Correlation between miR expression and clinical features

PANCANCER cohort with 12 disease types (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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 (2013): PANCANCER cohort with 12 disease types (Primary solid tumor cohort) - 21 April 2013: Correlation between miR expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Q23X68

Overview

Introduction

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

Summary

Testing the association between 817 miRs and 7 clinical features across 560 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.

5 miRs correlated to 'AGE'.

HSA-MIR-30B* , HSA-MIR-30D* , HSA-MIR-30B , HUR_4 , HSA-MIR-30D

14 miRs correlated to 'PRIMARY.SITE.OF.DISEASE'.

EBV-MIR-BART6-5P , HSA-MIR-96* , HSA-MIR-614 , KSHV-MIR-K12-9* , HSA-MIR-600 , ...

6 miRs correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

HSA-MIR-125B-1* , HSA-MIR-338-5P , EBV-MIR-BART17-5P , HSA-MIR-589* , HSA-MIR-518D-5P , ...

No miRs correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', 'TUMOR.STAGE', and 'COMPLETENESS.OF.RESECTION'.

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 miRs that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature	Statistical test	Significant miRs	Associated with		Associated with
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=5	older	N=1	younger	N=4
PRIMARY SITE OF DISEASE	ANOVA test	N=14
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=0
TUMOR STAGE	Spearman correlation test	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=6	yes	N=6	no	N=0
COMPLETENESS OF RESECTION	t test	N=0

Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	0.3-180.2 (median=28.2)
	censored	N = 265
	death	N = 290

	Significant markers	N = 0

Clinical variable #2: 'AGE'

5 miRs related to 'AGE'.

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


AGE	Mean (SD)	59.72 (12)
	Significant markers	N = 5
	pos. correlated	1
	neg. correlated	4

List of 5 miRs significantly correlated to 'AGE' by Spearman correlation test

Table S3. Get Full Table List of 5 miRs significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
HSA-MIR-30B*	-0.2258	8.937e-08	7.3e-05
HSA-MIR-30D*	-0.2229	1.305e-07	0.000106
HSA-MIR-30B	-0.2046	1.338e-06	0.00109
HUR_4	0.1982	2.857e-06	0.00233
HSA-MIR-30D	-0.196	3.718e-06	0.00302

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-30B* to 'AGE'. P value = 8.94e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

14 miRs related to 'PRIMARY.SITE.OF.DISEASE'.

Table S4. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'


PRIMARY.SITE.OF.DISEASE	Labels	N
	OMENTUM	2
	OVARY	556
	PERITONEUM (OVARY)	2

	Significant markers	N = 14

List of top 10 miRs differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S5. Get Full Table List of top 10 miRs differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

	ANOVA_P	Q
EBV-MIR-BART6-5P	6.206e-15	5.07e-12
HSA-MIR-96*	7.377e-13	6.02e-10
HSA-MIR-614	1.994e-12	1.63e-09
KSHV-MIR-K12-9*	2.263e-11	1.84e-08
HSA-MIR-600	2.513e-11	2.04e-08
HSA-MIR-26A-2*	1.058e-10	8.59e-08
EBV-MIR-BART9	2.718e-10	2.2e-07
HSA-MIR-548C-3P	6.115e-10	4.95e-07
HSA-MIR-374A*	4.45e-09	3.6e-06
HCMV-MIR-UL36*	1.391e-08	1.12e-05

Figure S2. Get High-res Image As an example, this figure shows the association of EBV-MIR-BART6-5P to 'PRIMARY.SITE.OF.DISEASE'. P value = 6.21e-15 with ANOVA analysis.

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

No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.

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


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	75.64 (13)
	Score	N
	40	2
	60	20
	80	49
	100	7

	Significant markers	N = 0

Clinical variable #5: 'TUMOR.STAGE'

No miR related to 'TUMOR.STAGE'.

Table S7. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	3.05 (0.56)
		N
	Stage 1	16
	Stage 2	25
	Stage 3	430
	Stage 4	85

	Significant markers	N = 0

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

6 miRs related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S8. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	3
	YES	557

	Significant markers	N = 6
	Higher in YES	6
	Higher in NO	0

List of 6 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S9. Get Full Table List of 6 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
HSA-MIR-125B-1*	12.1	1.84e-17	1.5e-14	0.7887
HSA-MIR-338-5P	12.55	1.637e-15	1.34e-12	0.8031
EBV-MIR-BART17-5P	8.01	4.928e-14	4.02e-11	0.6912
HSA-MIR-589*	13.09	1.558e-12	1.27e-09	0.766
HSA-MIR-518D-5P	6.97	5.384e-10	4.38e-07	0.681
HSA-MIR-486-5P	15.9	2.159e-08	1.75e-05	0.8259

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-125B-1* to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.84e-17 with T-test analysis.

Clinical variable #7: 'COMPLETENESS.OF.RESECTION'

No miR related to 'COMPLETENESS.OF.RESECTION'.

Table S10. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	14
	R1	28

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = PANCAN12-TP.mirna__h_mirna_8x15kv2__unc_edu__Level_3__unc_DWD_Batch_adjusted__data.data.txt
Clinical data file = PANCAN12-TP.clin.merged.picked.txt
Number of patients = 560
Number of miRs = 817
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

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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