Correlation between miRseq expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C10001K3
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C10001K3
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features. The input file " PRAD-TP.miRseq_RPKM_log2.txt " is generated in the pipeline miRseq_Preprocess in the stddata run.

Summary

Testing the association between 469 miRs and 11 clinical features across 494 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one miRs.

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-627 ,  HSA-MIR-132 ,  HSA-MIR-425 ,  HSA-MIR-26B ,  HSA-MIR-96 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-30A ,  HSA-MIR-133A-1 ,  HSA-MIR-222 ,  HSA-MIR-3676 ,  HSA-MIR-221 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-133A-2 ,  HSA-MIR-21 ,  HSA-MIR-1-2 ,  HSA-MIR-133B ,  HSA-MIR-217 ,  ...

  • 30 miRs correlated to 'RADIATION_THERAPY'.

    • HSA-MIR-21 ,  HSA-MIR-1468 ,  HSA-MIR-378C ,  HSA-MIR-184 ,  HSA-MIR-133B ,  ...

  • 12 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-423 ,  HSA-MIR-152 ,  HSA-MIR-182 ,  HSA-MIR-183 ,  HSA-MIR-326 ,  ...

  • 30 miRs correlated to 'RESIDUAL_TUMOR'.

    • HSA-MIR-425 ,  HSA-MIR-1274B ,  HSA-MIR-891A ,  HSA-MIR-301A ,  HSA-MIR-98 ,  ...

  • 30 miRs correlated to 'NUMBER_OF_LYMPH_NODES'.

    • HSA-MIR-133A-2 ,  HSA-MIR-21 ,  HSA-MIR-133B ,  HSA-MIR-1-2 ,  HSA-MIR-139 ,  ...

  • 30 miRs correlated to 'GLEASON_SCORE'.

    • HSA-MIR-217 ,  HSA-MIR-133B ,  HSA-MIR-133A-2 ,  HSA-MIR-21 ,  HSA-MIR-1-2 ,  ...

  • 30 miRs correlated to 'PSA_VALUE'.

    • HSA-MIR-135A-1 ,  HSA-MIR-582 ,  HSA-MIR-330 ,  HSA-MIR-155 ,  HSA-MIR-133A-1 ,  ...

  • No miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', and 'RACE'.

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
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=18 younger N=12
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=7 lower stage N=23
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=30 n0 N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Wilcoxon test N=12 prostate adenocarcinoma, other subtype N=12 prostate adenocarcinoma acinar type N=0
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=14 lower number_of_lymph_nodes N=16
GLEASON_SCORE Spearman correlation test N=30 higher score N=13 lower score N=17
PSA_VALUE Spearman correlation test N=30 higher psa_value N=12 lower psa_value N=18
RACE Kruskal-Wallis test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No miR related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0.8-165.2 (median=30.4)
  censored N = 483
  death N = 10
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

30 miRs related to 'YEARS_TO_BIRTH'.

Table S2.  Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH Mean (SD) 61.02 (6.8)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
List of top 10 miRs differentially expressed by 'YEARS_TO_BIRTH'

Table S3.  Get Full Table List of top 10 miRs significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-627 0.1966 2.348e-05 0.011
HSA-MIR-132 -0.1545 0.0006566 0.0963
HSA-MIR-425 0.1538 0.0006968 0.0963
HSA-MIR-26B 0.149 0.001024 0.0963
HSA-MIR-96 0.1489 0.001026 0.0963
HSA-MIR-34A 0.1451 0.001388 0.109
HSA-MIR-181D -0.1367 0.002602 0.174
HSA-LET-7B -0.1301 0.004179 0.218
HSA-MIR-335 0.128 0.004845 0.218
HSA-MIR-101-2 0.1273 0.005091 0.218
Clinical variable #3: 'PATHOLOGY_T_STAGE'

30 miRs related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.63 (0.52)
  N
  T2 187
  T3 291
  T4 9
     
  Significant markers N = 30
  pos. correlated 7
  neg. correlated 23
List of top 10 miRs differentially expressed by 'PATHOLOGY_T_STAGE'

Table S5.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-30A -0.298 1.904e-11 5.86e-09
HSA-MIR-133A-1 -0.2963 2.499e-11 5.86e-09
HSA-MIR-222 -0.2702 1.351e-09 2.11e-07
HSA-MIR-3676 -0.265 4.02e-09 4.71e-07
HSA-MIR-221 -0.2455 4.053e-08 3.8e-06
HSA-MIR-217 0.2386 9.907e-08 6.69e-06
HSA-MIR-582 -0.2385 9.984e-08 6.69e-06
HSA-MIR-133B -0.2371 1.27e-07 7.45e-06
HSA-MIR-1468 -0.2289 3.302e-07 1.72e-05
HSA-MIR-574 -0.2273 3.963e-07 1.86e-05
Clinical variable #4: 'PATHOLOGY_N_STAGE'

30 miRs related to 'PATHOLOGY_N_STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Labels N
  N0 344
  N1 78
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

Table S7.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
HSA-MIR-133A-2 8595 1.9e-06 0.000624 0.673
HSA-MIR-21 17983 2.66e-06 0.000624 0.6702
HSA-MIR-1-2 8986 5.249e-06 0.000821 0.6651
HSA-MIR-133B 9046 8.024e-06 0.000941 0.6619
HSA-MIR-217 17699 1.066e-05 0.000999 0.6596
HSA-MIR-184 5373 1.66e-05 0.0013 0.6782
HSA-MIR-139 9312 2.45e-05 0.00164 0.653
HSA-MIR-3676 9112 4.019e-05 0.00218 0.6499
HSA-MIR-133A-1 9431 4.185e-05 0.00218 0.6485
HSA-MIR-378 9576 7.883e-05 0.0037 0.6431
Clinical variable #5: 'RADIATION_THERAPY'

30 miRs related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 392
  YES 59
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

Table S9.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-21 16049 1.55e-06 0.000727 0.6939
HSA-MIR-1468 7479 1.208e-05 0.00283 0.6766
HSA-MIR-378C 7797 5.45e-05 0.00852 0.6629
HSA-MIR-184 5172 7.656e-05 0.00898 0.6752
HSA-MIR-133B 7774 0.0001215 0.00973 0.6563
HSA-MIR-30A 7982 0.0001244 0.00973 0.6549
HSA-MIR-708 15108 0.0001467 0.00983 0.6532
HSA-MIR-139 8303 0.0004771 0.0251 0.641
HSA-MIR-574 8346 0.0005664 0.0251 0.6391
HSA-MIR-133A-2 7898 0.0005848 0.0251 0.641
Clinical variable #6: 'HISTOLOGICAL_TYPE'

12 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PROSTATE ADENOCARCINOMA ACINAR TYPE 479
  PROSTATE ADENOCARCINOMA, OTHER SUBTYPE 15
     
  Significant markers N = 12
  Higher in PROSTATE ADENOCARCINOMA, OTHER SUBTYPE 12
  Higher in PROSTATE ADENOCARCINOMA ACINAR TYPE 0
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Clinical variable #7: 'RESIDUAL_TUMOR'

30 miRs related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 313
  R1 146
  R2 5
  RX 15
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RESIDUAL_TUMOR'

Table S13.  Get Full Table List of top 10 miRs differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
HSA-MIR-425 1.218e-05 0.00571
HSA-MIR-1274B 0.0001855 0.0435
HSA-MIR-891A 0.0003264 0.051
HSA-MIR-301A 0.0006238 0.0623
HSA-MIR-98 0.0007229 0.0623
HSA-MIR-96 0.001044 0.0623
HSA-MIR-653 0.001069 0.0623
HSA-MIR-378C 0.001134 0.0623
HSA-MIR-708 0.001493 0.0623
HSA-MIR-133B 0.001835 0.0623
Clinical variable #8: 'NUMBER_OF_LYMPH_NODES'

30 miRs related to 'NUMBER_OF_LYMPH_NODES'.

Table S14.  Basic characteristics of clinical feature: 'NUMBER_OF_LYMPH_NODES'

NUMBER_OF_LYMPH_NODES Mean (SD) 0.45 (1.4)
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
List of top 10 miRs differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S15.  Get Full Table List of top 10 miRs significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-133A-2 -0.2577 1.929e-07 9.05e-05
HSA-MIR-21 0.2491 3.945e-07 9.25e-05
HSA-MIR-133B -0.2425 8.322e-07 0.000114
HSA-MIR-1-2 -0.2408 9.71e-07 0.000114
HSA-MIR-139 -0.2273 3.938e-06 0.000369
HSA-MIR-217 0.224 5.455e-06 0.000426
HSA-MIR-133A-1 -0.2222 6.547e-06 0.000439
HSA-MIR-221 -0.219 8.883e-06 0.000521
HSA-MIR-184 -0.2405 1.093e-05 0.00057
HSA-MIR-708 0.208 2.5e-05 0.00117
Clinical variable #9: 'GLEASON_SCORE'

30 miRs related to 'GLEASON_SCORE'.

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

GLEASON_SCORE Mean (SD) 7.61 (1)
  Score N
  6 45
  7 246
  8 64
  9 136
  10 3
     
  Significant markers N = 30
  pos. correlated 13
  neg. correlated 17
List of top 10 miRs differentially expressed by 'GLEASON_SCORE'

Table S17.  Get Full Table List of top 10 miRs significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-217 0.3935 1.058e-19 4.96e-17
HSA-MIR-133B -0.3712 1.739e-17 4.08e-15
HSA-MIR-133A-2 -0.3653 9.23e-17 1.44e-14
HSA-MIR-21 0.3534 5.612e-16 6.58e-14
HSA-MIR-1-2 -0.3522 7.104e-16 6.66e-14
HSA-MIR-133A-1 -0.3455 2.678e-15 2.09e-13
HSA-MIR-221 -0.3351 1.977e-14 1.32e-12
HSA-MIR-222 -0.3268 9.353e-14 5.48e-12
HSA-MIR-592 0.348 1.174e-13 6.12e-12
HSA-MIR-708 0.3199 3.221e-13 1.51e-11
Clinical variable #10: 'PSA_VALUE'

30 miRs related to 'PSA_VALUE'.

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

PSA_VALUE Mean (SD) 1.75 (16)
  Significant markers N = 30
  pos. correlated 12
  neg. correlated 18
List of top 10 miRs differentially expressed by 'PSA_VALUE'

Table S19.  Get Full Table List of top 10 miRs significantly correlated to 'PSA_VALUE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-135A-1 -0.2047 1.538e-05 0.00721
HSA-MIR-582 -0.1878 7.563e-05 0.0177
HSA-MIR-330 0.1786 0.0001692 0.0223
HSA-MIR-155 0.1772 0.0001903 0.0223
HSA-MIR-133A-1 -0.1562 0.001022 0.0758
HSA-MIR-30A -0.156 0.00104 0.0758
HSA-MIR-150 0.1549 0.001132 0.0758
HSA-MIR-148A -0.1485 0.001808 0.0883
HSA-MIR-145 -0.148 0.001875 0.0883
HSA-MIR-1247 0.148 0.001882 0.0883
Clinical variable #11: 'RACE'

No miR related to 'RACE'.

Table S20.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 7
  WHITE 146
     
  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 = 494

  • Number of miRs = 469

  • Number of clinical features = 11

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[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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