Correlation between miRseq expression and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1GB2336
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GB2336
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 462 miRs and 12 clinical features across 512 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 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-223 ,  HSA-MIR-130B ,  HSA-MIR-34C ,  HSA-MIR-21 ,  HSA-MIR-365-2 ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-590 ,  HSA-MIR-148A ,  HSA-MIR-29B-2 ,  HSA-MIR-218-2 ,  HSA-MIR-615 ,  ...

  • 30 miRs correlated to 'NEOPLASM_DISEASESTAGE'.

    • HSA-MIR-139 ,  HSA-MIR-155 ,  HSA-MIR-625 ,  HSA-MIR-486 ,  HSA-LET-7I ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-139 ,  HSA-MIR-486 ,  HSA-MIR-155 ,  HSA-MIR-625 ,  HSA-MIR-21 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-193A ,  HSA-MIR-99B ,  HSA-MIR-34C ,  HSA-MIR-1468 ,  HSA-MIR-21 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_M_STAGE'.

    • HSA-MIR-625 ,  HSA-MIR-155 ,  HSA-MIR-106B ,  HSA-MIR-144 ,  HSA-MIR-10B ,  ...

  • 30 miRs correlated to 'GENDER'.

    • HSA-MIR-100 ,  HSA-MIR-708 ,  HSA-MIR-455 ,  HSA-MIR-599 ,  HSA-MIR-204 ,  ...

  • 30 miRs correlated to 'RACE'.

    • HSA-MIR-3605 ,  HSA-MIR-3607 ,  HSA-MIR-628 ,  HSA-MIR-142 ,  HSA-MIR-215 ,  ...

  • 1 miR correlated to 'ETHNICITY'.

    • HSA-MIR-219-2

  • No miRs correlated to 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_PACK_YEARS_SMOKED', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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 shorter survival N=26 longer survival N=4
YEARS_TO_BIRTH Spearman correlation test N=30 older N=24 younger N=6
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=22 lower stage N=8
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=30 n0 N=0
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=30 male N=30 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
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=1 not hispanic or latino N=1 hispanic or latino 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) 0.1-120.6 (median=36.4)
  censored N = 345
  death N = 166
     
  Significant markers N = 30
  associated with shorter survival 26
  associated with longer survival 4
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

HazardRatio Wald_P Q C_index
HSA-MIR-223 1.6 2.379e-12 1.1e-09 0.641
HSA-MIR-130B 1.84 3.45e-11 8e-09 0.644
HSA-MIR-34C 1.28 1.063e-10 1.6e-08 0.639
HSA-MIR-21 2 8.778e-10 1e-07 0.66
HSA-MIR-365-2 1.67 1.31e-08 1.2e-06 0.62
HSA-MIR-34B 1.31 2.106e-08 1.6e-06 0.63
HSA-MIR-365-1 1.64 3.533e-08 2.3e-06 0.616
HSA-MIR-1248 1.39 6.089e-08 3.2e-06 0.611
HSA-MIR-18A 1.53 6.274e-08 3.2e-06 0.615
HSA-MIR-10B 0.59 9.246e-08 4.3e-06 0.373
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) 60.62 (12)
  Significant markers N = 30
  pos. correlated 24
  neg. correlated 6
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-590 0.1803 4.088e-05 0.0189
HSA-MIR-148A 0.1477 0.0007994 0.137
HSA-MIR-29B-2 0.1431 0.00117 0.137
HSA-MIR-218-2 -0.1416 0.001318 0.137
HSA-MIR-615 0.1402 0.001483 0.137
HSA-MIR-362 0.1352 0.002179 0.157
HSA-MIR-29B-1 0.134 0.002376 0.157
HSA-MIR-30B 0.1321 0.002738 0.158
HSA-MIR-9-3 0.1889 0.003517 0.159
HSA-MIR-374A -0.1285 0.003585 0.159
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

30 miRs related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 251
  STAGE II 55
  STAGE III 126
  STAGE IV 80
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'NEOPLASM_DISEASESTAGE'

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

kruskal_wallis_P Q
HSA-MIR-139 1.446e-13 6.68e-11
HSA-MIR-155 1.228e-09 2.84e-07
HSA-MIR-625 3.172e-09 3.8e-07
HSA-MIR-486 3.292e-09 3.8e-07
HSA-LET-7I 1.27e-08 1.17e-06
HSA-MIR-21 2.729e-08 1.85e-06
HSA-MIR-130B 2.799e-08 1.85e-06
HSA-MIR-142 7.028e-08 4.06e-06
HSA-MIR-144 1.084e-07 5.56e-06
HSA-MIR-106B 1.323e-07 6.11e-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) 1.89 (0.96)
  N
  T1 256
  T2 67
  T3 178
  T4 11
     
  Significant markers N = 30
  pos. correlated 22
  neg. correlated 8
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-139 -0.3358 5.886e-15 2.72e-12
HSA-MIR-486 -0.2733 3.197e-10 6.45e-08
HSA-MIR-155 0.2715 4.19e-10 6.45e-08
HSA-MIR-625 0.2582 3.056e-09 3.53e-07
HSA-MIR-21 0.254 5.588e-09 5.16e-07
HSA-MIR-142 0.2448 2.001e-08 1.54e-06
HSA-MIR-130B 0.2425 2.764e-08 1.82e-06
HSA-MIR-144 -0.2358 6.732e-08 3.72e-06
HSA-MIR-9-1 0.2352 7.249e-08 3.72e-06
HSA-MIR-148A 0.2332 9.388e-08 4.34e-06
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 Labels N
  N0 226
  N1 18
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  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-193A 3037 0.0005041 0.0813 0.7466
HSA-MIR-99B 1046 0.0006114 0.0813 0.7429
HSA-MIR-34C 2640 0.0006128 0.0813 0.7502
HSA-MIR-1468 1082 0.001022 0.0813 0.7328
HSA-MIR-21 2980 0.001035 0.0813 0.7325
HSA-MIR-30C-2 1112 0.001386 0.0813 0.7266
HSA-MIR-10B 1114 0.00142 0.0813 0.7262
HSA-MIR-129-2 362 0.001468 0.0813 0.7877
HSA-MIR-3928 2681 0.0016 0.0813 0.7301
HSA-MIR-3074 1132 0.001759 0.0813 0.7217
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 406
  class1 78
     
  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-625 21783 1.457e-07 6.73e-05 0.6879
HSA-MIR-155 21490 5.764e-07 0.000107 0.6786
HSA-MIR-106B 21421 7.894e-07 0.000107 0.6764
HSA-MIR-144 10283 9.288e-07 0.000107 0.6753
HSA-MIR-10B 10443 1.891e-06 0.000175 0.6702
HSA-MIR-139 10544 2.933e-06 0.000226 0.667
HSA-MIR-130B 20922 6.895e-06 0.000453 0.6607
HSA-MIR-486 10777 7.84e-06 0.000453 0.6597
HSA-MIR-28 20824 1.032e-05 0.00053 0.6576
HSA-LET-7I 20707 1.656e-05 0.000765 0.6539
Clinical variable #7: 'GENDER'

30 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 180
  MALE 332
     
  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-100 42339 6.463e-15 2.99e-12 0.7085
HSA-MIR-708 39970 2.747e-10 6.35e-08 0.6688
HSA-MIR-455 21413 1.177e-07 1.81e-05 0.6417
HSA-MIR-599 7933 2.2e-07 2.54e-05 0.6706
HSA-MIR-204 21855 6.474e-07 5.98e-05 0.6332
HSA-MIR-31 31149 1.934e-06 0.000149 0.6341
HSA-MIR-155 37416 2.423e-06 0.00016 0.6261
HSA-MIR-500B 23278 4.427e-05 0.00256 0.6093
HSA-MIR-500A 23430 5.458e-05 0.0028 0.6079
HSA-MIR-676 17504 0.0001242 0.00574 0.6102
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

No miR related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 88.18 (21)
  Significant markers N = 0
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

No miR related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 28.93 (18)
  Significant markers N = 0
Clinical variable #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No miR related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1979.62 (21)
  Significant markers N = 0
Clinical variable #11: 'RACE'

30 miRs related to 'RACE'.

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

RACE Labels N
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 52
  WHITE 445
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RACE'

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

kruskal_wallis_P Q
HSA-MIR-3605 5.666e-13 2.62e-10
HSA-MIR-3607 1.148e-12 2.65e-10
HSA-MIR-628 2.205e-11 3.4e-09
HSA-MIR-142 1.721e-10 1.99e-08
HSA-MIR-215 9.257e-10 8.55e-08
HSA-MIR-3647 1.296e-09 9.98e-08
HSA-MIR-186 1.608e-09 1.06e-07
HSA-MIR-16-1 2.356e-09 1.36e-07
HSA-MIR-424 3.159e-09 1.62e-07
HSA-MIR-3653 4.871e-09 2.25e-07
Clinical variable #12: 'ETHNICITY'

One miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 24
  NOT HISPANIC OR LATINO 340
     
  Significant markers N = 1
  Higher in NOT HISPANIC OR LATINO 1
  Higher in HISPANIC OR LATINO 0
List of one miR differentially expressed by 'ETHNICITY'

Table S21.  Get Full Table List of one miR differentially expressed by 'ETHNICITY'

W(pos if higher in 'NOT HISPANIC OR LATINO') wilcoxontestP Q AUC
HSA-MIR-219-2 c("118", "0.0001702") c("118", "0.0001702") 0.0786 0.8961
Methods & Data
Input

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

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

  • Number of patients = 512

  • Number of miRs = 462

  • Number of clinical features = 12

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

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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