Correlation between miRseq expression and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C1SN08BJ
Overview
Introduction

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

Summary

Testing the association between 572 miRs and 13 clinical features across 409 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one miRs.

  • 30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-LET-7C ,  HSA-MIR-125B-2 ,  HSA-MIR-29C ,  HSA-MIR-1237 ,  HSA-MIR-337 ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-493 ,  HSA-MIR-552 ,  HSA-MIR-376B ,  HSA-MIR-214 ,  HSA-MIR-337 ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-199A-1 ,  HSA-MIR-199A-2 ,  HSA-MIR-199B ,  HSA-MIR-125B-1 ,  HSA-MIR-1245 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-199A-2 ,  HSA-MIR-199A-1 ,  HSA-MIR-199B ,  HSA-MIR-214 ,  HSA-MIR-99A ,  ...

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-100 ,  HSA-MIR-125B-1 ,  HSA-MIR-99A ,  HSA-MIR-1976 ,  HSA-MIR-944 ,  ...

  • 9 miRs correlated to 'GENDER'.

    • HSA-MIR-329-1 ,  HSA-MIR-223 ,  HSA-MIR-152 ,  HSA-MIR-655 ,  HSA-MIR-29A ,  ...

  • 30 miRs correlated to 'RADIATION_THERAPY'.

    • HSA-MIR-664 ,  HSA-MIR-101-1 ,  HSA-MIR-659 ,  HSA-MIR-29C ,  HSA-MIR-598 ,  ...

  • 30 miRs correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • HSA-MIR-30E ,  HSA-MIR-338 ,  HSA-MIR-101-1 ,  HSA-MIR-200B ,  HSA-MIR-200C ,  ...

  • 30 miRs correlated to 'NUMBER_OF_LYMPH_NODES'.

    • HSA-MIR-125B-1 ,  HSA-MIR-100 ,  HSA-MIR-99A ,  HSA-MIR-545 ,  HSA-MIR-3662 ,  ...

  • 30 miRs correlated to 'RACE'.

    • HSA-MIR-664 ,  HSA-MIR-598 ,  HSA-MIR-320A ,  HSA-MIR-212 ,  HSA-MIR-30D ,  ...

  • No miRs correlated to 'PATHOLOGY_M_STAGE', 'NUMBER_PACK_YEARS_SMOKED', and 'ETHNICITY'.

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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=26 younger N=4
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=20 lower stage N=10
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=11 lower stage N=19
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=9 male N=9 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=2 lower score N=28
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=11 lower number_of_lymph_nodes N=19
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 miRs 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) 0.4-166 (median=17.6)
  censored N = 228
  death N = 180
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
HSA-LET-7C 6.72e-05 0.038 0.57
HSA-MIR-125B-2 0.000287 0.08 0.582
HSA-MIR-29C 0.000419 0.08 0.413
HSA-MIR-1237 0.000856 0.12 0.508
HSA-MIR-337 0.00145 0.12 0.591
HSA-MIR-99A 0.00151 0.12 0.566
HSA-MIR-431 0.00161 0.12 0.577
HSA-MIR-101-1 0.00167 0.12 0.431
HSA-MIR-217 0.00245 0.14 0.563
HSA-MIR-200C 0.00273 0.14 0.437
Clinical variable #2: 'YEARS_TO_BIRTH'

30 miRs related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 68.05 (11)
  Significant markers N = 30
  pos. correlated 26
  neg. correlated 4
List of top 10 miRs differentially expressed by 'YEARS_TO_BIRTH'

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

SpearmanCorr corrP Q
HSA-MIR-493 0.2104 1.835e-05 0.00283
HSA-MIR-552 -0.2916 1.928e-05 0.00283
HSA-MIR-376B 0.2118 2.301e-05 0.00283
HSA-MIR-214 0.2048 3.068e-05 0.00283
HSA-MIR-337 0.2045 3.156e-05 0.00283
HSA-MIR-494 0.2109 3.673e-05 0.00283
HSA-MIR-377 0.2036 4.275e-05 0.00283
HSA-MIR-127 0.2007 4.445e-05 0.00283
HSA-MIR-655 0.2044 4.447e-05 0.00283
HSA-MIR-125B-1 0.1971 6.114e-05 0.0035
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 miRs related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 2
  STAGE II 131
  STAGE III 139
  STAGE IV 135
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
HSA-MIR-199A-1 1.005e-09 3.05e-07
HSA-MIR-199A-2 1.066e-09 3.05e-07
HSA-MIR-199B 5.152e-09 9.82e-07
HSA-MIR-125B-1 1.291e-08 1.28e-06
HSA-MIR-1245 1.572e-08 1.28e-06
HSA-MIR-99A 1.725e-08 1.28e-06
HSA-MIR-100 1.766e-08 1.28e-06
HSA-MIR-214 1.788e-08 1.28e-06
HSA-MIR-141 2.795e-08 1.75e-06
HSA-MIR-652 3.057e-08 1.75e-06
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 miRs related to 'PATHOLOGY_T_STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 2.81 (0.7)
  N
  T0 1
  T1 3
  T2 120
  T3 194
  T4 58
     
  Significant markers N = 30
  pos. correlated 20
  neg. correlated 10
List of top 10 miRs differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-199A-2 0.3104 7.723e-10 2.86e-07
HSA-MIR-199A-1 0.3084 9.986e-10 2.86e-07
HSA-MIR-199B 0.3037 1.842e-09 3.51e-07
HSA-MIR-214 0.2796 3.519e-08 5.03e-06
HSA-MIR-99A 0.2758 5.44e-08 6.22e-06
HSA-MIR-200C -0.2648 1.878e-07 1.79e-05
HSA-MIR-127 0.2584 3.762e-07 2.75e-05
HSA-MIR-3193 -0.288 3.851e-07 2.75e-05
HSA-MIR-493 0.2558 4.954e-07 3.15e-05
HSA-MIR-382 0.2488 1.028e-06 5.46e-05
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 miRs related to 'PATHOLOGY_N_STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Mean (SD) 0.6 (0.89)
  N
  N0 237
  N1 46
  N2 76
  N3 8
     
  Significant markers N = 30
  pos. correlated 11
  neg. correlated 19
List of top 10 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-100 0.267 2.074e-07 0.000107
HSA-MIR-125B-1 0.2615 3.752e-07 0.000107
HSA-MIR-99A 0.2422 2.667e-06 0.000508
HSA-MIR-1976 -0.2311 7.726e-06 0.00105
HSA-MIR-944 -0.2305 9.187e-06 0.00105
HSA-MIR-874 -0.2225 1.686e-05 0.00152
HSA-MIR-582 -0.2215 1.855e-05 0.00152
HSA-MIR-3613 -0.2199 2.137e-05 0.00153
HSA-MIR-125B-2 0.2144 4.425e-05 0.00281
HSA-MIR-545 -0.2242 6.112e-05 0.0035
Clinical variable #6: '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
  class0 195
  class1 11
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

9 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 107
  MALE 302
     
  Significant markers N = 9
  Higher in MALE 9
  Higher in FEMALE 0
List of 9 miRs differentially expressed by 'GENDER'

Table S13.  Get Full Table List of 9 miRs differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
HSA-MIR-329-1 7224 0.0004798 0.241 0.6276
HSA-MIR-223 12745 0.001167 0.241 0.6056
HSA-MIR-152 12919 0.002062 0.241 0.6002
HSA-MIR-655 12008 0.002716 0.241 0.5994
HSA-MIR-29A 13007 0.002723 0.241 0.5975
HSA-MIR-369 12983 0.002902 0.241 0.5969
HSA-MIR-376C 13032 0.002943 0.241 0.5967
HSA-MIR-651 13093 0.003551 0.254 0.5948
HSA-MIR-410 13099 0.004142 0.263 0.5933
Clinical variable #8: 'RADIATION_THERAPY'

30 miRs related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 363
  YES 20
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

Table S15.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-664 5547 7.003e-05 0.0188 0.764
HSA-MIR-101-1 5495 0.0001096 0.0188 0.7569
HSA-MIR-659 5130 0.0001148 0.0188 0.7566
HSA-MIR-29C 5468 0.0001377 0.0188 0.7532
HSA-MIR-598 5447 0.0001641 0.0188 0.7503
HSA-MIR-30B 5376 0.000293 0.0279 0.7405
HSA-MIR-3653 5298 0.000541 0.0399 0.7298
HSA-MIR-96 5294 0.0005579 0.0399 0.7292
HSA-MIR-200A 5265 0.0006961 0.0426 0.7252
HSA-MIR-1275 1214 0.0007716 0.0426 0.8056
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

30 miRs related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 83.04 (14)
  Significant markers N = 30
  pos. correlated 2
  neg. correlated 28
List of top 10 miRs differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

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

SpearmanCorr corrP Q
HSA-MIR-30E -0.3659 1.276e-05 0.00523
HSA-MIR-338 0.359 1.899e-05 0.00523
HSA-MIR-101-1 -0.3484 3.467e-05 0.00523
HSA-MIR-200B -0.3421 4.883e-05 0.00523
HSA-MIR-200C -0.3396 5.597e-05 0.00523
HSA-MIR-598 -0.3392 5.702e-05 0.00523
HSA-MIR-3655 -0.5297 6.404e-05 0.00523
HSA-MIR-148B -0.3308 8.884e-05 0.00635
HSA-MIR-151 -0.3238 0.0001274 0.00673
HSA-MIR-146B 0.3238 0.0001278 0.00673
Clinical variable #10: 'NUMBER_PACK_YEARS_SMOKED'

No miR related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 39.14 (53)
  Significant markers N = 0
Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

30 miRs related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.1 (7)
  Significant markers N = 30
  pos. correlated 11
  neg. correlated 19
List of top 10 miRs differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S20.  Get Full Table List of top 10 miRs significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-125B-1 0.2428 2.483e-05 0.00848
HSA-MIR-100 0.2406 2.965e-05 0.00848
HSA-MIR-99A 0.228 7.738e-05 0.0148
HSA-MIR-545 -0.2343 0.0001698 0.0243
HSA-MIR-3662 -0.2451 0.000398 0.0388
HSA-MIR-944 -0.2059 0.0004071 0.0388
HSA-MIR-1976 -0.1988 0.000593 0.0485
HSA-MIR-125B-2 0.1959 0.0008144 0.0582
HSA-MIR-455 -0.1913 0.0009583 0.0609
HSA-LET-7C 0.1885 0.001138 0.0651
Clinical variable #12: 'RACE'

30 miRs related to 'RACE'.

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

RACE Labels N
  ASIAN 44
  BLACK OR AFRICAN AMERICAN 23
  WHITE 324
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RACE'

Table S22.  Get Full Table List of top 10 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-664 1.89e-10 1.08e-07
HSA-MIR-598 8.439e-10 2.41e-07
HSA-MIR-320A 5.593e-09 1.07e-06
HSA-MIR-212 1.11e-08 1.43e-06
HSA-MIR-30D 1.252e-08 1.43e-06
HSA-MIR-29C 2.572e-08 2.45e-06
HSA-MIR-200C 3.482e-08 2.85e-06
HSA-MIR-200B 4.034e-08 2.88e-06
HSA-MIR-30E 1.705e-07 1.08e-05
HSA-MIR-200A 2.24e-07 1.28e-05
Clinical variable #13: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S23.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 9
  NOT HISPANIC OR LATINO 368
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = BLCA-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 409

  • Number of miRs = 572

  • Number of clinical features = 13

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)