Correlation between miRseq expression and clinical features
Esophageal Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1HM57TG
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/C1HM57TG
Overview
Introduction

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

Summary

Testing the association between 523 miRs and 15 clinical features across 184 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one miRs.

  • 9 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-380 ,  HSA-MIR-1301 ,  HSA-MIR-425 ,  HSA-MIR-517A ,  HSA-MIR-517B ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-375 ,  HSA-MIR-944 ,  HSA-MIR-708 ,  HSA-MIR-149 ,  HSA-MIR-193B ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-205 ,  HSA-MIR-192 ,  HSA-MIR-708 ,  HSA-MIR-194-2 ,  HSA-MIR-194-1 ,  ...

  • 27 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-199A-1 ,  HSA-MIR-556 ,  HSA-MIR-199A-2 ,  HSA-MIR-34B ,  HSA-MIR-206 ,  ...

  • 26 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-192 ,  HSA-MIR-23A ,  HSA-MIR-7-3 ,  HSA-MIR-194-1 ,  HSA-MIR-194-2 ,  ...

  • 30 miRs correlated to 'RADIATION_THERAPY'.

    • HSA-MIR-338 ,  HSA-MIR-1266 ,  HSA-MIR-592 ,  HSA-MIR-196A-2 ,  HSA-MIR-375 ,  ...

  • 30 miRs correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • HSA-MIR-4326 ,  HSA-MIR-107 ,  HSA-MIR-181C ,  HSA-MIR-26A-2 ,  HSA-MIR-3691 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-192 ,  HSA-MIR-194-1 ,  HSA-MIR-194-2 ,  HSA-MIR-215 ,  HSA-MIR-205 ,  ...

  • 3 miRs correlated to 'NUMBER_OF_LYMPH_NODES'.

    • HSA-MIR-421 ,  HSA-MIR-202 ,  HSA-MIR-641

  • 30 miRs correlated to 'RACE'.

    • HSA-MIR-149 ,  HSA-MIR-205 ,  HSA-MIR-944 ,  HSA-MIR-194-1 ,  HSA-MIR-224 ,  ...

  • No miRs correlated to 'PATHOLOGY_M_STAGE', 'GENDER', 'NUMBER_PACK_YEARS_SMOKED', 'RESIDUAL_TUMOR', 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=9   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=12 younger N=18
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=27 higher stage N=14 lower stage N=13
PATHOLOGY_N_STAGE Spearman correlation test N=26 higher stage N=16 lower stage N=10
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=18 lower score N=12
HISTOLOGICAL_TYPE Wilcoxon test N=30 esophagus squamous cell carcinoma N=30 esophagus adenocarcinoma, nos N=0
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=3 higher number_of_lymph_nodes N=2 lower number_of_lymph_nodes N=1
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

9 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) 0.1-122.1 (median=13.2)
  censored N = 106
  death N = 77
     
  Significant markers N = 9
  associated with shorter survival NA
  associated with longer survival NA
List of 9 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of 9 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-380 0.000188 0.098 0.499
HSA-MIR-1301 0.000577 0.15 0.607
HSA-MIR-425 0.00202 0.2 0.636
HSA-MIR-517A 0.00208 0.2 0.657
HSA-MIR-517B 0.00208 0.2 0.657
HSA-MIR-3647 0.00242 0.2 0.628
HSA-MIR-3655 0.00265 0.2 0.682
HSA-MIR-3150B 0.003 0.2 0.616
HSA-MIR-3653 0.00469 0.27 0.631
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) 62.53 (12)
  Significant markers N = 30
  pos. correlated 12
  neg. correlated 18
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-375 0.3875 5.506e-08 2.54e-05
HSA-MIR-944 -0.3867 9.705e-08 2.54e-05
HSA-MIR-708 -0.3307 4.544e-06 0.000697
HSA-MIR-149 -0.3284 5.328e-06 0.000697
HSA-MIR-193B -0.3087 2.006e-05 0.00178
HSA-MIR-452 -0.3085 2.041e-05 0.00178
HSA-MIR-29B-1 0.3023 3.028e-05 0.00196
HSA-MIR-205 -0.3024 3.179e-05 0.00196
HSA-MIR-29B-2 0.3007 3.37e-05 0.00196
HSA-MIR-147B 0.3125 4.368e-05 0.00228
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 8
  STAGE IA 5
  STAGE IB 6
  STAGE II 1
  STAGE IIA 46
  STAGE IIB 31
  STAGE III 26
  STAGE IIIA 14
  STAGE IIIB 9
  STAGE IIIC 7
  STAGE IV 5
  STAGE IVA 4
     
  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-205 1.786e-05 0.00934
HSA-MIR-192 4.216e-05 0.011
HSA-MIR-708 7.648e-05 0.0133
HSA-MIR-194-2 0.0001155 0.0134
HSA-MIR-194-1 0.0001285 0.0134
HSA-MIR-592 0.0002537 0.0198
HSA-MIR-944 0.0002882 0.0198
HSA-MIR-215 0.0003023 0.0198
HSA-MIR-375 0.0004401 0.0256
HSA-MIR-10A 0.0006926 0.0362
Clinical variable #4: 'PATHOLOGY_T_STAGE'

27 miRs related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.38 (0.84)
  N
  T0 1
  T1 31
  T2 43
  T3 87
  T4 5
     
  Significant markers N = 27
  pos. correlated 14
  neg. correlated 13
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-199A-1 0.2965 9.98e-05 0.0522
HSA-MIR-556 -0.3285 0.0002076 0.0543
HSA-MIR-199A-2 0.2743 0.0003335 0.0581
HSA-MIR-34B 0.2657 0.0007388 0.0828
HSA-MIR-206 -0.3035 0.0007921 0.0828
HSA-MIR-378 -0.2534 0.0009535 0.0831
HSA-MIR-708 0.2498 0.001129 0.0844
HSA-LET-7I 0.2439 0.001492 0.0975
HSA-MIR-125B-1 0.2385 0.001908 0.111
HSA-MIR-199B 0.2355 0.002191 0.115
Clinical variable #5: 'PATHOLOGY_N_STAGE'

26 miRs related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.71 (0.8)
  N
  N0 76
  N1 69
  N2 12
  N3 8
     
  Significant markers N = 26
  pos. correlated 16
  neg. correlated 10
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-192 0.2678 0.0005057 0.129
HSA-MIR-23A -0.2661 0.0005507 0.129
HSA-MIR-7-3 0.2743 0.0007415 0.129
HSA-MIR-194-1 0.2502 0.001189 0.13
HSA-MIR-194-2 0.2492 0.001247 0.13
HSA-MIR-365-2 -0.2425 0.001696 0.139
HSA-MIR-205 -0.2405 0.001859 0.139
HSA-MIR-27A -0.2344 0.002443 0.16
HSA-MIR-365-1 -0.2308 0.002861 0.16
HSA-LET-7B -0.2292 0.003063 0.16
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No miR related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 135
  class1 9
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 27
  MALE 157
     
  Significant markers N = 0
Clinical variable #8: 'RADIATION_THERAPY'

30 miRs related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 124
  YES 42
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

Table S14.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-338 1626 0.0002824 0.0888 0.6878
HSA-MIR-1266 1596 0.0006607 0.0888 0.6783
HSA-MIR-592 1478 0.0006851 0.0888 0.6815
HSA-MIR-196A-2 1640 0.0008209 0.0888 0.6748
HSA-MIR-375 1709 0.0008918 0.0888 0.6719
HSA-MIR-502 1719 0.001018 0.0888 0.6699
HSA-MIR-135A-1 894 0.001434 0.0951 0.6948
HSA-MIR-193B 3438 0.001961 0.0951 0.6601
HSA-MIR-196A-1 1773 0.002036 0.0951 0.6596
HSA-MIR-10A 1781 0.002249 0.0951 0.658
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

30 miRs related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 74.03 (16)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
List of top 10 miRs differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S16.  Get Full Table List of top 10 miRs significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-4326 -0.5423 6.366e-06 0.00333
HSA-MIR-107 0.4703 5.926e-05 0.0155
HSA-MIR-181C 0.4372 0.0002162 0.0286
HSA-MIR-26A-2 0.4369 0.0002184 0.0286
HSA-MIR-3691 0.5078 0.0005095 0.0533
HSA-MIR-365-2 -0.3955 0.0009241 0.0806
HSA-MIR-365-1 -0.387 0.001214 0.0834
HSA-MIR-1248 -0.3883 0.001276 0.0834
HSA-MIR-16-1 0.3794 0.001543 0.0896
HSA-MIR-1304 0.392 0.002136 0.11
Clinical variable #10: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  ESOPHAGUS ADENOCARCINOMA, NOS 89
  ESOPHAGUS SQUAMOUS CELL CARCINOMA 95
     
  Significant markers N = 30
  Higher in ESOPHAGUS SQUAMOUS CELL CARCINOMA 30
  Higher in ESOPHAGUS ADENOCARCINOMA, NOS 0
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Clinical variable #11: 'NUMBER_PACK_YEARS_SMOKED'

No miR related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 34.68 (22)
  Significant markers N = 0
Clinical variable #12: 'RESIDUAL_TUMOR'

No miR related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 136
  R1 13
  R2 2
  RX 7
     
  Significant markers N = 0
Clinical variable #13: 'NUMBER_OF_LYMPH_NODES'

3 miRs related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.53 (3.4)
  Significant markers N = 3
  pos. correlated 2
  neg. correlated 1
List of 3 miRs differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S22.  Get Full Table List of 3 miRs significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-421 0.2934 0.0005531 0.25
HSA-MIR-202 -0.3815 0.00112 0.25
HSA-MIR-641 0.3845 0.001436 0.25
Clinical variable #14: 'RACE'

30 miRs related to 'RACE'.

Table S23.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 45
  BLACK OR AFRICAN AMERICAN 5
  WHITE 114
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RACE'

Table S24.  Get Full Table List of top 10 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-149 1.831e-10 6.67e-08
HSA-MIR-205 2.551e-10 6.67e-08
HSA-MIR-944 1.926e-09 3.36e-07
HSA-MIR-194-1 2.635e-09 3.45e-07
HSA-MIR-224 5.015e-09 5.25e-07
HSA-MIR-194-2 7.219e-09 6.29e-07
HSA-MIR-192 1.398e-08 1.04e-06
HSA-MIR-193B 7.602e-08 4.97e-06
HSA-MIR-708 1.602e-07 9.31e-06
HSA-MIR-215 2.546e-07 1.33e-05
Clinical variable #15: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S25.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 6
  NOT HISPANIC OR LATINO 87
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 184

  • Number of miRs = 523

  • Number of clinical features = 15

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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