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 20008 genes and 5 clinical features across 162 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

1 gene correlated to 'AGE'.

LYNX1

15 genes correlated to 'PATHOLOGY.N.STAGE'.

DYRK2 , DLEU2__2 , CCL2 , TDRKH , KCNJ2 , ...

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

CTNNA3__1 , LRRTM3__1 , CTNNA3 , LRRTM3 , AKR1B10 , ...

No genes correlated to 'PATHOLOGY.T.STAGE', 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=1	older	N=1	younger	N=0
PATHOLOGY T STAGE	Spearman correlation test	N=0
PATHOLOGY N STAGE	t test	N=15	class1	N=0	class0	N=15
COMPLETENESS OF RESECTION	ANOVA test	N=7
NUMBER OF LYMPH NODES	Spearman correlation test	N=0

Clinical variable #1: 'AGE'

One gene related to 'AGE'.

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


AGE	Mean (SD)	60.04 (6.8)
	Significant markers	N = 1
	pos. correlated	1
	neg. correlated	0

List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S2. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
LYNX1	0.3624	2.313e-06	0.0463

Figure S1. Get High-res Image As an example, this figure shows the association of LYNX1 to 'AGE'. P value = 2.31e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'PATHOLOGY.T.STAGE'

No gene related to 'PATHOLOGY.T.STAGE'.

Table S3. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	2.61 (0.55)
		N
	2	68
	3	88
	4	5

	Significant markers	N = 0

Clinical variable #3: 'PATHOLOGY.N.STAGE'

15 genes related to 'PATHOLOGY.N.STAGE'.

Table S4. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'


PATHOLOGY.N.STAGE	Labels	N
	class0	128
	class1	15

	Significant markers	N = 15
	Higher in class1	0
	Higher in class0	15

List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

	T(pos if higher in 'class1')	ttestP	Q	AUC
DYRK2	-6.8	3.481e-09	6.96e-05	0.7432
DLEU2__2	-6.19	6.253e-08	0.00125	0.8177
CCL2	-6.08	7.085e-08	0.00142	0.8333
TDRKH	-5.68	1.115e-07	0.00223	0.7054
KCNJ2	-5.59	1.283e-07	0.00257	0.7307
RRM2	-5.79	3.398e-07	0.0068	0.7583
NUP54	-5.28	8.503e-07	0.017	0.6745
DDX21	-5.26	8.622e-07	0.0172	0.7172
ABCE1	-5.6	1.092e-06	0.0218	0.7635
ANAPC10	-5.6	1.092e-06	0.0218	0.7635

Figure S2. Get High-res Image As an example, this figure shows the association of DYRK2 to 'PATHOLOGY.N.STAGE'. P value = 3.48e-09 with T-test analysis.

Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

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

Table S6. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	119
	R1	31
	RX	3

	Significant markers	N = 7

List of 7 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S7. Get Full Table List of 7 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

	ANOVA_P	Q
CTNNA3__1	3.73e-13	7.46e-09
LRRTM3__1	3.73e-13	7.46e-09
CTNNA3	8.428e-13	1.69e-08
LRRTM3	8.428e-13	1.69e-08
AKR1B10	2.937e-09	5.87e-05
CAPN3	6.627e-07	0.0133
ZNF814	1.515e-06	0.0303

Figure S3. Get High-res Image As an example, this figure shows the association of CTNNA3__1 to 'COMPLETENESS.OF.RESECTION'. P value = 3.73e-13 with ANOVA analysis.

Clinical variable #5: 'NUMBER.OF.LYMPH.NODES'

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

Table S8. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'


NUMBER.OF.LYMPH.NODES	Mean (SD)	0.2 (0.72)
	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 = 162
Number of genes = 20008
Number of clinical features = 5

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.

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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