Correlation between miRseq expression and clinical features
Testicular Germ Cell Tumors (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C19Z9468
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/C19Z9468
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 658 miRs and 10 clinical features across 134 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one miRs.

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-199B ,  HSA-MIR-3940 ,  HSA-MIR-199A-2 ,  HSA-MIR-92B ,  HSA-MIR-582 ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-101-1 ,  HSA-MIR-29C ,  HSA-MIR-30E ,  HSA-MIR-195 ,  HSA-MIR-497 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-873 ,  HSA-MIR-628 ,  HSA-MIR-219-2 ,  HSA-MIR-769 ,  HSA-MIR-33A ,  ...

  • 30 miRs correlated to 'RADIATION_THERAPY'.

    • HSA-MIR-200B ,  HSA-MIR-181A-2 ,  HSA-MIR-214 ,  HSA-MIR-582 ,  HSA-MIR-455 ,  ...

  • 4 miRs correlated to 'ETHNICITY'.

    • HSA-MIR-598 ,  HSA-MIR-3130-1 ,  HSA-MIR-769 ,  HSA-MIR-2355

  • No miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', 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=17 younger N=13
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=13 lower stage N=17
PATHOLOGY_N_STAGE Spearman correlation test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test N=4 not hispanic or latino N=4 hispanic or latino 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) 0.1-244.5 (median=41.5)
  censored N = 130
  death N = 3
     
  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) 31.99 (9.3)
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
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-199B -0.2781 0.001139 0.136
HSA-MIR-3940 0.2737 0.001375 0.136
HSA-MIR-199A-2 -0.2701 0.001599 0.136
HSA-MIR-92B 0.2662 0.001881 0.136
HSA-MIR-582 -0.2653 0.001947 0.136
HSA-MIR-9-2 0.2642 0.002035 0.136
HSA-MIR-29B-1 0.2638 0.002075 0.136
HSA-MIR-199A-1 -0.263 0.002137 0.136
HSA-MIR-9-1 0.2627 0.002164 0.136
HSA-MIR-29B-2 0.26 0.002417 0.136
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 miRs related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 19
  STAGE IA 26
  STAGE IB 11
  STAGE II 5
  STAGE IIA 6
  STAGE IIB 1
  STAGE IIC 1
  STAGE III 2
  STAGE IIIA 1
  STAGE IIIB 6
  STAGE IIIC 5
  STAGE IS 46
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'PATHOLOGIC_STAGE'

Table S5.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
HSA-MIR-101-1 3.873e-05 0.0255
HSA-MIR-29C 0.0001783 0.0276
HSA-MIR-30E 0.0002028 0.0276
HSA-MIR-195 0.0002653 0.0276
HSA-MIR-497 0.0002926 0.0276
HSA-LET-7G 0.0003088 0.0276
HSA-MIR-29B-2 0.0003502 0.0276
HSA-MIR-29A 0.0003507 0.0276
HSA-MIR-29B-1 0.0003773 0.0276
HSA-MIR-1277 0.0005652 0.0372
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 miRs related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.47 (0.58)
  N
  T1 76
  T2 51
  T3 6
     
  Significant markers N = 30
  pos. correlated 13
  neg. correlated 17
List of top 10 miRs differentially expressed by 'PATHOLOGY_T_STAGE'

Table S7.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-873 -0.3031 0.0005061 0.144
HSA-MIR-628 -0.2952 0.0005627 0.144
HSA-MIR-219-2 0.2928 0.0006561 0.144
HSA-MIR-769 -0.2677 0.001838 0.159
HSA-MIR-33A -0.2673 0.001869 0.159
HSA-MIR-935 -0.266 0.001973 0.159
HSA-MIR-519C 0.2636 0.002347 0.159
HSA-MIR-504 -0.2623 0.002572 0.159
HSA-MIR-526A-1 0.299 0.002644 0.159
HSA-MIR-212 0.2574 0.002781 0.159
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No miR related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.25 (0.51)
  N
  N0 46
  N1 11
  N2 2
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No miR related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 115
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'RADIATION_THERAPY'

30 miRs related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 110
  YES 19
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

Table S11.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-200B 351 4.049e-06 0.00223 0.8321
HSA-MIR-181A-2 391 1.405e-05 0.00223 0.8129
HSA-MIR-214 401 1.898e-05 0.00223 0.8081
HSA-MIR-582 405 2.138e-05 0.00223 0.8062
HSA-MIR-455 407 2.268e-05 0.00223 0.8053
HSA-MIR-639 1633 2.675e-05 0.00223 0.8032
HSA-MIR-3941 1603 2.707e-05 0.00223 0.8035
HSA-MIR-767 1677 2.707e-05 0.00223 0.8024
HSA-MIR-3137 790 3.37e-05 0.00246 0.855
HSA-MIR-20B 435 5.109e-05 0.00255 0.7919
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

No miR related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 94.9 (6)
  Score N
  80 5
  90 40
  100 53
     
  Significant markers N = 0
Clinical variable #9: 'RACE'

No miR related to 'RACE'.

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

RACE Labels N
  ASIAN 4
  BLACK OR AFRICAN AMERICAN 6
  WHITE 119
     
  Significant markers N = 0
Clinical variable #10: 'ETHNICITY'

4 miRs related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 111
     
  Significant markers N = 4
  Higher in NOT HISPANIC OR LATINO 4
  Higher in HISPANIC OR LATINO 0
List of 4 miRs differentially expressed by 'ETHNICITY'

Table S15.  Get Full Table List of 4 miRs differentially expressed by 'ETHNICITY'

W(pos if higher in 'NOT HISPANIC OR LATINO') wilcoxontestP Q AUC
HSA-MIR-598 c("260", "0.0005475") c("260", "0.0005475") 0.244 0.8048
HSA-MIR-3130-1 c("1052", "0.001017") c("1052", "0.001017") 0.244 0.7898
HSA-MIR-769 c("283", "0.001113") c("283", "0.001113") 0.244 0.7875
HSA-MIR-2355 c("299", "0.001785") c("299", "0.001785") 0.294 0.7755
Methods & Data
Input

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

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

  • Number of patients = 134

  • Number of miRs = 658

  • Number of clinical features = 10

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, 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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