Correlation between miRseq expression and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1BC3XZT
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/C1BC3XZT
Overview
Introduction

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

Summary

Testing the association between 481 miRs and 12 clinical features across 291 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-3199-2 ,  HSA-MIR-34A ,  HSA-MIR-539 ,  HSA-MIR-323B ,  HSA-MIR-452 ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-34A ,  HSA-MIR-204 ,  HSA-MIR-486 ,  HSA-MIR-1468 ,  HSA-MIR-451 ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-224 ,  HSA-MIR-452 ,  HSA-MIR-200B ,  HSA-MIR-217 ,  HSA-MIR-429 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-200B ,  HSA-MIR-429 ,  HSA-MIR-200A ,  HSA-MIR-216A ,  HSA-MIR-217 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-34A ,  HSA-MIR-211 ,  HSA-MIR-589 ,  HSA-MIR-224 ,  HSA-MIR-584 ,  ...

  • 2 miRs correlated to 'PATHOLOGY_M_STAGE'.

    • HSA-MIR-34A ,  HSA-MIR-937

  • 30 miRs correlated to 'GENDER'.

    • HSA-MIR-625 ,  HSA-MIR-196A-2 ,  HSA-MIR-219-1 ,  HSA-LET-7E ,  HSA-MIR-125A ,  ...

  • 7 miRs correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • HSA-MIR-200B ,  HSA-LET-7I ,  HSA-MIR-429 ,  HSA-MIR-200A ,  HSA-MIR-27B ,  ...

  • 13 miRs correlated to 'RACE'.

    • HSA-MIR-1304 ,  HSA-MIR-1269 ,  HSA-MIR-769 ,  HSA-MIR-2114 ,  HSA-MIR-328 ,  ...

  • No miRs correlated to '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=26 lower stage N=4
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=19 lower stage N=11
PATHOLOGY_M_STAGE Wilcoxon test N=2 class1 N=2 class0 N=0
GENDER Wilcoxon test N=30 male N=30 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=7 higher score N=4 lower score N=3
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=13        
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=25.3)
  censored N = 246
  death N = 44
     
  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-3199-2 3.62e-09 1.7e-06 0.74
HSA-MIR-34A 4.58e-08 1.1e-05 0.212
HSA-MIR-539 2.16e-07 3.5e-05 0.719
HSA-MIR-323B 6.05e-07 7.3e-05 0.712
HSA-MIR-452 1.58e-06 0.00015 0.71
HSA-MIR-224 2.68e-06 0.00022 0.733
HSA-MIR-377 6.46e-06 0.00044 0.685
HSA-MIR-1293 7.56e-06 0.00045 0.713
HSA-MIR-382 9.16e-06 0.00049 0.696
HSA-MIR-200B 1.4e-05 0.00067 0.285
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) 61.52 (12)
  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 0.2712 3.268e-06 0.00157
HSA-MIR-204 0.2555 1.219e-05 0.0022
HSA-MIR-486 0.2527 1.528e-05 0.0022
HSA-MIR-1468 0.2504 1.829e-05 0.0022
HSA-MIR-451 0.2464 2.503e-05 0.00241
HSA-MIR-1976 0.2434 3.175e-05 0.00255
HSA-MIR-144 0.2313 7.86e-05 0.0054
HSA-MIR-765 0.2839 0.0001121 0.00647
HSA-MIR-30E 0.2254 0.000121 0.00647
HSA-MIR-627 0.2293 0.0001612 0.00776
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 173
  STAGE II 21
  STAGE III 52
  STAGE IV 15
     
  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-224 3.53e-09 1.25e-06
HSA-MIR-452 5.214e-09 1.25e-06
HSA-MIR-200B 1.275e-08 2.04e-06
HSA-MIR-217 6.321e-08 7.6e-06
HSA-MIR-429 1.665e-07 1.6e-05
HSA-MIR-320B-2 2.779e-07 2.23e-05
HSA-MIR-200A 9.959e-07 6.84e-05
HSA-MIR-216A 4.265e-06 0.000256
HSA-MIR-1-2 8.723e-06 0.00043
HSA-MIR-100 8.949e-06 0.00043
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) 1.55 (0.84)
  N
  T1 194
  T2 33
  T3 60
  T4 2
     
  Significant markers N = 30
  pos. correlated 26
  neg. correlated 4
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-200B -0.3711 7.286e-11 3.5e-08
HSA-MIR-429 -0.3443 1.827e-09 4.39e-07
HSA-MIR-200A -0.3305 8.558e-09 1.37e-06
HSA-MIR-216A 0.4643 1.41e-08 1.7e-06
HSA-MIR-217 0.325 2.185e-08 2.1e-06
HSA-MIR-452 0.3174 3.469e-08 2.78e-06
HSA-MIR-224 0.3063 1.141e-07 7.84e-06
HSA-MIR-143 0.2863 7.405e-07 4.45e-05
HSA-MIR-153-2 0.2856 1.226e-06 6.55e-05
HSA-MIR-320D-1 0.3756 1.857e-06 8.93e-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) 0.41 (0.59)
  N
  N0 50
  N1 24
  N2 4
     
  Significant markers N = 30
  pos. correlated 19
  neg. correlated 11
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-34A -0.4995 3.235e-06 0.00156
HSA-MIR-211 -0.5108 3.042e-05 0.0073
HSA-MIR-589 -0.4445 4.554e-05 0.0073
HSA-MIR-224 0.4203 0.0001277 0.0126
HSA-MIR-584 0.4197 0.0001307 0.0126
HSA-MIR-185 -0.3951 0.000344 0.0256
HSA-MIR-153-2 0.3895 0.0004246 0.0256
HSA-MIR-320C-1 0.4244 0.0004265 0.0256
HSA-MIR-381 0.3803 0.0005939 0.0306
HSA-MIR-502 -0.3783 0.0006372 0.0306
Clinical variable #6: 'PATHOLOGY_M_STAGE'

2 miRs related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 95
  class1 9
     
  Significant markers N = 2
  Higher in class1 2
  Higher in class0 0
List of 2 miRs differentially expressed by 'PATHOLOGY_M_STAGE'

Table S12.  Get Full Table List of 2 miRs differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
HSA-MIR-34A 74 4.48e-05 0.0216 0.9135
HSA-MIR-937 613 0.0003931 0.0945 0.8807
Clinical variable #7: 'GENDER'

30 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 77
  MALE 214
     
  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-625 11412 5.439e-07 0.000262 0.6926
HSA-MIR-196A-2 10759 6.924e-05 0.0167 0.6529
HSA-MIR-219-1 10686 0.0001117 0.0179 0.6485
HSA-LET-7E 5914 0.0002417 0.0291 0.6411
HSA-MIR-125A 5978 0.0003572 0.0344 0.6372
HSA-MIR-31 10404 0.0004773 0.0358 0.6344
HSA-MIR-339 10369 0.0007711 0.0358 0.6293
HSA-LET-7C 10362 0.0008025 0.0358 0.6288
HSA-MIR-99B 6120 0.000821 0.0358 0.6286
HSA-MIR-589 10354 0.0008399 0.0358 0.6284
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

7 miRs related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 87.66 (22)
  Significant markers N = 7
  pos. correlated 4
  neg. correlated 3
List of 7 miRs differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S16.  Get Full Table List of 7 miRs significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-200B 0.4367 7.183e-05 0.0312
HSA-LET-7I -0.4224 0.0001297 0.0312
HSA-MIR-429 0.4068 0.0002413 0.0387
HSA-MIR-200A 0.3802 0.0006487 0.078
HSA-MIR-27B 0.3685 0.0009766 0.094
HSA-MIR-874 -0.3576 0.001408 0.113
HSA-MIR-34C -0.3336 0.003031 0.208
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

No miR related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 31.73 (27)
  Significant markers N = 0
Clinical variable #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No miR related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1972.16 (16)
  Significant markers N = 0
Clinical variable #11: 'RACE'

13 miRs related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 2
  ASIAN 6
  BLACK OR AFRICAN AMERICAN 61
  WHITE 207
     
  Significant markers N = 13
List of top 10 miRs differentially expressed by 'RACE'

Table S20.  Get Full Table List of top 10 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-1304 6.125e-05 0.0295
HSA-MIR-1269 0.0008996 0.149
HSA-MIR-769 0.001193 0.149
HSA-MIR-2114 0.00124 0.149
HSA-MIR-328 0.001764 0.17
HSA-MIR-744 0.002213 0.177
HSA-MIR-3130-1 0.002973 0.204
HSA-MIR-125B-1 0.003704 0.223
HSA-MIR-598 0.004824 0.237
HSA-MIR-130B 0.004933 0.237
Clinical variable #12: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 243
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 291

  • Number of miRs = 481

  • Number of clinical features = 12

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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