Correlation between miRseq expression and clinical features
Colorectal Adenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C11V5D53
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/C11V5D53
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 420 miRs and 13 clinical features across 549 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 11 clinical features related to at least one miRs.

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-432 ,  HSA-MIR-141 ,  HSA-MIR-153-2 ,  HSA-MIR-26A-1 ,  HSA-MIR-410 ,  ...

  • 30 miRs correlated to 'TUMOR_TISSUE_SITE'.

    • HSA-MIR-10B ,  HSA-MIR-1201 ,  HSA-MIR-30C-2 ,  HSA-MIR-425 ,  HSA-MIR-1259 ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-625 ,  HSA-MIR-675 ,  HSA-MIR-616 ,  HSA-MIR-143 ,  HSA-MIR-106A ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-206 ,  HSA-MIR-501 ,  HSA-MIR-500 ,  HSA-MIR-144 ,  HSA-MIR-191 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-625 ,  HSA-MIR-146A ,  HSA-MIR-217 ,  HSA-MIR-511-1 ,  HSA-MIR-1-2 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_M_STAGE'.

    • HSA-MIR-625 ,  HSA-MIR-146A ,  HSA-MIR-589 ,  HSA-MIR-629 ,  HSA-MIR-1249 ,  ...

  • 1 miR correlated to 'GENDER'.

    • HSA-MIR-651

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-10B ,  HSA-MIR-1201 ,  HSA-MIR-30C-2 ,  HSA-MIR-20A ,  HSA-MIR-92A-1 ,  ...

  • 30 miRs correlated to 'RESIDUAL_TUMOR'.

    • HSA-MIR-330 ,  HSA-MIR-199B ,  HSA-MIR-136 ,  HSA-MIR-126 ,  HSA-MIR-1180 ,  ...

  • 30 miRs correlated to 'NUMBER_OF_LYMPH_NODES'.

    • HSA-MIR-146A ,  HSA-MIR-511-1 ,  HSA-MIR-625 ,  HSA-MIR-223 ,  HSA-MIR-146B ,  ...

  • 17 miRs correlated to 'RACE'.

    • HSA-MIR-1304 ,  HSA-MIR-412 ,  HSA-MIR-15A ,  HSA-MIR-30A ,  HSA-LET-7I ,  ...

  • No miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', and 'RADIATION_THERAPY'.

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=14 younger N=16
TUMOR_TISSUE_SITE Wilcoxon test N=30 rectum N=30 colon N=0
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=4 lower stage N=26
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=6 lower stage N=24
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=1 male N=1 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=4 lower number_of_lymph_nodes N=26
RACE Kruskal-Wallis test N=17        
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) 0-148 (median=21)
  censored N = 439
  death N = 109
     
  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) 66.84 (13)
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
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-432 -0.2232 1.517e-07 6.37e-05
HSA-MIR-141 0.1995 2.524e-06 0.000418
HSA-MIR-153-2 0.198 2.983e-06 0.000418
HSA-MIR-26A-1 0.1898 7.702e-06 0.000809
HSA-MIR-410 -0.1779 3.115e-05 0.00262
HSA-MIR-411 -0.1699 7.051e-05 0.00494
HSA-MIR-493 -0.1629 0.0001279 0.00669
HSA-MIR-431 -0.1618 0.0001423 0.00669
HSA-MIR-653 0.1635 0.0001435 0.00669
HSA-MIR-616 0.1614 0.0001637 0.00687
Clinical variable #3: 'TUMOR_TISSUE_SITE'

30 miRs related to 'TUMOR_TISSUE_SITE'.

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

TUMOR_TISSUE_SITE Labels N
  COLON 405
  RECTUM 140
     
  Significant markers N = 30
  Higher in RECTUM 30
  Higher in COLON 0
List of top 10 miRs differentially expressed by 'TUMOR_TISSUE_SITE'

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

W(pos if higher in 'RECTUM') wilcoxontestP Q AUC
HSA-MIR-10B 14346 2.82e-18 1.18e-15 0.747
HSA-MIR-1201 41462 3.265e-16 6.86e-14 0.7313
HSA-MIR-30C-2 40396 6.41e-14 8.97e-12 0.7125
HSA-MIR-425 39037 2.866e-11 3.01e-09 0.6885
HSA-MIR-1259 38954 4.066e-11 3.42e-09 0.687
HSA-MIR-191 38281 6.304e-10 4.41e-08 0.6751
HSA-MIR-1977 37985 1.993e-09 1.2e-07 0.6699
HSA-MIR-20A 37931 2.45e-09 1.29e-07 0.669
HSA-LET-7G 37832 3.567e-09 1.66e-07 0.6672
HSA-MIR-1274B 18254 1.362e-08 5.72e-07 0.6627
Clinical variable #4: 'PATHOLOGIC_STAGE'

30 miRs related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 94
  STAGE IA 1
  STAGE II 36
  STAGE IIA 154
  STAGE IIB 10
  STAGE IIC 2
  STAGE III 27
  STAGE IIIA 13
  STAGE IIIB 72
  STAGE IIIC 46
  STAGE IV 57
  STAGE IVA 22
  STAGE IVB 1
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'PATHOLOGIC_STAGE'

Table S7.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
HSA-MIR-625 7.919e-07 0.000333
HSA-MIR-675 3.152e-05 0.00662
HSA-MIR-616 0.0001085 0.0109
HSA-MIR-143 0.0001237 0.0109
HSA-MIR-106A 0.0001391 0.0109
HSA-LET-7F-2 0.0001564 0.0109
HSA-LET-7E 0.0002718 0.0155
HSA-MIR-141 0.00033 0.0155
HSA-MIR-589 0.0003758 0.0155
HSA-MIR-581 0.0003759 0.0155
Clinical variable #5: 'PATHOLOGY_T_STAGE'

30 miRs related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.86 (0.63)
  N
  T1 20
  T2 94
  T3 377
  T4 56
     
  Significant markers N = 30
  pos. correlated 4
  neg. correlated 26
List of top 10 miRs differentially expressed by 'PATHOLOGY_T_STAGE'

Table S9.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-206 -0.2351 1.749e-05 0.0053
HSA-MIR-501 -0.1791 2.525e-05 0.0053
HSA-MIR-500 -0.1711 5.777e-05 0.00809
HSA-MIR-144 -0.1673 8.407e-05 0.00854
HSA-MIR-191 -0.1654 0.0001017 0.00854
HSA-MIR-502 -0.1588 0.0001914 0.0116
HSA-MIR-362 -0.1588 0.0001927 0.0116
HSA-MIR-34C 0.1604 0.0002277 0.012
HSA-MIR-148B -0.153 0.0003292 0.0153
HSA-MIR-2110 -0.1538 0.000375 0.0153
Clinical variable #6: 'PATHOLOGY_N_STAGE'

30 miRs related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.61 (0.78)
  N
  N0 312
  N1 134
  N2 100
     
  Significant markers N = 30
  pos. correlated 6
  neg. correlated 24
List of top 10 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

Table S11.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-625 -0.1803 2.258e-05 0.00948
HSA-MIR-146A -0.1713 5.756e-05 0.0121
HSA-MIR-217 0.1559 0.0002554 0.0273
HSA-MIR-511-1 -0.1554 0.0002861 0.0273
HSA-MIR-1-2 0.152 0.0003632 0.0273
HSA-MIR-589 -0.1512 0.0003904 0.0273
HSA-MIR-629 -0.1444 0.0007169 0.0372
HSA-MIR-506 -0.2575 0.0007254 0.0372
HSA-MIR-146B -0.1431 0.0007962 0.0372
HSA-MIR-141 -0.1395 0.001083 0.0422
Clinical variable #7: 'PATHOLOGY_M_STAGE'

30 miRs related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 412
  class1 78
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 miRs differentially expressed by 'PATHOLOGY_M_STAGE'

Table S13.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
HSA-MIR-625 11979 0.0003632 0.0735 0.6272
HSA-MIR-146A 12012 0.0004052 0.0735 0.6262
HSA-MIR-589 12091 0.0005247 0.0735 0.6238
HSA-MIR-629 12315 0.001066 0.112 0.6168
HSA-MIR-1249 10281 0.001485 0.125 0.6182
HSA-MIR-146B 12609 0.002561 0.141 0.6076
HSA-MIR-886 12618 0.002628 0.141 0.6074
HSA-MIR-511-1 12321 0.00268 0.141 0.6078
HSA-MIR-1180 12546 0.003062 0.143 0.6058
HSA-MIR-155 12713 0.00344 0.144 0.6044
Clinical variable #8: 'GENDER'

One miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 257
  MALE 292
     
  Significant markers N = 1
  Higher in MALE 1
  Higher in FEMALE 0
List of one miR differentially expressed by 'GENDER'

Table S15.  Get Full Table List of one miR 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 29094 6.504e-05 0.0273 0.5995
Clinical variable #9: 'RADIATION_THERAPY'

No miR related to 'RADIATION_THERAPY'.

Table S16.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 399
  YES 25
     
  Significant markers N = 0
Clinical variable #10: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  COLON ADENOCARCINOMA 348
  COLON MUCINOUS ADENOCARCINOMA 53
  RECTAL ADENOCARCINOMA 127
  RECTAL MUCINOUS ADENOCARCINOMA 10
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S18.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
HSA-MIR-10B 7.051e-17 2.96e-14
HSA-MIR-1201 9.34e-15 1.96e-12
HSA-MIR-30C-2 2.282e-12 3.2e-10
HSA-MIR-20A 2.736e-11 2.87e-09
HSA-MIR-92A-1 4.343e-11 3.65e-09
HSA-MIR-425 5.888e-11 4.12e-09
HSA-MIR-1259 1.29e-10 7.74e-09
HSA-LET-7G 1.634e-10 8.58e-09
HSA-MIR-592 3.479e-10 1.62e-08
HSA-MIR-1977 2.19e-09 8.5e-08
Clinical variable #11: 'RESIDUAL_TUMOR'

30 miRs related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 384
  R1 5
  R2 36
  RX 26
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RESIDUAL_TUMOR'

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

kruskal_wallis_P Q
HSA-MIR-330 1.756e-08 4.67e-06
HSA-MIR-199B 4.404e-08 4.67e-06
HSA-MIR-136 5.042e-08 4.67e-06
HSA-MIR-126 6.41e-08 4.67e-06
HSA-MIR-1180 6.661e-08 4.67e-06
HSA-LET-7A-1 8.048e-08 4.67e-06
HSA-MIR-590 8.652e-08 4.67e-06
HSA-LET-7A-2 8.886e-08 4.67e-06
HSA-LET-7A-3 1.002e-07 4.68e-06
HSA-MIR-16-1 1.211e-07 5.09e-06
Clinical variable #12: 'NUMBER_OF_LYMPH_NODES'

30 miRs related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.19 (4.7)
  Significant markers N = 30
  pos. correlated 4
  neg. correlated 26
List of top 10 miRs differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S22.  Get Full Table List of top 10 miRs significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-146A -0.176 6.001e-05 0.0168
HSA-MIR-511-1 -0.1739 7.993e-05 0.0168
HSA-MIR-625 -0.1653 0.0001674 0.0234
HSA-MIR-223 -0.161 0.0002469 0.0259
HSA-MIR-146B -0.1517 0.0005565 0.0434
HSA-MIR-128-2 -0.1487 0.0007179 0.0434
HSA-MIR-511-2 -0.1481 0.0008068 0.0434
HSA-MIR-34A -0.147 0.000826 0.0434
HSA-MIR-128-1 -0.1455 0.0009353 0.0436
HSA-MIR-1-2 0.1435 0.001109 0.0437
Clinical variable #13: 'RACE'

17 miRs related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 12
  BLACK OR AFRICAN AMERICAN 26
  WHITE 280
     
  Significant markers N = 17
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-1304 4.082e-06 0.00171
HSA-MIR-412 4.502e-05 0.00945
HSA-MIR-15A 9.473e-05 0.0133
HSA-MIR-30A 0.001633 0.118
HSA-LET-7I 0.001702 0.118
HSA-MIR-16-1 0.001926 0.118
HSA-MIR-26B 0.001995 0.118
HSA-MIR-29B-1 0.002449 0.118
HSA-MIR-29B-2 0.002526 0.118
HSA-MIR-376A-1 0.004598 0.182
Methods & Data
Input

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

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

  • Number of patients = 549

  • Number of miRs = 420

  • Number of clinical features = 13

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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