Correlation between miRseq expression and clinical features

Breast Invasive Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Breast Invasive Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QJ7F78

Overview

Introduction

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

Summary

Testing the association between 496 genes and 8 clinical features across 852 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

1 gene correlated to 'Time to Death'.

HSA-MIR-874

26 genes correlated to 'AGE'.

HSA-MIR-424 , HSA-MIR-31 , HSA-MIR-598 , HSA-MIR-542 , HSA-MIR-99A , ...

1 gene correlated to 'DISTANT.METASTASIS'.

HSA-MIR-2276

4 genes correlated to 'LYMPH.NODE.METASTASIS'.

HSA-MIR-874 , HSA-MIR-574 , HSA-MIR-197 , HSA-MIR-101-1

8 genes correlated to 'NEOPLASM.DISEASESTAGE'.

HSA-MIR-210 , HSA-MIR-200A , HSA-MIR-143 , HSA-MIR-3653 , HSA-LET-7F-2 , ...

No genes correlated to 'GENDER', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NUMBER.OF.LYMPH.NODES'.

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

Clinical feature	Statistical test	Significant genes	Associated with		Associated with
Time to Death	Cox regression test	N=1	shorter survival	N=1	longer survival	N=0
AGE	Spearman correlation test	N=26	older	N=3	younger	N=23
GENDER	t test	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=0
DISTANT METASTASIS	ANOVA test	N=1
LYMPH NODE METASTASIS	ANOVA test	N=4
NUMBER OF LYMPH NODES	Spearman correlation test	N=0
NEOPLASM DISEASESTAGE	ANOVA test	N=8

Clinical variable #1: 'Time to Death'

One gene related to 'Time to Death'.

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


Time to Death	Duration (Months)	0-223.4 (median=18.2)
	censored	N = 698
	death	N = 95

	Significant markers	N = 1
	associated with shorter survival	1
	associated with longer survival	0

List of one gene significantly associated with 'Time to Death' by Cox regression test

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

	HazardRatio	Wald_P	Q	C_index
HSA-MIR-874	1.55	1.288e-05	0.0064	0.6

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-874 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.29e-05 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

26 genes related to 'AGE'.

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


AGE	Mean (SD)	58.42 (13)
	Significant markers	N = 26
	pos. correlated	3
	neg. correlated	23

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

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

	SpearmanCorr	corrP	Q
HSA-MIR-424	-0.2239	3.926e-11	1.95e-08
HSA-MIR-31	-0.2102	1.281e-09	6.34e-07
HSA-MIR-598	-0.2004	3.677e-09	1.82e-06
HSA-MIR-542	-0.1945	1.063e-08	5.24e-06
HSA-MIR-99A	-0.1921	1.615e-08	7.95e-06
HSA-MIR-381	-0.1897	2.522e-08	1.24e-05
HSA-MIR-652	-0.1704	5.841e-07	0.000286
HSA-LET-7C	-0.1698	6.279e-07	0.000307
HSA-MIR-450B	-0.1624	1.944e-06	0.000949
HSA-MIR-125B-1	-0.1552	5.372e-06	0.00262

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-424 to 'AGE'. P value = 3.93e-11 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	843
	MALE	9

	Significant markers	N = 0

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S6. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	210
	YES	642

	Significant markers	N = 0

Clinical variable #5: 'DISTANT.METASTASIS'

One gene related to 'DISTANT.METASTASIS'.

Table S7. Basic characteristics of clinical feature: 'DISTANT.METASTASIS'


DISTANT.METASTASIS	Labels	N
	CM0 (I+)	2
	M0	634
	M1	8
	MX	72

	Significant markers	N = 1

List of one gene differentially expressed by 'DISTANT.METASTASIS'

Table S8. Get Full Table List of one gene differentially expressed by 'DISTANT.METASTASIS'

	ANOVA_P	Q
HSA-MIR-2276	7.653e-08	3.8e-05

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-2276 to 'DISTANT.METASTASIS'. P value = 7.65e-08 with ANOVA analysis.

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

4 genes related to 'LYMPH.NODE.METASTASIS'.

Table S9. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'


LYMPH.NODE.METASTASIS	Labels	N
	N0	230
	N0 (I+)	13
	N0 (I-)	87
	N0 (MOL+)	1
	N1	85
	N1A	111
	N1B	26
	N1C	2
	N1MI	21
	N2	42
	N2A	46
	N3	13
	N3A	27
	N3B	2
	NX	10

	Significant markers	N = 4

List of 4 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S10. Get Full Table List of 4 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

	ANOVA_P	Q
HSA-MIR-874	9.82e-07	0.000487
HSA-MIR-574	3.282e-05	0.0162
HSA-MIR-197	4.845e-05	0.0239
HSA-MIR-101-1	8.016e-05	0.0395

Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-874 to 'LYMPH.NODE.METASTASIS'. P value = 9.82e-07 with ANOVA analysis.

Clinical variable #7: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S11. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'


NUMBER.OF.LYMPH.NODES	Mean (SD)	2.21 (4.4)
	Significant markers	N = 0

Clinical variable #8: 'NEOPLASM.DISEASESTAGE'

8 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S12. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	71
	STAGE IA	46
	STAGE IB	3
	STAGE II	8
	STAGE IIA	242
	STAGE IIB	162
	STAGE III	2
	STAGE IIIA	105
	STAGE IIIB	21
	STAGE IIIC	35
	STAGE IV	8
	STAGE X	12

	Significant markers	N = 8

List of 8 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S13. Get Full Table List of 8 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

	ANOVA_P	Q
HSA-MIR-210	3.528e-08	1.75e-05
HSA-MIR-200A	2.893e-06	0.00143
HSA-MIR-143	3.094e-06	0.00153
HSA-MIR-3653	2.553e-05	0.0126
HSA-LET-7F-2	5.345e-05	0.0263
HSA-MIR-125B-2	8.064e-05	0.0396
HSA-MIR-3607	8.394e-05	0.0411
HSA-MIR-99A	9.765e-05	0.0478

Figure S5. Get High-res Image As an example, this figure shows the association of HSA-MIR-210 to 'NEOPLASM.DISEASESTAGE'. P value = 3.53e-08 with ANOVA analysis.

Methods & Data

Input

Expresson data file = BRCA-TP.miRseq_RPKM_log2.txt
Clinical data file = BRCA-TP.clin.merged.picked.txt
Number of patients = 852
Number of genes = 496
Number of clinical features = 8

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

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

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[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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