Uterine Corpus Endometrioid Carcinoma: 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 17782 genes and 5 clinical features across 152 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

24 genes correlated to 'AGE'.

ALS2CL , SLFN14 , PIK3C2B , GPRC5C , NDOR1 , ...

971 genes correlated to 'HISTOLOGICAL.TYPE'.

WDTC1 , EXD1 , C7ORF70 , TMEM61 , CHP , ...

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

TMEM125

1 gene correlated to 'NEOADJUVANT.THERAPY'.

ZFHX3

No genes correlated to 'Time to Death'

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=24	older	N=4	younger	N=20
HISTOLOGICAL TYPE	ANOVA test	N=971
RADIATIONS RADIATION REGIMENINDICATION	t test	N=1	yes	N=1	no	N=0
NEOADJUVANT THERAPY	t test	N=1	yes	N=0	no	N=1

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)	0-173.6 (median=12.6)
	censored	N = 143
	death	N = 9

	Significant markers	N = 0

Clinical variable #2: 'AGE'

24 genes related to 'AGE'.

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


AGE	Mean (SD)	63.38 (11)
	Significant markers	N = 24
	pos. correlated	4
	neg. correlated	20

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
ALS2CL	-0.4666	1.366e-09	2.43e-05
SLFN14	0.4424	1.149e-08	0.000204
PIK3C2B	-0.4163	9.564e-08	0.0017
GPRC5C	-0.4152	1.046e-07	0.00186
NDOR1	-0.403	2.642e-07	0.0047
ACAA2	-0.3969	4.139e-07	0.00736
SCARNA17	-0.3969	4.139e-07	0.00736
PLA2G15	-0.3956	4.552e-07	0.00809
NCRNA00203	-0.3944	4.963e-07	0.00882
TAC3	0.392	5.907e-07	0.0105

Figure S1. Get High-res Image As an example, this figure shows the association of ALS2CL to 'AGE'. P value = 1.37e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

971 genes related to 'HISTOLOGICAL.TYPE'.

Table S4. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	120
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	1
	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	1
	MIXED SEROUS AND ENDOMETRIOID	6
	SEROUS ENDOMETRIAL ADENOCARCINOMA	24

	Significant markers	N = 971

List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
WDTC1	6.534e-104	1.16e-99
EXD1	4.98e-83	8.86e-79
C7ORF70	6.68e-70	1.19e-65
TMEM61	5.283e-65	9.39e-61
CHP	8.728e-56	1.55e-51
PNPO	2.764e-50	4.91e-46
C9ORF80	4.563e-48	8.11e-44
ASCC3	9.97e-48	1.77e-43
LOC221442	1.327e-47	2.36e-43
GBP2	4.827e-42	8.58e-38

Figure S2. Get High-res Image As an example, this figure shows the association of WDTC1 to 'HISTOLOGICAL.TYPE'. P value = 6.53e-104 with ANOVA analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S6. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	46
	YES	106

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

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

Table S7. Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
TMEM125	5.11	9.798e-07	0.0174	0.7334

Figure S3. Get High-res Image As an example, this figure shows the association of TMEM125 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 9.8e-07 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

One gene related to 'NEOADJUVANT.THERAPY'.

Table S8. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	48
	YES	104

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

List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

Table S9. Get Full Table List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
ZFHX3	-4.92	2.504e-06	0.0445	0.6983

Figure S4. Get High-res Image As an example, this figure shows the association of ZFHX3 to 'NEOADJUVANT.THERAPY'. P value = 2.5e-06 with T-test analysis.

Methods & Data

Input

Expresson data file = UCEC.meth.for_correlation.filtered_data.txt
Clinical data file = UCEC.clin.merged.picked.txt
Number of patients = 152
Number of genes = 17782
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

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

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

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

[5] 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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