Correlation between miRseq expression and clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C14T6HTT
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/C14T6HTT
Overview
Introduction

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

Summary

Testing the association between 524 miRs and 10 clinical features across 179 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one miRs.

  • 1 miR correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-758

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-181A-2 ,  HSA-MIR-671 ,  HSA-MIR-483 ,  HSA-MIR-29B-2 ,  HSA-MIR-29C ,  ...

  • 30 miRs correlated to 'TUMOR_TISSUE_SITE'.

    • HSA-MIR-143 ,  HSA-MIR-433 ,  HSA-MIR-3127 ,  HSA-MIR-889 ,  HSA-MIR-541 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-433 ,  HSA-MIR-143 ,  HSA-MIR-889 ,  HSA-MIR-410 ,  HSA-MIR-3127 ,  ...

  • 1 miR correlated to 'RACE'.

    • HSA-MIR-1304

  • No miRs correlated to 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_OF_LYMPH_NODES', 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=1   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=7 younger N=23
TUMOR_TISSUE_SITE Wilcoxon test N=30 extra-adrenal site N=30 adrenal gland N=0
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test   N=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=1        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

One 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) 0.1-316.7 (median=24.7)
  censored N = 172
  death N = 6
     
  Significant markers N = 1
  associated with shorter survival NA
  associated with longer survival NA
List of one miR differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of one miR 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-758 0.000484 0.25 0.653
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) 47.33 (15)
  Significant markers N = 30
  pos. correlated 7
  neg. correlated 23
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-181A-2 -0.3176 1.473e-05 0.00772
HSA-MIR-671 0.2823 0.0001287 0.027
HSA-MIR-483 -0.2756 0.0001889 0.027
HSA-MIR-29B-2 0.2739 0.0002077 0.027
HSA-MIR-29C 0.2695 0.0002638 0.027
HSA-MIR-29B-1 0.2666 0.0003086 0.027
HSA-MIR-3929 -0.298 0.0005191 0.0387
HSA-MIR-670 -0.2663 0.0005915 0.0387
HSA-MIR-143 -0.2406 0.001178 0.0618
HSA-MIR-224 -0.2391 0.001269 0.0618
Clinical variable #3: 'TUMOR_TISSUE_SITE'

30 miRs related to 'TUMOR_TISSUE_SITE'.

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

TUMOR_TISSUE_SITE Labels N
  ADRENAL GLAND 147
  EXTRA-ADRENAL SITE 32
     
  Significant markers N = 30
  Higher in EXTRA-ADRENAL SITE 30
  Higher in ADRENAL GLAND 0
List of top 10 miRs differentially expressed by 'TUMOR_TISSUE_SITE'

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

W(pos if higher in 'EXTRA-ADRENAL SITE') wilcoxontestP Q AUC
HSA-MIR-143 3797 5.389e-08 2.82e-05 0.8072
HSA-MIR-433 3707 3.411e-07 8.94e-05 0.7881
HSA-MIR-3127 1051 9.787e-07 0.000171 0.7766
HSA-MIR-889 3579 3.888e-06 0.000481 0.7608
HSA-MIR-541 3509 5.892e-06 0.000481 0.7562
HSA-MIR-410 3555 5.984e-06 0.000481 0.7557
HSA-MIR-26A-2 3551 6.425e-06 0.000481 0.7549
HSA-MIR-27A 3540 7.804e-06 0.000511 0.7526
HSA-MIR-34C 3470 1.178e-05 0.000608 0.7478
HSA-MIR-376A-1 3506 1.409e-05 0.000608 0.7453
Clinical variable #4: 'GENDER'

No miR related to 'GENDER'.

Table S7.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 101
  MALE 78
     
  Significant markers N = 0
Clinical variable #5: 'RADIATION_THERAPY'

No miR related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 172
  YES 5
     
  Significant markers N = 0
Clinical variable #6: 'KARNOFSKY_PERFORMANCE_SCORE'

No miR related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 96.77 (6.2)
  Score N
  70 1
  80 2
  90 13
  100 46
     
  Significant markers N = 0
Clinical variable #7: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PARAGANGLIOMA 18
  PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA 13
  PHEOCHROMOCYTOMA 148
     
  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-433 7.608e-07 0.000285
HSA-MIR-143 1.088e-06 0.000285
HSA-MIR-889 1.033e-05 0.00177
HSA-MIR-410 1.353e-05 0.00177
HSA-MIR-3127 1.865e-05 0.00195
HSA-MIR-541 2.835e-05 0.00246
HSA-MIR-431 3.305e-05 0.00246
HSA-MIR-363 4.077e-05 0.00246
HSA-MIR-376A-1 4.262e-05 0.00246
HSA-MIR-27A 4.689e-05 0.00246
Clinical variable #8: 'NUMBER_OF_LYMPH_NODES'

No miR related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 0.86 (2.8)
  Value N
  0 16
  1 3
  2 1
  13 1
     
  Significant markers N = 0
Clinical variable #9: 'RACE'

One miR related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 6
  BLACK OR AFRICAN AMERICAN 20
  WHITE 148
     
  Significant markers N = 1
List of one miR differentially expressed by 'RACE'

Table S14.  Get Full Table List of one miR differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-1304 4.384e-05 0.023
Clinical variable #10: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 5
  NOT HISPANIC OR LATINO 138
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 179

  • Number of miRs = 524

  • 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, 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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