Correlation between miRseq expression and clinical features

Sarcoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1639NVF

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 508 miRs and 5 clinical features across 248 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-3677 , HSA-MIR-520B , HSA-MIR-3680 , HSA-MIR-512-1 , HSA-MIR-92A-2 , ...

30 miRs correlated to 'YEARS_TO_BIRTH'.

HSA-MIR-1245 , HSA-MIR-511-1 , HSA-MIR-21 , HSA-MIR-22 , HSA-MIR-155 , ...

27 miRs correlated to 'GENDER'.

HSA-MIR-22 , HSA-MIR-887 , HSA-MIR-488 , HSA-MIR-106A , HSA-MIR-187 , ...

No miRs correlated to 'RACE', 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=30	longer survival	N=0
YEARS_TO_BIRTH	Spearman correlation test	N=30	older	N=28	younger	N=2
GENDER	Wilcoxon test	N=27	male	N=27	female	N=0
RACE	Kruskal-Wallis test	N=0
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)	0.1-188.2 (median=23.1)
	censored	N = 164
	death	N = 83

	Significant markers	N = 30
	associated with shorter survival	30
	associated with longer survival	0

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-3677	1.49	1.87e-05	0.0065	0.649
HSA-MIR-520B	1.34	2.566e-05	0.0065	0.689
HSA-MIR-3680	1.72	4.619e-05	0.0073	0.678
HSA-MIR-512-1	1.36	5.741e-05	0.0073	0.65
HSA-MIR-92A-2	1.49	0.0001314	0.012	0.641
HSA-MIR-301B	1.28	0.0001461	0.012	0.627
HSA-MIR-17	1.47	0.0001964	0.013	0.625
HSA-MIR-3605	1.4	0.0002089	0.013	0.653
HSA-MIR-361	1.64	0.0002966	0.014	0.621
HSA-MIR-301A	1.37	0.0003013	0.014	0.635

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)	61.33 (14)
	Significant markers	N = 30
	pos. correlated	28
	neg. correlated	2

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-1245	0.3272	2.712e-07	0.000138
HSA-MIR-511-1	0.2858	6.842e-06	0.00154
HSA-MIR-21	0.2782	9.067e-06	0.00154
HSA-MIR-22	0.2665	2.194e-05	0.00279
HSA-MIR-155	0.2612	3.241e-05	0.00329
HSA-MIR-589	0.254	5.376e-05	0.00455
HSA-MIR-511-2	0.248	0.0001032	0.00749
HSA-MIR-339	0.2323	0.00023	0.0146
HSA-MIR-424	0.2281	0.0003011	0.017
HSA-MIR-616	0.221	0.0005212	0.0265

Clinical variable #3: 'GENDER'

27 miRs related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	133
	MALE	115

	Significant markers	N = 27
	Higher in MALE	27
	Higher in FEMALE	0

List of top 10 miRs differentially expressed by 'GENDER'

Table S6. Get Full Table List of top 10 miRs differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
HSA-MIR-22	9911	5.895e-05	0.0299	0.648
HSA-MIR-887	5509	0.0001476	0.0375	0.6398
HSA-MIR-488	1080	0.0006126	0.0989	0.6851
HSA-MIR-106A	5782	0.0009313	0.0989	0.622
HSA-MIR-187	2706	0.001008	0.0989	0.6454
HSA-MIR-301A	5824	0.001212	0.0989	0.6192
HSA-MIR-374A	9428	0.00158	0.0989	0.6164
HSA-MIR-26A-2	9417	0.001689	0.0989	0.6157
HSA-MIR-454	5884	0.001751	0.0989	0.6153
HSA-MIR-205	1752	0.002133	0.108	0.65

Clinical variable #4: 'RACE'

No miR related to 'RACE'.

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


RACE	Labels	N
	ASIAN	6
	BLACK OR AFRICAN AMERICAN	18
	WHITE	197

	Significant markers	N = 0

Clinical variable #5: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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


ETHNICITY	Labels	N
	HISPANIC OR LATINO	5
	NOT HISPANIC OR LATINO	193

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = SARC-TP.miRseq_RPKM_log2.txt
Clinical data file = SARC-TP.merged_data.txt
Number of patients = 248
Number of miRs = 508
Number of clinical features = 5

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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