Correlation between gene methylation status and clinical features

Sarcoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Q23ZBG

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 19856 genes and 5 clinical features across 250 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

30 genes correlated to 'YEARS_TO_BIRTH'.

RANBP17 , PLEKHM3 , HLA-DQA2 , CCL3 , SLC29A1 , ...

30 genes correlated to 'GENDER'.

ALG11__1 , UTP14C , FAM35A , GLUD1 , FRG1B , ...

1 gene correlated to 'RACE'.

ISCA1

No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', and 'ETHNICITY'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature	Statistical test	Significant genes	Associated with		Associated with
DAYS_TO_DEATH_OR_LAST_FUP	Cox regression test	N=0
YEARS_TO_BIRTH	Spearman correlation test	N=30	older	N=8	younger	N=22
GENDER	Wilcoxon test	N=30	male	N=30	female	N=0
RACE	Kruskal-Wallis test	N=1
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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


DAYS_TO_DEATH_OR_LAST_FUP	Duration (Months)	0.1-188.2 (median=23.1)
	censored	N = 166
	death	N = 83

	Significant markers	N = 0

Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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


YEARS_TO_BIRTH	Mean (SD)	61.35 (14)
	Significant markers	N = 30
	pos. correlated	8
	neg. correlated	22

List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S3. Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

	SpearmanCorr	corrP	Q
RANBP17	0.3454	2.19e-08	0.000157
PLEKHM3	0.3453	2.214e-08	0.000157
HLA-DQA2	-0.3446	2.368e-08	0.000157
CCL3	-0.3367	5.128e-08	0.00024
SLC29A1	0.3343	6.461e-08	0.00024
SOHLH1	-0.3323	7.827e-08	0.00024
CCL18	-0.3303	9.505e-08	0.00024
CD33	-0.3301	9.68e-08	0.00024
TMEM63A	0.328	1.175e-07	0.000259
LOC153328	-0.324	1.71e-07	0.000303

Clinical variable #3: 'GENDER'

30 genes related to 'GENDER'.

Table S4. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	135
	MALE	115

	Significant markers	N = 30
	Higher in MALE	30
	Higher in FEMALE	0

List of top 10 genes differentially expressed by 'GENDER'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
ALG11__1	14154	3.434e-29	3.41e-25	0.9117
UTP14C	14154	3.434e-29	3.41e-25	0.9117
FAM35A	3883	9.963e-12	4.95e-08	0.7499
GLUD1	3883	9.963e-12	4.95e-08	0.7499
FRG1B	4594	2.708e-08	9.24e-05	0.7041
KIF4B	4597	2.791e-08	9.24e-05	0.7039
HFE2	4800	2.016e-07	0.000572	0.6908
MIR548H4__1	4909	5.541e-07	0.0011	0.6838
NOX5__1	4909	5.541e-07	0.0011	0.6838
SPESP1__1	4909	5.541e-07	0.0011	0.6838

Clinical variable #4: 'RACE'

One gene related to 'RACE'.

Table S6. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	ASIAN	6
	BLACK OR AFRICAN AMERICAN	18
	WHITE	198

	Significant markers	N = 1

List of one gene differentially expressed by 'RACE'

Table S7. Get Full Table List of one gene differentially expressed by 'RACE'

	kruskal_wallis_P	Q
ISCA1	5.837e-06	0.116

Clinical variable #5: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S8. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	5
	NOT HISPANIC OR LATINO	194

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = SARC-TP.meth.by_min_clin_corr.data.txt
Clinical data file = SARC-TP.merged_data.txt
Number of patients = 250
Number of genes = 19856
Number of clinical features = 5

Selected clinical features

For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .
Survival time data

Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

if 'vital_status'==0(alive),

if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

if 'vital_status'==NA,excludes this case in survival analysis and report the case.

cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

This analysis excluded clinical variables that has only NA values.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)

[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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