Correlation between miR expression and clinical features

Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between miR expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1KD1X01

Overview

Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 817 miRs and 8 clinical features across 561 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one miRs.

30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

HSA-MIR-551B* , HSA-MIR-198 , HSA-MIR-298 , HSA-MIR-422A , HSA-MIR-193B* , ...

30 miRs correlated to 'YEARS_TO_BIRTH'.

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

1 miR correlated to 'RACE'.

HSA-MIR-145

No miRs correlated to 'PRIMARY_SITE_OF_DISEASE', 'KARNOFSKY_PERFORMANCE_SCORE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'COMPLETENESS_OF_RESECTION', 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 miRs that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature	Statistical test	Significant miRs	Associated with		Associated with
DAYS_TO_DEATH_OR_LAST_FUP	Cox regression test	N=30	shorter survival	N=19	longer survival	N=11
YEARS_TO_BIRTH	Spearman correlation test	N=30	older	N=8	younger	N=22
PRIMARY_SITE_OF_DISEASE	Kruskal-Wallis test	N=0
KARNOFSKY_PERFORMANCE_SCORE	Spearman correlation test	N=0
RADIATIONS_RADIATION_REGIMENINDICATION	Wilcoxon test	N=0
COMPLETENESS_OF_RESECTION	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis test	N=1
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 miRs 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)	0.3-180.2 (median=28.7)
	censored	N = 268
	death	N = 292

	Significant markers	N = 30
	associated with shorter survival	19
	associated with longer survival	11

List of top 10 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
HSA-MIR-551B*	9.8	0.0001053	0.078	0.578
HSA-MIR-198	1.9	0.0001908	0.078	0.571
HSA-MIR-298	20	0.0003771	0.099	0.563
HSA-MIR-422A	2.1	0.0006343	0.099	0.554
HSA-MIR-193B*	2.1	0.0008353	0.099	0.559
HUR_2	0	0.0008426	0.099	0.471
HSA-MIR-519E*	3.4	0.0008472	0.099	0.552
HCMV-MIR-UL112	14	0.001126	0.11	0.566
HSA-MIR-505	0.65	0.00134	0.11	0.438
HSA-MIR-518C*	1.49	0.001456	0.11	0.569

Clinical variable #2: 'YEARS_TO_BIRTH'

30 miRs related to 'YEARS_TO_BIRTH'.

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


YEARS_TO_BIRTH	Mean (SD)	59.72 (12)
	Significant markers	N = 30
	pos. correlated	8
	neg. correlated	22

List of top 10 miRs differentially expressed by 'YEARS_TO_BIRTH'

Table S4. Get Full Table List of top 10 miRs significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

	SpearmanCorr	corrP	Q
HSA-MIR-30B*	-0.2247	1.001e-07	4.83e-05
HSA-MIR-30D*	-0.2235	1.183e-07	4.83e-05
HSA-MIR-30B	-0.2034	1.515e-06	0.000413
HUR_4	0.1978	2.942e-06	0.000558
HSA-MIR-30D	-0.1965	3.414e-06	0.000558
HSA-LET-7A*	-0.1643	0.0001082	0.0147
HSA-MIR-331-3P	0.162	0.0001356	0.0158
HSA-MIR-449A	-0.1582	0.0001943	0.017
HSA-MIR-338-3P	0.1573	0.0002122	0.017
HSA-MIR-193B	-0.1566	0.0002274	0.017

Clinical variable #3: 'PRIMARY_SITE_OF_DISEASE'

No miR related to 'PRIMARY_SITE_OF_DISEASE'.

Table S5. Basic characteristics of clinical feature: 'PRIMARY_SITE_OF_DISEASE'


PRIMARY_SITE_OF_DISEASE	Labels	N
	OMENTUM	2
	OVARY	557
	PERITONEUM OVARY	2

	Significant markers	N = 0

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: 'RADIATIONS_RADIATION_REGIMENINDICATION'

No miR related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

Table S7. Basic characteristics of clinical feature: 'RADIATIONS_RADIATION_REGIMENINDICATION'


RADIATIONS_RADIATION_REGIMENINDICATION	Labels	N
	NO	3
	YES	558

	Significant markers	N = 0

Clinical variable #6: 'COMPLETENESS_OF_RESECTION'

No miR related to 'COMPLETENESS_OF_RESECTION'.

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


COMPLETENESS_OF_RESECTION	Labels	N
	R0	14
	R1	27
	R2	1

	Significant markers	N = 0

Clinical variable #7: 'RACE'

One miR related to 'RACE'.

Table S9. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	2
	ASIAN	20
	BLACK OR AFRICAN AMERICAN	23
	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	1
	WHITE	485

	Significant markers	N = 1

List of one miR differentially expressed by 'RACE'

Table S10. Get Full Table List of one miR differentially expressed by 'RACE'

	kruskal_wallis_P	Q
HSA-MIR-145	0.0002809	0.229

Clinical variable #8: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S11. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	11
	NOT HISPANIC OR LATINO	329

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = OV-TP.mirna__h_mirna_8x15kv2__unc_edu__Level_3__unc_DWD_Batch_adjusted__data.data.txt
Clinical data file = OV-TP.merged_data.txt
Number of patients = 561
Number of miRs = 817
Number of clinical features = 8

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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