
	YES	74

	Significant markers	N = 1
	Higher in YES	0
	Higher in NO	1

List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S10. Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
ADCY8	-4.99	1.212e-06	0.0155	0.5544

Figure S5. Get High-res Image As an example, this figure shows the association of ADCY8 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.21e-06 with T-test analysis.

Clinical variable #6: 'NEOADJUVANT.THERAPY'

No gene related to 'NEOADJUVANT.THERAPY'.

Table S11. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	164
	YES	116

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = GBM.meth.for_correlation.filtered_data.txt
Clinical data file = GBM.clin.merged.picked.txt
Number of patients = 280
Number of genes = 12759
Number of clinical features = 6

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.

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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