Correlation between miRseq expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1H993N0
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/C1H993N0
Overview
Introduction

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

Summary

Testing the association between 474 miRs and 13 clinical features across 179 samples, statistically thresholded by Q value < 0.05, 9 clinical features related to at least one miRs.

- 3 miRs correlated to 'PATHOLOGY.T.STAGE'.

- HSA-MIR-30A , HSA-MIR-486 , HSA-MIR-3676

- 3 miRs correlated to 'NUMBER.OF.LYMPH.NODES'.

- HSA-MIR-378 , HSA-MIR-221 , HSA-MIR-222

- 1 miR correlated to 'GLEASON_SCORE_COMBINED'.

- HSA-MIR-217

- 3 miRs correlated to 'GLEASON_SCORE_PRIMARY'.

- HSA-MIR-221 , HSA-MIR-378C , HSA-MIR-222

- 1 miR correlated to 'GLEASON_SCORE'.

- HSA-MIR-217

- 3 miRs correlated to 'PSA_RESULT_PREOP'.

- HSA-MIR-15B , HSA-MIR-182 , HSA-MIR-183

- 81 miRs correlated to 'DAYS_TO_PREOP_PSA'.

- HSA-MIR-660 , HSA-MIR-181C , HSA-MIR-181A-1 , HSA-MIR-362 , HSA-MIR-130A , ...

- 1 miR correlated to 'PSA_VALUE'.

- HSA-MIR-133B

- 1 miR correlated to 'DAYS_TO_PSA'.

- HSA-MIR-21

- No miRs correlated to 'AGE', 'PATHOLOGY.N.STAGE', 'COMPLETENESS.OF.RESECTION', and 'GLEASON_SCORE_SECONDARY'.

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 |
--- | --- | --- | --- | --- | --- | ---
AGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=3 | higher stage | N=0 | lower stage | N=3
PATHOLOGY N STAGE | t test | N=0 |  |  |  |
COMPLETENESS OF RESECTION | ANOVA test | N=0 |  |  |  |
NUMBER OF LYMPH NODES | Spearman correlation test | N=3 | higher number.of.lymph.nodes | N=0 | lower number.of.lymph.nodes | N=3
GLEASON_SCORE_COMBINED | Spearman correlation test | N=1 | higher score | N=1 | lower score | N=0
GLEASON_SCORE_PRIMARY | Spearman correlation test | N=3 | higher score | N=0 | lower score | N=3
GLEASON_SCORE_SECONDARY | Spearman correlation test | N=0 |  |  |  |
GLEASON_SCORE | Spearman correlation test | N=1 | higher score | N=1 | lower score | N=0
PSA_RESULT_PREOP | Spearman correlation test | N=3 | higher psa_result_preop | N=3 | lower psa_result_preop | N=0
DAYS_TO_PREOP_PSA | Spearman correlation test | N=81 | higher days_to_preop_psa | N=3 | lower days_to_preop_psa | N=78
PSA_VALUE | Spearman correlation test | N=1 | higher psa_value | N=0 | lower psa_value | N=1
DAYS_TO_PSA | Spearman correlation test | N=1 | higher days_to_psa | N=0 | lower days_to_psa | N=1

Clinical variable #1: 'AGE'

No miR related to 'AGE'.

Table S1. Basic characteristics of clinical feature: 'AGE'

AGE | Mean (SD) | 60.22 (7)
| Significant markers | N = 0

Clinical variable #2: 'PATHOLOGY.T.STAGE'

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

Table S2. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 2.59 (0.54)
|  | N
| 2 | 77
| 3 | 96
| 4 | 4
| Significant markers | N = 3
| pos. correlated | 0
| neg. correlated | 3

List of 3 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S3. Get Full Table List of 3 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-30A | -0.3153 | 1.915e-05 | 0.00908
HSA-MIR-486 | -0.3083 | 2.99e-05 | 0.0141
HSA-MIR-3676 | -0.2998 | 5.292e-05 | 0.025

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-30A to 'PATHOLOGY.T.STAGE'. P value = 1.92e-05 with Spearman correlation analysis.

Clinical variable #3: 'PATHOLOGY.N.STAGE'

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

Table S4. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Labels | N
| class0 | 143
| class1 | 16
| Significant markers | N = 0

Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

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

Table S5. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 132
| R1 | 34
| RX | 3
| Significant markers | N = 0

Clinical variable #5: 'NUMBER.OF.LYMPH.NODES'

3 miRs related to 'NUMBER.OF.LYMPH.NODES'.

Table S6. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES | Mean (SD) | 0.18 (0.7)
| Significant markers | N = 3
| pos. correlated | 0
| neg. correlated | 3

List of 3 miRs significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S7. Get Full Table List of 3 miRs significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-378 | -0.3394 | 1.374e-05 | 0.00651
HSA-MIR-221 | -0.3095 | 7.993e-05 | 0.0378
HSA-MIR-222 | -0.3087 | 8.389e-05 | 0.0396

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-378 to 'NUMBER.OF.LYMPH.NODES'. P value = 1.37e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #6: 'GLEASON_SCORE_COMBINED'

One miR related to 'GLEASON_SCORE_COMBINED'.

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

GLEASON_SCORE_COMBINED | Mean (SD) | 7.26 (0.75)
| Score | N
| 6 | 11
| 7 | 132
| 8 | 16
| 9 | 19
| 10 | 1
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one miR significantly correlated to 'GLEASON_SCORE_COMBINED' by Spearman correlation test

Table S9. Get Full Table List of one miR significantly correlated to 'GLEASON_SCORE_COMBINED' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-217 | 0.3131 | 1.975e-05 | 0.00936

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-217 to 'GLEASON_SCORE_COMBINED'. P value = 1.98e-05 with Spearman correlation analysis.

Clinical variable #7: 'GLEASON_SCORE_PRIMARY'

3 miRs related to 'GLEASON_SCORE_PRIMARY'.

Table S10. Basic characteristics of clinical feature: 'GLEASON_SCORE_PRIMARY'

GLEASON_SCORE_PRIMARY | Mean (SD) | 3.47 (0.56)
| Score | N
| 2 | 1
| 3 | 98
| 4 | 75
| 5 | 5
| Significant markers | N = 3
| pos. correlated | 0
| neg. correlated | 3

List of 3 miRs significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

Table S11. Get Full Table List of 3 miRs significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-221 | -0.3351 | 4.546e-06 | 0.00215
HSA-MIR-378C | -0.2884 | 9.015e-05 | 0.0426
HSA-MIR-222 | -0.2884 | 9.062e-05 | 0.0428

Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-221 to 'GLEASON_SCORE_PRIMARY'. P value = 4.55e-06 with Spearman correlation analysis.

Clinical variable #8: 'GLEASON_SCORE_SECONDARY'

No miR related to 'GLEASON_SCORE_SECONDARY'.

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

GLEASON_SCORE_SECONDARY | Mean (SD) | 3.79 (0.61)
| Score | N
| 3 | 56
| 4 | 105
| 5 | 18
| Significant markers | N = 0

Clinical variable #9: 'GLEASON_SCORE'

One miR related to 'GLEASON_SCORE'.

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

GLEASON_SCORE | Mean (SD) | 7.3 (0.79)
| Score | N
| 6 | 11
| 7 | 127
| 8 | 18
| 9 | 22
| 10 | 1
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one miR significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

Table S14. Get Full Table List of one miR significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-217 | 0.3574 | 9.026e-07 | 0.000428

Figure S5. Get High-res Image As an example, this figure shows the association of HSA-MIR-217 to 'GLEASON_SCORE'. P value = 9.03e-07 with Spearman correlation analysis.

Clinical variable #10: 'PSA_RESULT_PREOP'

3 miRs related to 'PSA_RESULT_PREOP'.

Table S15. Basic characteristics of clinical feature: 'PSA_RESULT_PREOP'

PSA_RESULT_PREOP | Mean (SD) | 10.36 (10)
| Significant markers | N = 3
| pos. correlated | 3
| neg. correlated | 0

List of 3 miRs significantly correlated to 'PSA_RESULT_PREOP' by Spearman correlation test

Table S16. Get Full Table List of 3 miRs significantly correlated to 'PSA_RESULT_PREOP' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-15B | 0.3075 | 3.138e-05 | 0.0149
HSA-MIR-182 | 0.2923 | 7.889e-05 | 0.0373
HSA-MIR-183 | 0.2883 | 9.962e-05 | 0.047

Figure S6. Get High-res Image As an example, this figure shows the association of HSA-MIR-15B to 'PSA_RESULT_PREOP'. P value = 3.14e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #11: 'DAYS_TO_PREOP_PSA'

81 miRs related to 'DAYS_TO_PREOP_PSA'.

Table S17. Basic characteristics of clinical feature: 'DAYS_TO_PREOP_PSA'

DAYS_TO_PREOP_PSA | Mean (SD) | -2.95 (100)
| Significant markers | N = 81
| pos. correlated | 3
| neg. correlated | 78

List of top 10 miRs significantly correlated to 'DAYS_TO_PREOP_PSA' by Spearman correlation test

Table S18. Get Full Table List of top 10 miRs significantly correlated to 'DAYS_TO_PREOP_PSA' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-660 | -0.4554 | 2.719e-10 | 1.29e-07
HSA-MIR-181C | -0.4229 | 6.117e-09 | 2.89e-06
HSA-MIR-181A-1 | -0.4092 | 2.076e-08 | 9.8e-06
HSA-MIR-362 | -0.4088 | 2.137e-08 | 1.01e-05
HSA-MIR-130A | -0.4045 | 3.103e-08 | 1.46e-05
HSA-MIR-1247 | 0.402 | 3.83e-08 | 1.8e-05
HSA-MIR-19A | -0.4013 | 4.068e-08 | 1.9e-05
HSA-MIR-199A-1 | -0.3984 | 5.198e-08 | 2.43e-05
HSA-MIR-532 | -0.3981 | 5.332e-08 | 2.48e-05
HSA-MIR-125B-1 | -0.3979 | 5.406e-08 | 2.51e-05

Figure S7. Get High-res Image As an example, this figure shows the association of HSA-MIR-660 to 'DAYS_TO_PREOP_PSA'. P value = 2.72e-10 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #12: 'PSA_VALUE'

One miR related to 'PSA_VALUE'.

Table S19. Basic characteristics of clinical feature: 'PSA_VALUE'

PSA_VALUE | Mean (SD) | 1.47 (4.5)
| Significant markers | N = 1
| pos. correlated | 0
| neg. correlated | 1

List of one miR significantly correlated to 'PSA_VALUE' by Spearman correlation test

Table S20. Get Full Table List of one miR significantly correlated to 'PSA_VALUE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-133B | -0.3 | 0.0001052 | 0.0499

Figure S8. Get High-res Image As an example, this figure shows the association of HSA-MIR-133B to 'PSA_VALUE'. P value = 0.000105 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #13: 'DAYS_TO_PSA'

One miR related to 'DAYS_TO_PSA'.

Table S21. Basic characteristics of clinical feature: 'DAYS_TO_PSA'

DAYS_TO_PSA | Mean (SD) | 540.11 (500)
| Significant markers | N = 1
| pos. correlated | 0
| neg. correlated | 1

List of one miR significantly correlated to 'DAYS_TO_PSA' by Spearman correlation test

Table S22. Get Full Table List of one miR significantly correlated to 'DAYS_TO_PSA' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-21 | -0.3043 | 8.258e-05 | 0.0391

Figure S9. Get High-res Image As an example, this figure shows the association of HSA-MIR-21 to 'DAYS_TO_PSA'. P value = 8.26e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

- Expresson data file = PRAD-TP.miRseq_RPKM_log2.txt

- Clinical data file = PRAD-TP.merged_data.txt

- Number of patients = 179

- Number of miRs = 474

- Number of clinical features = 13

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

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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