Correlation between miRseq expression and clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12B8WX1

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 544 miRs and 11 clinical features across 312 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one miRs.

9 miRs correlated to 'Time to Death'.

HSA-MIR-149 , HSA-MIR-632 , HSA-MIR-23C , HSA-MIR-3677 , HSA-MIR-139 , ...

28 miRs correlated to 'AGE'.

HSA-MIR-412 , HSA-MIR-1269 , HSA-MIR-181B-1 , HSA-MIR-200C , HSA-MIR-296 , ...

3 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

HSA-MIR-23C , HSA-MIR-139 , HSA-MIR-550A-1

24 miRs correlated to 'PATHOLOGY.T.STAGE'.

HSA-MIR-23C , HSA-MIR-139 , HSA-MIR-550A-1 , HSA-MIR-149 , HSA-MIR-122 , ...

3 miRs correlated to 'PATHOLOGY.M.STAGE'.

HSA-MIR-3130-1 , HSA-MIR-93 , HSA-MIR-3607

8 miRs correlated to 'GENDER'.

HSA-MIR-26A-2 , HSA-MIR-331 , HSA-MIR-1266 , HSA-MIR-1301 , HSA-MIR-375 , ...

14 miRs correlated to 'RACE'.

HSA-MIR-23C , HSA-MIR-3130-1 , HSA-MIR-532 , HSA-MIR-30D , HSA-MIR-511-2 , ...

No miRs correlated to 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', '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
Time to Death	Cox regression test	N=9	shorter survival	N=7	longer survival	N=2
AGE	Spearman correlation test	N=28	older	N=4	younger	N=24
NEOPLASM DISEASESTAGE	Kruskal-Wallis test	N=3
PATHOLOGY T STAGE	Spearman correlation test	N=24	higher stage	N=17	lower stage	N=7
PATHOLOGY N STAGE	Wilcoxon test	N=0
PATHOLOGY M STAGE	Kruskal-Wallis test	N=3
GENDER	Wilcoxon test	N=8	male	N=8	female	N=0
HISTOLOGICAL TYPE	Kruskal-Wallis test	N=0
COMPLETENESS OF RESECTION	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis test	N=14
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'Time to Death'

9 miRs related to 'Time to Death'.

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


Time to Death	Duration (Months)	0-113 (median=14.6)
	censored	N = 201
	death	N = 90

	Significant markers	N = 9
	associated with shorter survival	7
	associated with longer survival	2

List of 9 miRs differentially expressed by 'Time to Death'

Table S2. Get Full Table List of 9 miRs significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
HSA-MIR-149	1.37	2.425e-05	0.013	0.617
HSA-MIR-632	1.79	7.215e-05	0.039	0.623
HSA-MIR-23C	0.73	0.0001046	0.057	0.401
HSA-MIR-3677	1.4	0.0001069	0.058	0.628
HSA-MIR-139	0.73	0.0001409	0.076	0.345
HSA-MIR-570	1.44	0.0001839	0.099	0.597
HSA-MIR-331	1.58	0.0002592	0.14	0.595
HSA-MIR-489	1.57	0.0003511	0.19	0.62
HSA-MIR-3680	1.52	0.0005426	0.29	0.59

Clinical variable #2: 'AGE'

28 miRs related to 'AGE'.

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


AGE	Mean (SD)	59.61 (13)
	Significant markers	N = 28
	pos. correlated	4
	neg. correlated	24

List of top 10 miRs differentially expressed by 'AGE'

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

	SpearmanCorr	corrP	Q
HSA-MIR-412	-0.2601	5.157e-06	0.00281
HSA-MIR-1269	0.2572	5.18e-06	0.00281
HSA-MIR-181B-1	-0.2479	1.04e-05	0.00564
HSA-MIR-200C	-0.2376	2.44e-05	0.0132
HSA-MIR-296	-0.2433	2.818e-05	0.0152
HSA-MIR-181D	-0.2346	3.109e-05	0.0168
HSA-MIR-889	-0.2305	4.429e-05	0.0238
HSA-MIR-483	-0.2294	5.09e-05	0.0273
HSA-MIR-181A-2	-0.2265	5.877e-05	0.0315
HSA-MIR-30D	-0.2119	0.0001754	0.0938

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

3 miRs related to 'NEOPLASM.DISEASESTAGE'.

Table S5. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	144
	STAGE II	71
	STAGE III	2
	STAGE IIIA	54
	STAGE IIIB	7
	STAGE IIIC	9
	STAGE IV	2
	STAGE IVA	1
	STAGE IVB	2

	Significant markers	N = 3

List of 3 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S6. Get Full Table List of 3 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

	ANOVA_P	Q
HSA-MIR-23C	6.228e-05	0.0339
HSA-MIR-139	0.0002802	0.152
HSA-MIR-550A-1	0.0005332	0.289

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

24 miRs related to 'PATHOLOGY.T.STAGE'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	1.8 (0.92)
		N
	0	1
	1	152
	2	77
	3	67
	4	13

	Significant markers	N = 24
	pos. correlated	17
	neg. correlated	7

List of top 10 miRs differentially expressed by 'PATHOLOGY.T.STAGE'

Table S8. Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
HSA-MIR-23C	-0.314	1.17e-07	6.36e-05
HSA-MIR-139	-0.2907	1.891e-07	0.000103
HSA-MIR-550A-1	0.2712	1.26e-06	0.000683
HSA-MIR-149	0.2689	1.616e-06	0.000874
HSA-MIR-122	-0.2544	5.755e-06	0.00311
HSA-MIR-22	-0.2477	1.019e-05	0.00549
HSA-MIR-194-2	-0.234	3.164e-05	0.017
HSA-MIR-194-1	-0.2331	3.411e-05	0.0183
HSA-MIR-3170	0.2316	4.989e-05	0.0267
HSA-MIR-550A-2	0.2268	5.737e-05	0.0307

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

No miR related to 'PATHOLOGY.N.STAGE'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'


PATHOLOGY.N.STAGE	Labels	N
	class0	222
	class1	4

	Significant markers	N = 0

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

3 miRs related to 'PATHOLOGY.M.STAGE'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'


PATHOLOGY.M.STAGE	Labels	N
	M0	241
	M1	4
	MX	67

	Significant markers	N = 3

List of 3 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S11. Get Full Table List of 3 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

	ANOVA_P	Q
HSA-MIR-3130-1	2.792e-05	0.0152
HSA-MIR-93	0.0001365	0.0741
HSA-MIR-3607	0.0002094	0.114

Clinical variable #7: 'GENDER'

8 miRs related to 'GENDER'.

Table S12. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	100
	MALE	212

	Significant markers	N = 8
	Higher in MALE	8
	Higher in FEMALE	0

List of 8 miRs differentially expressed by 'GENDER'

Table S13. Get Full Table List of 8 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-26A-2	7512	3.296e-05	0.0179	0.6457
HSA-MIR-331	7584	5.01e-05	0.0272	0.6423
HSA-MIR-1266	7748	0.0001553	0.0842	0.6328
HSA-MIR-1301	7801	0.0001676	0.0907	0.632
HSA-MIR-375	7809	0.000175	0.0945	0.6317
HSA-MIR-676	6398	0.0003892	0.21	0.6301
HSA-MIR-3170	7411	0.0004849	0.261	0.6249
HSA-MIR-122	13167	0.0005577	0.3	0.6211

Clinical variable #8: 'HISTOLOGICAL.TYPE'

No miR related to 'HISTOLOGICAL.TYPE'.

Table S14. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	FIBROLAMELLAR CARCINOMA	2
	HEPATOCELLULAR CARCINOMA	304
	HEPATOCHOLANGIOCARCINOMA (MIXED)	6

	Significant markers	N = 0

Clinical variable #9: 'COMPLETENESS.OF.RESECTION'

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

Table S15. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	274
	R1	14
	R2	1
	RX	17

	Significant markers	N = 0

Clinical variable #10: 'RACE'

14 miRs related to 'RACE'.

Table S16. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	AMERICAN INDIAN OR ALASKA NATIVE	1
	ASIAN	144
	BLACK OR AFRICAN AMERICAN	15
	WHITE	143

	Significant markers	N = 14

List of top 10 miRs differentially expressed by 'RACE'

Table S17. Get Full Table List of top 10 miRs differentially expressed by 'RACE'

	ANOVA_P	Q
HSA-MIR-23C	3.272e-13	1.78e-10
HSA-MIR-3130-1	4.754e-09	2.58e-06
HSA-MIR-532	1.82e-06	0.000986
HSA-MIR-30D	3.746e-06	0.00203
HSA-MIR-511-2	2.298e-05	0.0124
HSA-MIR-766	4.885e-05	0.0263
HSA-MIR-511-1	5.374e-05	0.0289
HSA-MIR-1304	9.675e-05	0.052
HSA-MIR-338	0.0001156	0.062
HSA-MIR-30E	0.0001583	0.0847

Clinical variable #11: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S18. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	9
	NOT HISPANIC OR LATINO	287

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = LIHC-TP.miRseq_RPKM_log2.txt
Clinical data file = LIHC-TP.merged_data.txt
Number of patients = 312
Number of miRs = 544
Number of clinical features = 11

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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