Correlation between miRseq expression and clinical features

Skin Cutaneous Melanoma (Metastatic)

15 January 2014 | analyses__2014_01_15

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/C1RB7324

Overview

Introduction

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

Summary

Testing the association between 598 miRs and 12 clinical features across 251 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one miRs.

6 miRs correlated to 'Time from Specimen Diagnosis to Death'.

HSA-MIR-1976 , HSA-MIR-155 , HSA-MIR-342 , HSA-MIR-625 , HSA-MIR-146B , ...

12 miRs correlated to 'PRIMARY.SITE.OF.DISEASE'.

HSA-MIR-150 , HSA-MIR-342 , HSA-MIR-205 , HSA-MIR-194-1 , HSA-MIR-194-2 , ...

1 miR correlated to 'PATHOLOGY.T.STAGE'.

HSA-MIR-1537

1 miR correlated to 'BRESLOW.THICKNESS'.

HSA-MIR-1537

No miRs correlated to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'MELANOMA.ULCERATION', 'MELANOMA.PRIMARY.KNOWN', and 'GENDER'.

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 Q value < 0.05.

Clinical feature	Statistical test	Significant miRs	Associated with		Associated with
Time from Specimen Diagnosis to Death	Cox regression test	N=6	shorter survival	N=0	longer survival	N=6
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=0
PRIMARY SITE OF DISEASE	ANOVA test	N=12
NEOPLASM DISEASESTAGE	ANOVA test	N=0
PATHOLOGY T STAGE	Spearman correlation test	N=1	higher stage	N=1	lower stage	N=0
PATHOLOGY N STAGE	Spearman correlation test	N=0
PATHOLOGY M STAGE	ANOVA test	N=0
MELANOMA ULCERATION	t test	N=0
MELANOMA PRIMARY KNOWN	t test	N=0
BRESLOW THICKNESS	Spearman correlation test	N=1	higher breslow.thickness	N=1	lower breslow.thickness	N=0
GENDER	t test	N=0

Clinical variable #1: 'Time from Specimen Diagnosis to Death'

6 miRs related to 'Time from Specimen Diagnosis to Death'.

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


Time from Specimen Diagnosis to Death	Duration (Months)	0.1-124.3 (median=14.2)
	censored	N = 120
	death	N = 119

	Significant markers	N = 6
	associated with shorter survival	0
	associated with longer survival	6

List of 6 miRs significantly associated with 'Time from Specimen Diagnosis to Death' by Cox regression test

Table S2. Get Full Table List of 6 miRs significantly associated with 'Time from Specimen Diagnosis to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
HSA-MIR-1976	0.63	6.68e-08	4e-05	0.349
HSA-MIR-155	0.78	7.451e-08	4.4e-05	0.342
HSA-MIR-342	0.71	4.393e-07	0.00026	0.343
HSA-MIR-625	0.68	3.613e-06	0.0021	0.346
HSA-MIR-146B	0.73	3.209e-05	0.019	0.397
HSA-MIR-150	0.86	4.232e-05	0.025	0.37

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-1976 to 'Time from Specimen Diagnosis to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 6.68e-08 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'Time to Death'

No miR related to 'Time to Death'.

Table S3. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	0.2-357.4 (median=47.8)
	censored	N = 124
	death	N = 121

	Significant markers	N = 0

Clinical variable #3: 'AGE'

No miR related to 'AGE'.

Table S4. Basic characteristics of clinical feature: 'AGE'


AGE	Mean (SD)	55.64 (16)
	Significant markers	N = 0

Clinical variable #4: 'PRIMARY.SITE.OF.DISEASE'

12 miRs related to 'PRIMARY.SITE.OF.DISEASE'.

Table S5. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'


PRIMARY.SITE.OF.DISEASE	Labels	N
	DISTANT METASTASIS	32
	PRIMARY TUMOR	1
	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	53
	REGIONAL LYMPH NODE	164

	Significant markers	N = 12

List of top 10 miRs differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S6. Get Full Table List of top 10 miRs differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

	ANOVA_P	Q
HSA-MIR-150	7.965e-07	0.000476
HSA-MIR-342	1.157e-06	0.000691
HSA-MIR-205	1.062e-05	0.00633
HSA-MIR-194-1	1.182e-05	0.00703
HSA-MIR-194-2	2.271e-05	0.0135
HSA-MIR-410	2.73e-05	0.0162
HSA-MIR-136	3.576e-05	0.0212
HSA-MIR-379	4.327e-05	0.0256
HSA-MIR-369	4.447e-05	0.0262
HSA-MIR-217	5.043e-05	0.0297

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-150 to 'PRIMARY.SITE.OF.DISEASE'. P value = 7.96e-07 with ANOVA analysis.

Clinical variable #5: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S7. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	I OR II NOS	10
	STAGE 0	5
	STAGE I	21
	STAGE IA	10
	STAGE IB	24
	STAGE II	14
	STAGE IIA	9
	STAGE IIB	13
	STAGE IIC	9
	STAGE III	28
	STAGE IIIA	11
	STAGE IIIB	22
	STAGE IIIC	40
	STAGE IV	12

	Significant markers	N = 0

Clinical variable #6: 'PATHOLOGY.T.STAGE'

One miR related to 'PATHOLOGY.T.STAGE'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	2.37 (1.3)
		N
	0	21
	1	29
	2	57
	3	46
	4	50

	Significant markers	N = 1
	pos. correlated	1
	neg. correlated	0

List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S9. Get Full Table List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
HSA-MIR-1537	0.3153	3.921e-05	0.0234

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-1537 to 'PATHOLOGY.T.STAGE'. P value = 3.92e-05 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N.STAGE'

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

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


PATHOLOGY.N.STAGE	Mean (SD)	0.83 (1.1)
		N
	0	130
	1	45
	2	31
	3	30

	Significant markers	N = 0

Clinical variable #8: 'PATHOLOGY.M.STAGE'

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

Table S11. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'


PATHOLOGY.M.STAGE	Labels	N
	M0	226
	M1	4
	M1A	2
	M1B	2
	M1C	5

	Significant markers	N = 0

Clinical variable #9: 'MELANOMA.ULCERATION'

No miR related to 'MELANOMA.ULCERATION'.

Table S12. Basic characteristics of clinical feature: 'MELANOMA.ULCERATION'


MELANOMA.ULCERATION	Labels	N
	NO	96
	YES	62

	Significant markers	N = 0

Clinical variable #10: 'MELANOMA.PRIMARY.KNOWN'

No miR related to 'MELANOMA.PRIMARY.KNOWN'.

Table S13. Basic characteristics of clinical feature: 'MELANOMA.PRIMARY.KNOWN'


MELANOMA.PRIMARY.KNOWN	Labels	N
	NO	31
	YES	220

	Significant markers	N = 0

Clinical variable #11: 'BRESLOW.THICKNESS'

One miR related to 'BRESLOW.THICKNESS'.

Table S14. Basic characteristics of clinical feature: 'BRESLOW.THICKNESS'


BRESLOW.THICKNESS	Mean (SD)	3.55 (5.1)
	Significant markers	N = 1
	pos. correlated	1
	neg. correlated	0

List of one miR significantly correlated to 'BRESLOW.THICKNESS' by Spearman correlation test

Table S15. Get Full Table List of one miR significantly correlated to 'BRESLOW.THICKNESS' by Spearman correlation test

	SpearmanCorr	corrP	Q
HSA-MIR-1537	0.3998	3.347e-07	2e-04

Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-1537 to 'BRESLOW.THICKNESS'. P value = 3.35e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #12: 'GENDER'

No miR related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	95
	MALE	156

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = SKCM-TM.miRseq_RPKM_log2.txt
Clinical data file = SKCM-TM.merged_data.txt
Number of patients = 251
Number of miRs = 598
Number of clinical features = 12

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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