Correlation between miRseq expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1RN36HS
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/C1RN36HS
Overview
Introduction

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

Summary

Testing the association between 468 miRs and 13 clinical features across 202 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one miRs.

  • 5 miRs correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-30A ,  HSA-MIR-3676 ,  HSA-MIR-486 ,  HSA-MIR-451 ,  HSA-MIR-30C-2

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

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

  • 6 miRs correlated to 'GLEASON_SCORE_COMBINED'.

    • HSA-MIR-592 ,  HSA-MIR-30A ,  HSA-MIR-222 ,  HSA-MIR-221 ,  HSA-MIR-1-2 ,  ...

  • 9 miRs correlated to 'GLEASON_SCORE_PRIMARY'.

    • HSA-MIR-221 ,  HSA-MIR-222 ,  HSA-MIR-378C ,  HSA-MIR-1-2 ,  HSA-MIR-3687 ,  ...

  • 6 miRs correlated to 'GLEASON_SCORE'.

    • HSA-MIR-221 ,  HSA-MIR-592 ,  HSA-MIR-222 ,  HSA-MIR-30A ,  HSA-MIR-217 ,  ...

  • 1 miR correlated to 'PSA_RESULT_PREOP'.

    • HSA-MIR-15B

  • 72 miRs correlated to 'DAYS_TO_PREOP_PSA'.

    • HSA-MIR-660 ,  HSA-MIR-362 ,  HSA-MIR-1247 ,  HSA-MIR-130A ,  HSA-MIR-181A-1 ,  ...

  • 1 miR correlated to 'PSA_VALUE'.

    • HSA-MIR-450A-1

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

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=5 higher stage N=0 lower stage N=5
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=6 higher score N=1 lower score N=5
GLEASON_SCORE_PRIMARY Spearman correlation test N=9 higher score N=1 lower score N=8
GLEASON_SCORE_SECONDARY Spearman correlation test   N=0        
GLEASON_SCORE Spearman correlation test N=6 higher score N=2 lower score N=4
PSA_RESULT_PREOP Spearman correlation test N=1 higher psa_result_preop N=1 lower psa_result_preop N=0
DAYS_TO_PREOP_PSA Spearman correlation test N=72 higher days_to_preop_psa N=3 lower days_to_preop_psa N=69
PSA_VALUE Spearman correlation test N=1 higher psa_value N=0 lower psa_value N=1
DAYS_TO_PSA Spearman correlation test   N=0        
Clinical variable #1: 'AGE'

No miR related to 'AGE'.

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

AGE Mean (SD) 60.28 (6.9)
  Significant markers N = 0
Clinical variable #2: 'PATHOLOGY.T.STAGE'

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

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

PATHOLOGY.T.STAGE Mean (SD) 2.57 (0.54)
  N
  2 90
  3 106
  4 4
     
  Significant markers N = 5
  pos. correlated 0
  neg. correlated 5
List of 5 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S3.  Get Full Table List of 5 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-30A -0.3315 1.623e-06 0.000759
HSA-MIR-3676 -0.3093 8.779e-06 0.0041
HSA-MIR-486 -0.2966 2.005e-05 0.00934
HSA-MIR-451 -0.2865 3.915e-05 0.0182
HSA-MIR-30C-2 -0.2747 8.266e-05 0.0384

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-30A to 'PATHOLOGY.T.STAGE'. P value = 1.62e-06 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 155
  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 151
  R1 36
  RX 5
     
  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) 0.17 (0.67)
  Value N
  0 153
  1 10
  2 2
  3 3
  6 1
     
  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 -0.3056 5.322e-05 0.0249
HSA-MIR-222 -0.3017 6.705e-05 0.0313
HSA-MIR-221 -0.2967 8.951e-05 0.0417

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 = 5.32e-05 with Spearman correlation analysis.

Clinical variable #6: 'GLEASON_SCORE_COMBINED'

6 miRs related to 'GLEASON_SCORE_COMBINED'.

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

GLEASON_SCORE_COMBINED Mean (SD) 7.22 (0.75)
  Significant markers N = 6
  pos. correlated 1
  neg. correlated 5
List of 6 miRs significantly correlated to 'GLEASON_SCORE_COMBINED' by Spearman correlation test

Table S9.  Get Full Table List of 6 miRs significantly correlated to 'GLEASON_SCORE_COMBINED' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-592 0.3342 8.444e-06 0.00395
HSA-MIR-30A -0.2952 1.999e-05 0.00933
HSA-MIR-222 -0.2896 2.906e-05 0.0135
HSA-MIR-221 -0.2893 2.977e-05 0.0138
HSA-MIR-1-2 -0.2828 4.551e-05 0.0211
HSA-MIR-3676 -0.2709 0.0001004 0.0465

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-592 to 'GLEASON_SCORE_COMBINED'. P value = 8.44e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #7: 'GLEASON_SCORE_PRIMARY'

9 miRs related to 'GLEASON_SCORE_PRIMARY'.

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

GLEASON_SCORE_PRIMARY Mean (SD) 3.46 (0.57)
  Score N
  2 1
  3 113
  4 82
  5 6
     
  Significant markers N = 9
  pos. correlated 1
  neg. correlated 8
List of 9 miRs significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

Table S11.  Get Full Table List of 9 miRs significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-221 -0.3675 7.452e-08 3.49e-05
HSA-MIR-222 -0.3366 9.667e-07 0.000451
HSA-MIR-378C -0.3118 6.269e-06 0.00292
HSA-MIR-1-2 -0.3071 8.784e-06 0.00408
HSA-MIR-3687 0.3148 1.208e-05 0.00561
HSA-MIR-205 -0.2915 2.565e-05 0.0119
HSA-MIR-184 -0.3075 4.302e-05 0.0199
HSA-MIR-139 -0.2783 6.05e-05 0.0279
HSA-MIR-133A-1 -0.2726 8.692e-05 0.04

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-221 to 'GLEASON_SCORE_PRIMARY'. P value = 7.45e-08 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) 3.76 (0.61)
  Score N
  3 67
  4 116
  5 19
     
  Significant markers N = 0
Clinical variable #9: 'GLEASON_SCORE'

6 miRs related to 'GLEASON_SCORE'.

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

GLEASON_SCORE Mean (SD) 7.27 (0.78)
  Score N
  6 15
  7 142
  8 21
  9 23
  10 1
     
  Significant markers N = 6
  pos. correlated 2
  neg. correlated 4
List of 6 miRs significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

Table S14.  Get Full Table List of 6 miRs significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-221 -0.3091 7.616e-06 0.00356
HSA-MIR-592 0.3339 8.607e-06 0.00402
HSA-MIR-222 -0.3069 8.869e-06 0.00413
HSA-MIR-30A -0.3058 9.599e-06 0.00446
HSA-MIR-217 0.284 4.197e-05 0.0195
HSA-MIR-3676 -0.2787 6.172e-05 0.0286

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-221 to 'GLEASON_SCORE'. P value = 7.62e-06 with Spearman correlation analysis.

Clinical variable #10: 'PSA_RESULT_PREOP'

One miR related to 'PSA_RESULT_PREOP'.

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

PSA_RESULT_PREOP Mean (SD) 10.24 (10)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one miR significantly correlated to 'PSA_RESULT_PREOP' by Spearman correlation test

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

SpearmanCorr corrP Q
HSA-MIR-15B 0.2996 1.632e-05 0.00764

Figure S6.  Get High-res Image As an example, this figure shows the association of HSA-MIR-15B to 'PSA_RESULT_PREOP'. P value = 1.63e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #11: 'DAYS_TO_PREOP_PSA'

72 miRs related to 'DAYS_TO_PREOP_PSA'.

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

DAYS_TO_PREOP_PSA Mean (SD) -4.55 (97)
  Significant markers N = 72
  pos. correlated 3
  neg. correlated 69
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 -0.4264 4.609e-10 2.16e-07
HSA-MIR-362 -0.3948 1.023e-08 4.78e-06
HSA-MIR-1247 0.387 2.101e-08 9.79e-06
HSA-MIR-130A -0.3832 2.962e-08 1.38e-05
HSA-MIR-181A-1 -0.3798 4.009e-08 1.86e-05
HSA-MIR-199A-1 -0.3691 1.021e-07 4.73e-05
HSA-MIR-19A -0.3681 1.11e-07 5.13e-05
HSA-MIR-181C -0.3662 1.298e-07 5.98e-05
HSA-MIR-532 -0.3662 1.298e-07 5.98e-05
HSA-MIR-500B -0.3664 1.489e-07 6.83e-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 = 4.61e-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) 1.36 (4.3)
  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-450A-1 -0.3024 6.452e-05 0.0302

Figure S8.  Get High-res Image As an example, this figure shows the association of HSA-MIR-450A-1 to 'PSA_VALUE'. P value = 6.45e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #13: 'DAYS_TO_PSA'

No miR related to 'DAYS_TO_PSA'.

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

DAYS_TO_PSA Mean (SD) 576.88 (550)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 202

  • Number of miRs = 468

  • 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.

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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