Thyroid Adenocarcinoma: 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 20236 genes and 5 clinical features across 157 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

37 genes correlated to 'AGE'.

MGA , SPATA18 , PBX4 , CMTM2 , ZNF518B , ...

5 genes correlated to 'GENDER'.

KIF4B , FAM35A , GLUD1 , TLE1 , ANKRD20A4

249 genes correlated to 'HISTOLOGICAL.TYPE'.

CSF2 , ARHGEF2 , PACSIN3 , LOC730755 , C5ORF62 , ...

3 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

STX17 , PPM1E , DSCR9

176 genes correlated to 'NEOADJUVANT.THERAPY'.

POP1 , SEMA3F , SLC10A6 , DSN1 , PPM1J , ...

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=37	older	N=37	younger	N=0
GENDER	t test	N=5	male	N=1	female	N=4
HISTOLOGICAL TYPE	ANOVA test	N=249
RADIATIONS RADIATION REGIMENINDICATION	t test	N=3	yes	N=3	no	N=0
NEOADJUVANT THERAPY	t test	N=176	yes	N=77	no	N=99

Clinical variable #1: 'AGE'

37 genes related to 'AGE'.

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


AGE	Mean (SD)	46.72 (16)
	Significant markers	N = 37
	pos. correlated	37
	neg. correlated	0

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

Table S2. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MGA	0.5078	1.137e-11	2.3e-07
SPATA18	0.503	1.906e-11	3.86e-07
PBX4	0.4972	3.499e-11	7.08e-07
CMTM2	0.4932	5.286e-11	1.07e-06
ZNF518B	0.4842	1.316e-10	2.66e-06
GPR37	0.4753	3.184e-10	6.44e-06
STAC2	0.4603	1.313e-09	2.66e-05
SIPA1	0.4493	3.574e-09	7.23e-05
ELOVL2	0.4463	4.661e-09	9.43e-05
DHX40	0.4428	6.36e-09	0.000129

Figure S1. Get High-res Image As an example, this figure shows the association of MGA to 'AGE'. P value = 1.14e-11 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'GENDER'

5 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	113
	MALE	44

	Significant markers	N = 5
	Higher in MALE	1
	Higher in FEMALE	4

List of 5 genes differentially expressed by 'GENDER'

Table S4. Get Full Table List of 5 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
KIF4B	-8.54	1.988e-13	4.02e-09	0.8566
FAM35A	-7.11	6.045e-10	1.22e-05	0.8455
GLUD1	-7.13	1.214e-09	2.46e-05	0.8315
TLE1	-6.43	2.013e-08	0.000407	0.8439
ANKRD20A4	5	2.291e-06	0.0463	0.7671

Figure S2. Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 1.99e-13 with T-test analysis.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

249 genes related to 'HISTOLOGICAL.TYPE'.

Table S5. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	OTHER	4
	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	91
	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	43
	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)	19

	Significant markers	N = 249

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

Table S6. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
CSF2	2.205e-16	4.46e-12
ARHGEF2	1.033e-15	2.09e-11
PACSIN3	1.028e-14	2.08e-10
LOC730755	1.309e-14	2.65e-10
C5ORF62	3.023e-14	6.12e-10
MIR21	3.308e-14	6.69e-10
SLC34A2	4.799e-14	9.71e-10
FUK	6.523e-14	1.32e-09
TNFRSF1A	1.06e-13	2.14e-09
MIR146B	1.188e-13	2.4e-09

Figure S3. Get High-res Image As an example, this figure shows the association of CSF2 to 'HISTOLOGICAL.TYPE'. P value = 2.2e-16 with ANOVA analysis.

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

3 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S7. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	14
	YES	143

	Significant markers	N = 3
	Higher in YES	3
	Higher in NO	0

List of 3 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8. Get Full Table List of 3 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
STX17	7.47	7.066e-12	1.43e-07	0.6993
PPM1E	5.81	6.135e-07	0.0124	0.7483
DSCR9	5.49	6.448e-07	0.013	0.7488

Figure S4. Get High-res Image As an example, this figure shows the association of STX17 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.07e-12 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

176 genes related to 'NEOADJUVANT.THERAPY'.

Table S9. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	3
	YES	154

	Significant markers	N = 176
	Higher in YES	77
	Higher in NO	99

List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S10. Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
POP1	-21.09	5.595e-40	1.13e-35	0.9545
SEMA3F	14.24	4.122e-29	8.34e-25	0.9589
SLC10A6	13.98	1.188e-28	2.4e-24	0.9697
DSN1	-14.21	1.548e-28	3.13e-24	0.9048
PPM1J	-13.61	2.937e-28	5.94e-24	0.8528
CCRL2	-14.13	1.091e-27	2.21e-23	0.881
C11ORF52	14.11	4.231e-27	8.56e-23	0.9351
ECEL1P2	12.62	1.665e-25	3.37e-21	0.8831
ALAS1	13.08	8.375e-23	1.69e-18	0.9177
CCBL1	-13.77	7.327e-21	1.48e-16	0.8874

Figure S5. Get High-res Image As an example, this figure shows the association of POP1 to 'NEOADJUVANT.THERAPY'. P value = 5.6e-40 with T-test analysis.

Methods & Data

Input

Expresson data file = THCA.meth.for_correlation.filtered_data.txt
Clinical data file = THCA.clin.merged.picked.txt
Number of patients = 157
Number of genes = 20236
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.

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] 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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