Correlation between miRseq expression and clinical features
Thymoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C16T0M4F
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/C16T0M4F
Overview
Introduction

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

Summary

Testing the association between 625 miRs and 8 clinical features across 124 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one miRs.

  • 30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-3200 ,  HSA-MIR-140 ,  HSA-MIR-3917 ,  HSA-MIR-548D-1 ,  HSA-MIR-542 ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-521-2 ,  HSA-MIR-3117 ,  HSA-MIR-652 ,  HSA-MIR-150 ,  HSA-MIR-874 ,  ...

  • 2 miRs correlated to 'GENDER'.

    • HSA-MIR-651 ,  HSA-MIR-361

  • 30 miRs correlated to 'RADIATION_THERAPY'.

    • HSA-MIR-516A-2 ,  HSA-MIR-516B-2 ,  HSA-MIR-190 ,  HSA-MIR-515-1 ,  HSA-MIR-139 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-125B-1 ,  HSA-MIR-101-2 ,  HSA-MIR-181A-1 ,  HSA-MIR-181B-1 ,  HSA-MIR-125A ,  ...

  • 4 miRs correlated to 'RACE'.

    • HSA-MIR-3130-1 ,  HSA-MIR-320A ,  HSA-MIR-1269 ,  HSA-MIR-320B-2

  • No miRs correlated to 'TUMOR_TISSUE_SITE', 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=14 younger N=16
TUMOR_TISSUE_SITE Wilcoxon test   N=0        
GENDER Wilcoxon test N=2 male N=2 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RACE Kruskal-Wallis test N=4        
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) 0.5-150.4 (median=40.6)
  censored N = 115
  death N = 8
     
  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-3200 0.00047 0.18 0.736
HSA-MIR-140 0.000614 0.18 0.159
HSA-MIR-3917 0.00125 0.18 0.121
HSA-MIR-548D-1 0.00126 0.18 0.122
HSA-MIR-542 0.00141 0.18 0.166
HSA-MIR-429 0.00196 0.18 0.705
HSA-MIR-181C 0.00218 0.18 0.263
HSA-MIR-181D 0.00265 0.18 0.256
HSA-MIR-548E 0.00275 0.18 0.298
HSA-MIR-1976 0.00281 0.18 0.789
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) 58.15 (13)
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
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-521-2 0.5306 1.374e-06 0.000762
HSA-MIR-3117 -0.4964 2.438e-06 0.000762
HSA-MIR-652 -0.3622 3.842e-05 0.00586
HSA-MIR-150 -0.3601 4.304e-05 0.00586
HSA-MIR-874 -0.3556 5.443e-05 0.00586
HSA-MIR-545 -0.365 5.625e-05 0.00586
HSA-MIR-519E 0.4091 0.0001229 0.011
HSA-MIR-345 -0.3351 0.0001515 0.0118
HSA-MIR-518A-2 0.3517 0.0002034 0.0127
HSA-MIR-515-1 0.3619 0.0002154 0.0127
Clinical variable #3: 'TUMOR_TISSUE_SITE'

No miR related to 'TUMOR_TISSUE_SITE'.

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

TUMOR_TISSUE_SITE Labels N
  ANTERIOR MEDIASTINUM 27
  THYMUS 97
     
  Significant markers N = 0
Clinical variable #4: 'GENDER'

2 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 60
  MALE 64
     
  Significant markers N = 2
  Higher in MALE 2
  Higher in FEMALE 0
List of 2 miRs differentially expressed by 'GENDER'

Table S7.  Get Full Table List of 2 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-651 918 5.514e-07 0.000345 0.7609
HSA-MIR-361 1223 0.0004968 0.155 0.6815
Clinical variable #5: 'RADIATION_THERAPY'

30 miRs related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 81
  YES 43
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

Table S9.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-516A-2 915 1.448e-05 0.00882 0.7373
HSA-MIR-516B-2 866 4.202e-05 0.00882 0.7292
HSA-MIR-190 974 5.655e-05 0.00882 0.7204
HSA-MIR-515-1 582 9.37e-05 0.00882 0.7406
HSA-MIR-139 997 9.384e-05 0.00882 0.7138
HSA-MIR-518A-1 750 0.0001093 0.00882 0.7261
HSA-MIR-516A-1 965 0.0001181 0.00882 0.7128
HSA-MIR-512-2 1016 0.0001411 0.00882 0.7083
HSA-MIR-523 830 0.0001601 0.00882 0.7162
HSA-MIR-520G 954 0.0001741 0.00882 0.7091
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  THYMOMA; TYPE A 17
  THYMOMA; TYPE AB 38
  THYMOMA; TYPE B1 15
  THYMOMA; TYPE B2 31
  THYMOMA; TYPE B3 12
  THYMOMA; TYPE C 11
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
HSA-MIR-125B-1 2.135e-13 8.83e-11
HSA-MIR-101-2 3.668e-13 8.83e-11
HSA-MIR-181A-1 4.24e-13 8.83e-11
HSA-MIR-181B-1 8.038e-13 1.26e-10
HSA-MIR-125A 1.185e-12 1.48e-10
HSA-MIR-128-1 1.684e-12 1.74e-10
HSA-MIR-128-2 1.954e-12 1.74e-10
HSA-MIR-652 4.355e-12 3.23e-10
HSA-MIR-130A 4.655e-12 3.23e-10
HSA-MIR-625 7.281e-12 4.42e-10
Clinical variable #7: 'RACE'

4 miRs related to 'RACE'.

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

RACE Labels N
  ASIAN 13
  BLACK OR AFRICAN AMERICAN 6
  WHITE 103
     
  Significant markers N = 4
List of 4 miRs differentially expressed by 'RACE'

Table S13.  Get Full Table List of 4 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-3130-1 0.0006867 0.294
HSA-MIR-320A 0.00119 0.294
HSA-MIR-1269 0.00186 0.294
HSA-MIR-320B-2 0.001881 0.294
Clinical variable #8: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 10
  NOT HISPANIC OR LATINO 100
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 124

  • Number of miRs = 625

  • Number of clinical features = 8

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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