Correlation between miRseq expression and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C13R0S6S
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/C13R0S6S
Overview
Introduction

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

Summary

Testing the association between 499 miRs and 12 clinical features across 1077 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 11 clinical features related to at least one miRs.

  • 9 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-22 ,  HSA-MIR-30A ,  HSA-MIR-31 ,  HSA-LET-7B ,  HSA-MIR-3622A ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-31 ,  HSA-MIR-424 ,  HSA-MIR-381 ,  HSA-MIR-598 ,  HSA-MIR-99A ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-210 ,  HSA-MIR-499 ,  HSA-MIR-99A ,  HSA-MIR-222 ,  HSA-MIR-125B-1 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-758 ,  HSA-MIR-9-2 ,  HSA-MIR-127 ,  HSA-MIR-9-1 ,  HSA-MIR-409 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-10A ,  HSA-MIR-577 ,  HSA-MIR-3613 ,  HSA-MIR-141 ,  HSA-MIR-548B ,  ...

  • 1 miR correlated to 'PATHOLOGY_M_STAGE'.

    • HSA-MIR-374C

  • 13 miRs correlated to 'GENDER'.

    • HSA-MIR-29A ,  HSA-MIR-223 ,  HSA-MIR-454 ,  HSA-MIR-194-1 ,  HSA-MIR-3199-2 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-210 ,  HSA-MIR-1306 ,  HSA-MIR-301A ,  HSA-MIR-301B ,  HSA-MIR-130B ,  ...

  • 30 miRs correlated to 'NUMBER_OF_LYMPH_NODES'.

    • HSA-MIR-577 ,  HSA-MIR-10A ,  HSA-MIR-3613 ,  HSA-MIR-141 ,  HSA-LET-7G ,  ...

  • 30 miRs correlated to 'RACE'.

    • HSA-MIR-660 ,  HSA-MIR-20A ,  HSA-MIR-93 ,  HSA-MIR-1304 ,  HSA-MIR-103-1 ,  ...

  • 5 miRs correlated to 'ETHNICITY'.

    • HSA-MIR-23B ,  HSA-MIR-3934 ,  HSA-MIR-2110 ,  HSA-MIR-27B ,  HSA-MIR-23A

  • No miRs correlated to '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=9   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=7 younger N=23
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=3 lower stage N=27
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=2 lower stage N=28
PATHOLOGY_M_STAGE Wilcoxon test N=1 class1 N=1 class0 N=0
GENDER Wilcoxon test N=13 male N=13 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=3 lower number_of_lymph_nodes N=27
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test N=5 not hispanic or latino N=5 hispanic or latino 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) 0-282.9 (median=28.1)
  censored N = 928
  death N = 148
     
  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-22 0.000278 0.14 0.585
HSA-MIR-30A 0.00154 0.28 0.399
HSA-MIR-31 0.00168 0.28 0.422
HSA-LET-7B 0.00313 0.28 0.416
HSA-MIR-3622A 0.00368 0.28 0.577
HSA-MIR-618 0.00403 0.28 0.357
HSA-MIR-874 0.00434 0.28 0.574
HSA-MIR-1307 0.00465 0.28 0.592
HSA-MIR-1224 0.00506 0.28 0.548
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) 58.63 (13)
  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-31 -0.211 1.012e-11 5.05e-09
HSA-MIR-424 -0.2031 2.393e-11 5.97e-09
HSA-MIR-381 -0.1943 1.76e-10 2.93e-08
HSA-MIR-598 -0.1916 3.104e-10 3.87e-08
HSA-MIR-99A -0.1895 4.858e-10 4.85e-08
HSA-MIR-652 -0.1849 1.288e-09 1.07e-07
HSA-LET-7C -0.1681 3.583e-08 2.4e-06
HSA-MIR-542 -0.1677 3.851e-08 2.4e-06
HSA-MIR-375 0.1604 1.487e-07 8.25e-06
HSA-MIR-202 -0.1959 1.851e-07 9.24e-06
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 89
  STAGE IA 85
  STAGE IB 7
  STAGE II 6
  STAGE IIA 353
  STAGE IIB 250
  STAGE III 2
  STAGE IIIA 153
  STAGE IIIB 26
  STAGE IIIC 64
  STAGE IV 20
  STAGE X 14
     
  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-210 1.759e-05 0.00878
HSA-MIR-499 4.265e-05 0.01
HSA-MIR-99A 6.01e-05 0.01
HSA-MIR-222 0.0001679 0.0174
HSA-MIR-125B-1 0.0002693 0.0174
HSA-MIR-130B 0.0002851 0.0174
HSA-LET-7F-2 0.0002867 0.0174
HSA-MIR-125B-2 0.0002902 0.0174
HSA-MIR-374C 0.0003137 0.0174
HSA-MIR-301A 0.000374 0.0178
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 miRs related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.94 (0.73)
  N
  T1 279
  T2 620
  T3 135
  T4 40
     
  Significant markers N = 30
  pos. correlated 3
  neg. correlated 27
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-758 -0.1276 2.82e-05 0.0141
HSA-MIR-9-2 0.1069 0.0004511 0.0573
HSA-MIR-127 -0.1055 0.0005334 0.0573
HSA-MIR-9-1 0.1044 0.0006133 0.0573
HSA-MIR-409 -0.1031 0.0007231 0.0573
HSA-MIR-374A -0.1019 0.0008204 0.0573
HSA-MIR-1287 -0.1019 0.0008295 0.0573
HSA-MIR-1976 -0.101 0.0009187 0.0573
HSA-MIR-382 -0.097 0.001463 0.07
HSA-MIR-134 -0.0964 0.001555 0.07
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 miRs related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.77 (0.91)
  N
  N0 508
  N1 356
  N2 118
  N3 75
     
  Significant markers N = 30
  pos. correlated 2
  neg. correlated 28
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-10A 0.1419 3.636e-06 0.00181
HSA-MIR-577 -0.141 4.794e-05 0.012
HSA-MIR-3613 -0.1192 0.000102 0.017
HSA-MIR-141 -0.1138 0.0002106 0.0256
HSA-MIR-548B -0.1264 0.0003178 0.0256
HSA-MIR-92A-2 -0.1104 0.0003214 0.0256
HSA-MIR-30A -0.1096 0.0003588 0.0256
HSA-MIR-455 -0.1078 0.0004482 0.028
HSA-MIR-17 -0.1025 0.0008424 0.038
HSA-MIR-30C-2 -0.1018 0.0009199 0.038
Clinical variable #6: 'PATHOLOGY_M_STAGE'

One miR related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 887
  class1 21
     
  Significant markers N = 1
  Higher in class1 1
  Higher in class0 0
List of one miR differentially expressed by 'PATHOLOGY_M_STAGE'

Table S12.  Get Full Table List of one miR differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
HSA-MIR-374C 3586 0.0001309 0.0653 0.8538
Clinical variable #7: 'GENDER'

13 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 1065
  MALE 12
     
  Significant markers N = 13
  Higher in MALE 13
  Higher in FEMALE 0
List of top 10 miRs differentially expressed by 'GENDER'

Table S14.  Get Full Table List of top 10 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-29A 2471 0.0002551 0.127 0.8067
HSA-MIR-223 2782 0.0007604 0.19 0.7823
HSA-MIR-454 9733 0.001811 0.195 0.7616
HSA-MIR-194-1 3057 0.00187 0.195 0.7608
HSA-MIR-3199-2 146 0.001956 0.195 0.9487
HSA-MIR-378C 3116 0.002633 0.207 0.7522
HSA-MIR-21 9548 0.00321 0.207 0.7471
HSA-MIR-194-2 3243 0.003318 0.207 0.7462
HSA-MIR-887 9287 0.005486 0.297 0.7329
HSA-MIR-129-1 1396 0.006779 0.297 0.778
Clinical variable #8: 'RADIATION_THERAPY'

No miR related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 441
  YES 543
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  INFILTRATING CARCINOMA NOS 1
  INFILTRATING DUCTAL CARCINOMA 768
  INFILTRATING LOBULAR CARCINOMA 200
  MEDULLARY CARCINOMA 6
  METAPLASTIC CARCINOMA 9
  MIXED HISTOLOGY (PLEASE SPECIFY) 29
  MUCINOUS CARCINOMA 17
  OTHER, SPECIFY 46
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S17.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
HSA-MIR-210 1.263e-46 6.3e-44
HSA-MIR-1306 7.207e-39 1.8e-36
HSA-MIR-301A 2.833e-38 4.71e-36
HSA-MIR-301B 6.407e-37 6.47e-35
HSA-MIR-130B 6.48e-37 6.47e-35
HSA-MIR-197 1.747e-36 1.32e-34
HSA-MIR-328 1.859e-36 1.32e-34
HSA-MIR-345 2.518e-36 1.57e-34
HSA-MIR-505 1.308e-35 7.25e-34
HSA-MIR-616 1.769e-35 8.83e-34
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

30 miRs related to 'NUMBER_OF_LYMPH_NODES'.

Table S18.  Basic characteristics of clinical feature: 'NUMBER_OF_LYMPH_NODES'

NUMBER_OF_LYMPH_NODES Mean (SD) 2.37 (4.6)
  Significant markers N = 30
  pos. correlated 3
  neg. correlated 27
List of top 10 miRs differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S19.  Get Full Table List of top 10 miRs significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-577 -0.1609 1.423e-05 0.0071
HSA-MIR-10A 0.1334 5.195e-05 0.013
HSA-MIR-3613 -0.1288 9.397e-05 0.0156
HSA-MIR-141 -0.1236 0.0001774 0.0221
HSA-LET-7G -0.1175 0.0003685 0.0308
HSA-MIR-221 -0.117 0.0003895 0.0308
HSA-MIR-148A -0.1161 0.0004323 0.0308
HSA-MIR-2114 -0.1438 0.0005035 0.0314
HSA-MIR-19B-2 -0.1121 0.0006834 0.0338
HSA-MIR-17 -0.1118 0.0007041 0.0338
Clinical variable #11: 'RACE'

30 miRs related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 61
  BLACK OR AFRICAN AMERICAN 182
  WHITE 745
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RACE'

Table S21.  Get Full Table List of top 10 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-660 3.35e-23 1.67e-20
HSA-MIR-20A 4.246e-20 1.06e-17
HSA-MIR-93 3.837e-19 6.38e-17
HSA-MIR-1304 2.152e-17 2.68e-15
HSA-MIR-103-1 6.367e-17 6.35e-15
HSA-MIR-361 1.564e-15 1.28e-13
HSA-MIR-17 1.794e-15 1.28e-13
HSA-MIR-26A-1 2.694e-15 1.68e-13
HSA-MIR-185 5.952e-14 3.3e-12
HSA-MIR-3615 3.7e-13 1.85e-11
Clinical variable #12: 'ETHNICITY'

5 miRs related to 'ETHNICITY'.

Table S22.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 39
  NOT HISPANIC OR LATINO 873
     
  Significant markers N = 5
  Higher in NOT HISPANIC OR LATINO 5
  Higher in HISPANIC OR LATINO 0
List of 5 miRs differentially expressed by 'ETHNICITY'

Table S23.  Get Full Table List of 5 miRs differentially expressed by 'ETHNICITY'

W(pos if higher in 'NOT HISPANIC OR LATINO') wilcoxontestP Q AUC
HSA-MIR-23B c("22110", "0.001577") c("22110", "0.001577") 0.257 0.6494
HSA-MIR-3934 c("19210", "0.001853") c("19210", "0.001853") 0.257 0.6531
HSA-MIR-2110 c("20843", "0.002294") c("20843", "0.002294") 0.257 0.6461
HSA-MIR-27B c("21909", "0.002404") c("21909", "0.002404") 0.257 0.6435
HSA-MIR-23A c("21876", "0.002573") c("21876", "0.002573") 0.257 0.6425
Methods & Data
Input

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

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

  • Number of patients = 1077

  • Number of miRs = 499

  • Number of clinical features = 12

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)
Copyright © 2016 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle