Correlation between mRNA expression and clinical features

Kidney Renal Clear Cell Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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/C1SF2TSP

Overview

Introduction

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

Summary

Testing the association between 17814 genes and 7 clinical features across 72 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

66 genes correlated to 'NEOPLASM.DISEASESTAGE'.

TCF23 , PAX4 , OPRS1 , SNRP70 , JARID1C , ...

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

UBIAD1 , FAM64A , M6PRBP1 , ACTL6B , PIK3C3 , ...

64 genes correlated to 'PATHOLOGY.M.STAGE'.

IGFBPL1 , CYORF15B , DDX3Y , EIF1AY , OAS1 , ...

13 genes correlated to 'GENDER'.

DDX3Y , RPS4Y1 , CYORF15A , EIF1AY , RPS4Y2 , ...

No genes correlated to 'Time to Death', 'AGE', and 'PATHOLOGY.T.STAGE'.

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=0
NEOPLASM DISEASESTAGE	ANOVA test	N=66
PATHOLOGY T STAGE	Spearman correlation test	N=0
PATHOLOGY N STAGE	t test	N=94	class1	N=52	class0	N=42
PATHOLOGY M STAGE	t test	N=64	m1	N=23	m0	N=41
GENDER	t test	N=13	male	N=13	female	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)	0.5-101.1 (median=36.8)
	censored	N = 57
	death	N = 14

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	60.54 (12)
	Significant markers	N = 0

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

66 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S3. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	40
	STAGE II	13
	STAGE III	14
	STAGE IV	5

	Significant markers	N = 66

List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S4. Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

	ANOVA_P	Q
TCF23	6.108e-12	1.09e-07
PAX4	6.492e-11	1.16e-06
OPRS1	9.388e-10	1.67e-05
SNRP70	2.371e-09	4.22e-05
JARID1C	1.003e-08	0.000179
MSTO1	1.063e-08	0.000189
SBNO2	1.133e-08	0.000202
ZNF646	2.465e-08	0.000439
GPR152	3.295e-08	0.000587
POM121L1	3.476e-08	0.000619

Figure S1. Get High-res Image As an example, this figure shows the association of TCF23 to 'NEOPLASM.DISEASESTAGE'. P value = 6.11e-12 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

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

Table S5. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	1.67 (0.84)
		N
	1	41
	2	14
	3	17

	Significant markers	N = 0

Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'


PATHOLOGY.N.STAGE	Labels	N
	class0	35
	class1	3

	Significant markers	N = 94
	Higher in class1	52
	Higher in class0	42

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

Table S7. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

	T(pos if higher in 'class1')	ttestP	Q	AUC
UBIAD1	-11.9	1.001e-13	1.77e-09	1
FAM64A	11.15	4.121e-13	7.3e-09	0.9714
M6PRBP1	12.29	5.831e-13	1.03e-08	1
ACTL6B	10.94	1.665e-12	2.95e-08	0.9714
PIK3C3	-12.6	8.97e-12	1.59e-07	1
SEMA3B	9.87	1.045e-11	1.85e-07	0.9238
SOAT2	15.76	1.32e-11	2.34e-07	1
FCHO1	9.48	2.586e-11	4.58e-07	0.9429
PSMA7	9.41	3.234e-11	5.73e-07	0.9619
ADSL	-10.42	4.212e-11	7.46e-07	0.9714

Figure S2. Get High-res Image As an example, this figure shows the association of UBIAD1 to 'PATHOLOGY.N.STAGE'. P value = 1e-13 with T-test analysis.

Clinical variable #6: 'PATHOLOGY.M.STAGE'

64 genes related to 'PATHOLOGY.M.STAGE'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'


PATHOLOGY.M.STAGE	Labels	N
	M0	67
	M1	5

	Significant markers	N = 64
	Higher in M1	23
	Higher in M0	41

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

Table S9. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

	T(pos if higher in 'M1')	ttestP	Q	AUC
IGFBPL1	-10.69	2.075e-15	3.7e-11	0.8955
CYORF15B	-10.34	1.448e-14	2.58e-10	0.9224
DDX3Y	-9.7	1.45e-14	2.58e-10	0.809
EIF1AY	-10.87	2.906e-14	5.18e-10	0.9493
OAS1	9.58	2.205e-12	3.93e-08	0.9433
RESP18	9.36	1.212e-11	2.16e-07	0.9463
PELI2	-8.79	1.784e-11	3.18e-07	0.8657
UTY	-10.33	3.74e-11	6.66e-07	0.9821
JARID1D	-10.14	4.94e-11	8.8e-07	0.9045
HOXA7	-9.6	6.012e-11	1.07e-06	0.8776

Figure S3. Get High-res Image As an example, this figure shows the association of IGFBPL1 to 'PATHOLOGY.M.STAGE'. P value = 2.07e-15 with T-test analysis.

Clinical variable #7: 'GENDER'

13 genes related to 'GENDER'.

Table S10. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	29
	MALE	43

	Significant markers	N = 13
	Higher in MALE	13
	Higher in FEMALE	0

List of top 10 genes differentially expressed by 'GENDER'

Table S11. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
DDX3Y	13.71	6.875e-21	1.22e-16	0.9575
RPS4Y1	14.08	1.088e-20	1.94e-16	0.9599
CYORF15A	13.48	2.222e-20	3.96e-16	0.9655
EIF1AY	13.4	2.577e-20	4.59e-16	0.9623
RPS4Y2	13.25	3.662e-20	6.52e-16	0.9527
JARID1D	12.43	5.763e-19	1.03e-14	0.9583
ZFY	11.55	9.008e-18	1.6e-13	0.9615
CYORF15B	11	6.54e-17	1.16e-12	0.9415
UTY	10.7	2.396e-16	4.27e-12	0.9479
USP9Y	9.59	2.26e-14	4.02e-10	0.9318

Figure S4. Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 6.87e-21 with T-test analysis.

Methods & Data

Input

Expresson data file = KIRC-TP.medianexp.txt
Clinical data file = KIRC-TP.merged_data.txt
Number of patients = 72
Number of genes = 17814
Number of clinical features = 7

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

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] 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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