Correlation between mRNA expression and clinical features

Glioblastoma Multiforme (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1CJ8BWT

Overview

Introduction

This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 12042 genes and 6 clinical features across 525 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

78 genes correlated to 'AGE'.

FBXO17 , KIAA0495 , RANBP17 , NOL3 , TUSC3 , ...

21 genes correlated to 'GENDER'.

DDX3Y , RPS4Y1 , JARID1D , EIF1AY , NLGN4Y , ...

1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

TM4SF20

578 genes correlated to 'HISTOLOGICAL.TYPE'.

VIP , CDH8 , CLDN3 , KCNV1 , RYR2 , ...

No genes correlated to 'Time to Death', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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=78	older	N=63	younger	N=15
GENDER	t test	N=21	male	N=13	female	N=8
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=1	higher score	N=1	lower score	N=0
HISTOLOGICAL TYPE	ANOVA test	N=578
RADIATIONS RADIATION REGIMENINDICATION	t test	N=0

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)	0.1-127.6 (median=10.4)
	censored	N = 92
	death	N = 433

	Significant markers	N = 0

Clinical variable #2: 'AGE'

78 genes related to 'AGE'.

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


AGE	Mean (SD)	57.68 (15)
	Significant markers	N = 78
	pos. correlated	63
	neg. correlated	15

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
FBXO17	0.3074	5.924e-13	7.13e-09
KIAA0495	0.2949	5.437e-12	6.55e-08
RANBP17	-0.2916	9.501e-12	1.14e-07
NOL3	0.2831	3.903e-11	4.7e-07
TUSC3	-0.2761	1.217e-10	1.46e-06
C14ORF45	0.2726	2.115e-10	2.55e-06
DRG2	0.2617	1.147e-09	1.38e-05
C5ORF21	0.2498	6.579e-09	7.92e-05
CBR1	0.2455	1.203e-08	0.000145
H2AFY2	-0.2455	1.206e-08	0.000145

Figure S1. Get High-res Image As an example, this figure shows the association of FBXO17 to 'AGE'. P value = 5.92e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

21 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	205
	MALE	320

	Significant markers	N = 21
	Higher in MALE	13
	Higher in FEMALE	8

List of top 10 genes differentially expressed by 'GENDER'

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

	T(pos if higher in 'MALE')	ttestP	Q	AUC
DDX3Y	37.88	6.04e-149	7.27e-145	0.9584
RPS4Y1	40.8	3.155e-144	3.8e-140	0.9504
JARID1D	34.8	3.172e-138	3.82e-134	0.9571
EIF1AY	34.85	3.22e-137	3.88e-133	0.9499
NLGN4Y	30.29	1.034e-111	1.24e-107	0.9388
UTY	26.77	4.476e-99	5.39e-95	0.9435
USP9Y	22.07	1.533e-75	1.84e-71	0.934
CYORF15B	22.11	4.66e-75	5.61e-71	0.9433
ZFY	14.7	4.243e-41	5.11e-37	0.8444
HDHD1A	-10.45	1.615e-22	1.94e-18	0.7641

Figure S2. Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 6.04e-149 with T-test analysis.

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

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S6. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	77.12 (14)
	Significant markers	N = 1
	pos. correlated	1
	neg. correlated	0

List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S7. Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

	SpearmanCorr	corrP	Q
TM4SF20	0.2356	2.62e-06	0.0316

Figure S3. Get High-res Image As an example, this figure shows the association of TM4SF20 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 2.62e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

578 genes related to 'HISTOLOGICAL.TYPE'.

Table S8. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	GLIOBLASTOMA MULTIFORME (GBM)	6
	TREATED PRIMARY GBM	20
	UNTREATED PRIMARY (DE NOVO) GBM	499

	Significant markers	N = 578

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

Table S9. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
VIP	8.545e-23	1.03e-18
CDH8	1.951e-22	2.35e-18
CLDN3	3.941e-21	4.75e-17
KCNV1	3.825e-20	4.6e-16
RYR2	7.965e-20	9.59e-16
PAX8	2.083e-19	2.51e-15
CLCA4	3.139e-19	3.78e-15
KIAA0774	4.213e-19	5.07e-15
NEUROD6	6.786e-19	8.17e-15
CACNG3	9.112e-19	1.1e-14

Figure S4. Get High-res Image As an example, this figure shows the association of VIP to 'HISTOLOGICAL.TYPE'. P value = 8.55e-23 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S10. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	360
	YES	165

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = GBM-TP.medianexp.txt
Clinical data file = GBM-TP.merged_data.txt
Number of patients = 525
Number of genes = 12042
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

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

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

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

[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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