Correlation between miRseq expression and clinical features
Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C13B5ZJ3
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/C13B5ZJ3
Overview
Introduction

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

Summary

Testing the association between 468 miRs and 14 clinical features across 873 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-MIR-130B ,  HSA-MIR-365-1 ,  HSA-MIR-365-2 ,  HSA-MIR-1293 ,  HSA-MIR-18A ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-34A ,  HSA-MIR-339 ,  HSA-MIR-615 ,  HSA-MIR-197 ,  HSA-MIR-204 ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-LET-7I ,  HSA-MIR-130B ,  HSA-MIR-153-2 ,  HSA-MIR-224 ,  HSA-MIR-199B ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-153-2 ,  HSA-MIR-429 ,  HSA-MIR-219-1 ,  HSA-MIR-130B ,  HSA-MIR-204 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-26A-1 ,  HSA-MIR-126 ,  HSA-MIR-139 ,  HSA-MIR-10B ,  HSA-MIR-24-1 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_M_STAGE'.

    • HSA-MIR-155 ,  HSA-MIR-130B ,  HSA-LET-7I ,  HSA-MIR-486 ,  HSA-MIR-193A ,  ...

  • 30 miRs correlated to 'GENDER'.

    • HSA-MIR-31 ,  HSA-MIR-625 ,  HSA-MIR-21 ,  HSA-MIR-676 ,  HSA-MIR-100 ,  ...

  • 3 miRs correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • HSA-LET-7I ,  HSA-MIR-181B-1 ,  HSA-MIR-181A-1

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-126 ,  HSA-MIR-145 ,  HSA-MIR-628 ,  HSA-MIR-210 ,  HSA-MIR-1180 ,  ...

  • 30 miRs correlated to 'RACE'.

    • HSA-MIR-3607 ,  HSA-MIR-26A-1 ,  HSA-MIR-590 ,  HSA-MIR-3605 ,  HSA-MIR-628 ,  ...

  • No miRs correlated to 'RADIATION_THERAPY', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 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=28 younger N=2
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=24 lower stage N=6
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=9 lower stage N=21
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=3 higher score N=0 lower score N=3
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
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) 0.1-194.8 (median=35.3)
  censored N = 646
  death N = 226
     
  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-MIR-130B 0 0 0.685
HSA-MIR-365-1 9.85e-14 2.3e-11 0.652
HSA-MIR-365-2 2.92e-12 4.5e-10 0.656
HSA-MIR-1293 5.91e-12 6.9e-10 0.681
HSA-MIR-18A 8.16e-12 7.6e-10 0.631
HSA-MIR-301A 5.86e-11 4.6e-09 0.647
HSA-MIR-153-2 1.17e-10 7.8e-09 0.643
HSA-MIR-155 6.09e-10 3.6e-08 0.647
HSA-MIR-1269 2.77e-09 1.4e-07 0.635
HSA-MIR-320B-2 4.41e-09 2.1e-07 0.644
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) 60.18 (13)
  Significant markers N = 30
  pos. correlated 28
  neg. correlated 2
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-34A 0.226 1.624e-11 7.6e-09
HSA-MIR-339 0.1749 2.162e-07 5.06e-05
HSA-MIR-615 0.1677 7.115e-07 0.000111
HSA-MIR-197 0.165 1.016e-06 0.000119
HSA-MIR-204 0.1624 1.539e-06 0.000144
HSA-MIR-218-2 -0.1595 2.321e-06 0.000177
HSA-MIR-877 0.1671 2.961e-06 0.000177
HSA-MIR-455 0.1577 3.021e-06 0.000177
HSA-MIR-505 0.1564 3.638e-06 0.000189
HSA-MIR-26B 0.154 5.143e-06 0.000241
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 447
  STAGE II 101
  STAGE III 189
  STAGE IV 104
     
  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-LET-7I 1.364e-18 6.39e-16
HSA-MIR-130B 6.676e-17 1.56e-14
HSA-MIR-153-2 9.038e-16 1.41e-13
HSA-MIR-224 2.325e-14 2.72e-12
HSA-MIR-199B 9.084e-14 8.5e-12
HSA-MIR-365-2 1.82e-13 1.42e-11
HSA-MIR-320B-2 2.233e-13 1.49e-11
HSA-MIR-130A 3.059e-13 1.74e-11
HSA-MIR-365-1 3.541e-13 1.74e-11
HSA-MIR-155 3.719e-13 1.74e-11
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) 1.79 (0.93)
  N
  T1 474
  T2 125
  T3 257
  T4 15
     
  Significant markers N = 30
  pos. correlated 24
  neg. correlated 6
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-153-2 0.269 1.055e-15 4.94e-13
HSA-MIR-429 -0.2601 6.344e-15 1.48e-12
HSA-MIR-219-1 -0.2562 5.989e-14 7.79e-12
HSA-MIR-130B 0.2502 6.656e-14 7.79e-12
HSA-MIR-204 -0.2469 1.5e-13 1.22e-11
HSA-MIR-200A -0.2466 1.569e-13 1.22e-11
HSA-MIR-200B -0.2455 2.024e-13 1.35e-11
HSA-LET-7I 0.2379 1.144e-12 6.7e-11
HSA-MIR-224 0.2338 2.9e-12 1.48e-10
HSA-MIR-424 0.2332 3.168e-12 1.48e-10
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) 0.15 (0.4)
  N
  N0 318
  N1 44
  N2 6
     
  Significant markers N = 30
  pos. correlated 9
  neg. correlated 21
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-26A-1 -0.2876 2.114e-08 5.98e-06
HSA-MIR-126 -0.2852 2.555e-08 5.98e-06
HSA-MIR-139 -0.2805 4.415e-08 6.89e-06
HSA-MIR-10B -0.2673 1.933e-07 2.26e-05
HSA-MIR-24-1 -0.2566 6.071e-07 5.68e-05
HSA-MIR-21 0.2448 2.01e-06 0.000157
HSA-MIR-3173 -0.3127 2.486e-06 0.000163
HSA-MIR-135B 0.2489 3.074e-06 0.000163
HSA-MIR-1468 -0.2406 3.126e-06 0.000163
HSA-MIR-30E -0.2387 3.643e-06 0.000169
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 miRs related to 'PATHOLOGY_M_STAGE'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 535
  class1 89
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 miRs differentially expressed by 'PATHOLOGY_M_STAGE'

Table S12.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
HSA-MIR-155 32766 1.282e-08 6e-06 0.6881
HSA-MIR-130B 32505 3.339e-08 7.81e-06 0.6827
HSA-LET-7I 32234 8.768e-08 1.37e-05 0.677
HSA-MIR-486 15742 3.034e-07 3.55e-05 0.6694
HSA-MIR-193A 31758 4.457e-07 4.17e-05 0.667
HSA-MIR-106B 31703 5.348e-07 4.17e-05 0.6658
HSA-MIR-144 16206 1.388e-06 9.19e-05 0.6596
HSA-MIR-153-2 31255 1.57e-06 9.19e-05 0.6589
HSA-MIR-28 31127 3.358e-06 0.000175 0.6537
HSA-MIR-365-2 30992 5.069e-06 0.000224 0.6509
Clinical variable #7: 'GENDER'

30 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 285
  MALE 588
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 miRs differentially expressed by 'GENDER'

Table S14.  Get Full Table List of top 10 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-31 91297 4.708e-09 2.2e-06 0.6269
HSA-MIR-625 103268 2.473e-08 5.79e-06 0.6162
HSA-MIR-21 101428 4.453e-07 6.95e-05 0.6053
HSA-MIR-676 53540 1.273e-06 0.000149 0.6063
HSA-MIR-100 100256 2.441e-06 0.000228 0.5983
HSA-MIR-708 100009 3.445e-06 0.000269 0.5968
HSA-MIR-135B 89921 9.094e-06 0.000608 0.5953
HSA-MIR-27A 99030 1.288e-05 0.000754 0.5909
HSA-MIR-584 69120 2.682e-05 0.00139 0.5875
HSA-MIR-455 69434 3.973e-05 0.00186 0.5857
Clinical variable #8: 'RADIATION_THERAPY'

No miR related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 410
  YES 3
     
  Significant markers N = 0
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

3 miRs related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 87.78 (23)
  Significant markers N = 3
  pos. correlated 0
  neg. correlated 3
List of 3 miRs differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S17.  Get Full Table List of 3 miRs significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-LET-7I -0.3388 3.273e-05 0.0153
HSA-MIR-181B-1 -0.288 0.0004647 0.109
HSA-MIR-181A-1 -0.2749 0.0008556 0.133
Clinical variable #10: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  KIDNEY CHROMOPHOBE 66
  KIDNEY CLEAR CELL RENAL CARCINOMA 516
  KIDNEY PAPILLARY RENAL CELL CARCINOMA 291
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S19.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
HSA-MIR-126 2.679e-131 1.25e-128
HSA-MIR-145 1.81e-105 4.23e-103
HSA-MIR-628 2.919e-100 4.55e-98
HSA-MIR-210 3.659e-99 4.28e-97
HSA-MIR-1180 6.741e-99 6.31e-97
HSA-MIR-200B 3.482e-96 2.72e-94
HSA-MIR-1271 5.369e-95 3.59e-93
HSA-MIR-3605 1.937e-94 1.13e-92
HSA-MIR-195 4.557e-94 2.37e-92
HSA-MIR-424 3.37e-92 1.58e-90
Clinical variable #11: 'NUMBER_PACK_YEARS_SMOKED'

No miR related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 30.39 (25)
  Significant markers N = 0
Clinical variable #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No miR related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1973.45 (16)
  Significant markers N = 0
Clinical variable #13: 'RACE'

30 miRs related to 'RACE'.

Table S22.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 2
  ASIAN 16
  BLACK OR AFRICAN AMERICAN 121
  WHITE 710
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RACE'

Table S23.  Get Full Table List of top 10 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-3607 8.641e-17 4.04e-14
HSA-MIR-26A-1 1.346e-14 3.15e-12
HSA-MIR-590 2.615e-14 3.16e-12
HSA-MIR-3605 2.703e-14 3.16e-12
HSA-MIR-628 4.991e-13 4.67e-11
HSA-MIR-3647 1.858e-12 1.45e-10
HSA-MIR-16-1 9.353e-12 6.25e-10
HSA-MIR-181C 3.517e-11 1.88e-09
HSA-MIR-577 3.619e-11 1.88e-09
HSA-MIR-424 6.145e-11 2.88e-09
Clinical variable #14: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S24.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 40
  NOT HISPANIC OR LATINO 620
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 873

  • Number of miRs = 468

  • Number of clinical features = 14

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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