Correlation between miR expression and clinical features

Glioblastoma Multiforme (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between miR expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C17080T1

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 534 miRs and 8 clinical features across 563 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one miRs.

28 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

HSA-MIR-222 , HSA-MIR-34A , HSA-MIR-148A , HSA-MIR-221 , HSA-MIR-34B , ...

30 miRs correlated to 'YEARS_TO_BIRTH'.

HSA-MIR-210 , HSA-MIR-148A , HSA-MIR-552 , HSA-MIR-29B , HSA-MIR-34A , ...

1 miR correlated to 'RADIATION_THERAPY'.

EBV-MIR-BART18

30 miRs correlated to 'HISTOLOGICAL_TYPE'.

HSA-MIR-302A* , HSA-MIR-515-3P , HSA-MIR-519B , EBV-MIR-BART12 , EBV-MIR-BART1-3P , ...

9 miRs correlated to 'RACE'.

HSA-MIR-624 , HSA-MIR-551A , HSA-MIR-641 , EBV-MIR-BART6-5P , HSA-MIR-302A* , ...

No miRs correlated to 'GENDER', 'KARNOFSKY_PERFORMANCE_SCORE', 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=28		N=NA		N=NA
YEARS_TO_BIRTH	Spearman correlation test	N=30	older	N=10	younger	N=20
GENDER	Wilcoxon test	N=0
RADIATION_THERAPY	Wilcoxon test	N=1	yes	N=1	no	N=0
KARNOFSKY_PERFORMANCE_SCORE	Spearman correlation test	N=0
HISTOLOGICAL_TYPE	Kruskal-Wallis test	N=30
RACE	Kruskal-Wallis test	N=9
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

28 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)	0.1-127.6 (median=12.2)
	censored	N = 93
	death	N = 469

	Significant markers	N = 28
	associated with shorter survival	NA
	associated with longer survival	NA

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. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

	logrank_P	Q	C_index
HSA-MIR-222	1.98e-08	1.1e-05	0.568
HSA-MIR-34A	9.58e-06	0.0021	0.547
HSA-MIR-148A	1.16e-05	0.0021	0.568
HSA-MIR-221	2.79e-05	0.0037	0.561
HSA-MIR-34B	0.000112	0.012	0.538
HSA-MIR-17-5P	0.0011	0.098	0.459
HSA-MIR-17-3P	0.00197	0.15	0.454
HSA-MIR-20A	0.0022	0.15	0.46
HSA-MIR-453	0.00257	0.15	0.484
HSA-MIR-210	0.003	0.15	0.553

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)	57.94 (14)
	Significant markers	N = 30
	pos. correlated	10
	neg. correlated	20

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-210	0.2092	5.509e-07	0.000294
HSA-MIR-148A	0.1965	2.633e-06	0.000703
HSA-MIR-552	-0.1701	4.983e-05	0.00887
HSA-MIR-29B	0.1615	0.0001182	0.0147
HSA-MIR-34A	0.1583	0.0001619	0.0147
HSA-MIR-625	-0.1581	0.0001647	0.0147
HSA-MIR-340	-0.1503	0.0003445	0.0257
HSA-MIR-620	-0.1491	0.0003853	0.0257
HSA-MIR-17-3P	-0.1454	0.0005375	0.0319
HSA-MIR-17-5P	-0.1429	0.0006719	0.0359

Clinical variable #3: 'GENDER'

No miR related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	219
	MALE	344

	Significant markers	N = 0

Clinical variable #4: 'RADIATION_THERAPY'

One miR related to 'RADIATION_THERAPY'.

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


RADIATION_THERAPY	Labels	N
	NO	73
	YES	466

	Significant markers	N = 1
	Higher in YES	1
	Higher in NO	0

List of one miR differentially expressed by 'RADIATION_THERAPY'

Table S7. Get Full Table List of one miR differentially expressed by 'RADIATION_THERAPY'

	W(pos if higher in 'YES')	wilcoxontestP	Q	AUC
EBV-MIR-BART18	21367	0.0004285	0.229	0.6281

Clinical variable #5: 'KARNOFSKY_PERFORMANCE_SCORE'

No miR related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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


KARNOFSKY_PERFORMANCE_SCORE	Mean (SD)	77 (16)
	Significant markers	N = 0

Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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


HISTOLOGICAL_TYPE	Labels	N
	GLIOBLASTOMA MULTIFORME (GBM)	11
	TREATED PRIMARY GBM	20
	UNTREATED PRIMARY (DE NOVO) GBM	532

	Significant markers	N = 30

List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S10. Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

	kruskal_wallis_P	Q
HSA-MIR-302A*	3.434e-05	0.0183
HSA-MIR-515-3P	0.0004329	0.116
HSA-MIR-519B	0.001028	0.12
EBV-MIR-BART12	0.001175	0.12
EBV-MIR-BART1-3P	0.001692	0.12
HSA-MIR-609	0.001718	0.12
HSA-MIR-101	0.001751	0.12
HSA-MIR-29C	0.001967	0.12
HSA-MIR-372	0.002101	0.12
HSA-MIR-302C	0.002249	0.12

Clinical variable #7: 'RACE'

9 miRs related to 'RACE'.

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


RACE	Labels	N
	ASIAN	13
	BLACK OR AFRICAN AMERICAN	30
	WHITE	495

	Significant markers	N = 9

List of 9 miRs differentially expressed by 'RACE'

Table S12. Get Full Table List of 9 miRs differentially expressed by 'RACE'

	kruskal_wallis_P	Q
HSA-MIR-624	6.761e-05	0.0361
HSA-MIR-551A	0.0005197	0.139
HSA-MIR-641	0.002953	0.277
EBV-MIR-BART6-5P	0.00326	0.277
HSA-MIR-302A*	0.003627	0.277
HSA-MIR-149	0.003705	0.277
HSA-MIR-148A	0.003837	0.277
HSA-MIR-607	0.004582	0.277
HSA-MIR-545	0.004673	0.277

Clinical variable #8: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S13. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	12
	NOT HISPANIC OR LATINO	461

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = GBM-TP.mirna__h_mirna_8x15k__unc_edu__Level_3__unc_DWD_Batch_adjusted__data.data.txt
Clinical data file = GBM-TP.merged_data.txt
Number of patients = 563
Number of miRs = 534
Number of clinical features = 8

Selected clinical features

Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

There are also useful links about clinical features.

CDE Browser

Data dictionary for clinical data

NCI wiki - Biospecimen and Clinical XSD Files Specification

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, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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)

Made with Nozzle