Correlation between miRseq expression and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C1377845
Overview
Introduction

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

Summary

Testing the association between 544 miRs and 12 clinical features across 372 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one miRs.

  • 30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-149 ,  HSA-MIR-489 ,  HSA-MIR-3677 ,  HSA-MIR-658 ,  HSA-MIR-632 ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-1269 ,  HSA-MIR-412 ,  HSA-MIR-181D ,  HSA-MIR-200C ,  HSA-MIR-889 ,  ...

  • 21 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-550A-1 ,  HSA-MIR-139 ,  HSA-MIR-642A ,  HSA-MIR-23C ,  HSA-MIR-194-1 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-550A-1 ,  HSA-MIR-139 ,  HSA-MIR-23C ,  HSA-MIR-149 ,  HSA-MIR-550A-2 ,  ...

  • 30 miRs correlated to 'GENDER'.

    • HSA-MIR-26A-2 ,  HSA-MIR-331 ,  HSA-MIR-375 ,  HSA-MIR-106A ,  HSA-MIR-1266 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-194-1 ,  HSA-MIR-194-2 ,  HSA-MIR-192 ,  HSA-MIR-10A ,  HSA-MIR-122 ,  ...

  • 30 miRs correlated to 'RACE'.

    • HSA-MIR-23C ,  HSA-MIR-3130-1 ,  HSA-MIR-532 ,  HSA-MIR-30D ,  HSA-MIR-1304 ,  ...

  • 5 miRs correlated to 'ETHNICITY'.

    • HSA-MIR-19A ,  HSA-MIR-618 ,  HSA-MIR-340 ,  HSA-MIR-3607 ,  HSA-MIR-508

  • No miRs correlated to 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', and 'RESIDUAL_TUMOR'.

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=5 younger N=25
PATHOLOGIC_STAGE Kruskal-Wallis test N=21        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=23 lower stage N=7
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
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'

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) 0-120.8 (median=19.8)
  censored N = 244
  death N = 127
     
  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-149 5.37e-08 2.9e-05 0.641
HSA-MIR-489 1.44e-07 3.9e-05 0.654
HSA-MIR-3677 2.2e-06 4e-04 0.654
HSA-MIR-658 6.66e-06 0.00077 0.629
HSA-MIR-632 7.05e-06 0.00077 0.641
HSA-MIR-139 1.35e-05 0.0012 0.355
HSA-MIR-212 2.13e-05 0.0017 0.611
HSA-MIR-100 2.76e-05 0.0019 0.395
HSA-MIR-3610 4.52e-05 0.0027 0.561
HSA-MIR-3680 5.54e-05 0.003 0.622
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) 59.22 (13)
  Significant markers N = 30
  pos. correlated 5
  neg. correlated 25
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-1269 0.262 4.261e-07 0.000232
HSA-MIR-412 -0.2425 3.678e-06 0.001
HSA-MIR-181D -0.2327 6.467e-06 0.00117
HSA-MIR-200C -0.2139 3.519e-05 0.00319
HSA-MIR-889 -0.2125 4.066e-05 0.00319
HSA-LET-7E -0.2116 4.277e-05 0.00319
HSA-MIR-483 -0.2124 4.412e-05 0.00319
HSA-MIR-296 -0.2173 4.697e-05 0.00319
HSA-MIR-181B-1 -0.2087 5.463e-05 0.0033
HSA-MIR-98 -0.2059 6.926e-05 0.00368
Clinical variable #3: 'PATHOLOGIC_STAGE'

21 miRs related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 172
  STAGE II 86
  STAGE III 3
  STAGE IIIA 64
  STAGE IIIB 9
  STAGE IIIC 9
  STAGE IV 2
  STAGE IVA 1
  STAGE IVB 2
     
  Significant markers N = 21
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-550A-1 0.0001239 0.056
HSA-MIR-139 0.0002869 0.056
HSA-MIR-642A 0.0003087 0.056
HSA-MIR-23C 0.000561 0.0625
HSA-MIR-194-1 0.0005743 0.0625
HSA-MIR-194-2 0.0007317 0.0663
HSA-MIR-7-2 0.001787 0.126
HSA-MIR-550A-2 0.001858 0.126
HSA-MIR-210 0.002228 0.135
HSA-MIR-346 0.003197 0.174
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) 1.79 (0.9)
  N
  T1 182
  T2 94
  T3 80
  T4 13
     
  Significant markers N = 30
  pos. correlated 23
  neg. correlated 7
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-550A-1 0.2647 2.576e-07 0.000116
HSA-MIR-139 -0.2586 4.72e-07 0.000116
HSA-MIR-23C -0.2704 6.401e-07 0.000116
HSA-MIR-149 0.23 8.559e-06 0.00116
HSA-MIR-550A-2 0.2179 2.475e-05 0.00247
HSA-MIR-194-1 -0.2141 3.38e-05 0.00247
HSA-MIR-22 -0.2137 3.501e-05 0.00247
HSA-MIR-194-2 -0.2132 3.627e-05 0.00247
HSA-MIR-122 -0.2086 5.378e-05 0.00325
HSA-MIR-642A 0.2153 8.09e-05 0.00429
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No miR related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 254
  N1 4
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No miR related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 269
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 119
  MALE 253
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 miRs differentially expressed by 'GENDER'

Table S12.  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-26A-2 11083 4.063e-05 0.0113 0.6319
HSA-MIR-331 11088 4.155e-05 0.0113 0.6317
HSA-MIR-375 11275 9.407e-05 0.0162 0.6255
HSA-MIR-106A 11342 0.000125 0.0162 0.6233
HSA-MIR-1266 11407 0.0001994 0.0162 0.6196
HSA-MIR-363 11485 0.0002257 0.0162 0.6185
HSA-MIR-1301 11488 0.0002285 0.0162 0.6184
HSA-MIR-122 18594 0.0002528 0.0162 0.6176
HSA-MIR-182 11560 0.0003053 0.0162 0.616
HSA-MIR-3065 11581 0.0003319 0.0162 0.6153
Clinical variable #8: 'RADIATION_THERAPY'

No miR related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 340
  YES 9
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  FIBROLAMELLAR CARCINOMA 3
  HEPATOCELLULAR CARCINOMA 362
  HEPATOCHOLANGIOCARCINOMA (MIXED) 7
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S15.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
HSA-MIR-194-1 0.0001281 0.0319
HSA-MIR-194-2 0.00016 0.0319
HSA-MIR-192 0.0001761 0.0319
HSA-MIR-10A 0.0002879 0.0391
HSA-MIR-122 0.0003802 0.0414
HSA-MIR-214 0.001017 0.0922
HSA-MIR-200B 0.001693 0.101
HSA-MIR-375 0.001853 0.101
HSA-MIR-708 0.001871 0.101
HSA-MIR-27A 0.002167 0.101
Clinical variable #10: 'RESIDUAL_TUMOR'

No miR related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 325
  R1 17
  R2 1
  RX 22
     
  Significant markers N = 0
Clinical variable #11: 'RACE'

30 miRs related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 2
  ASIAN 161
  BLACK OR AFRICAN AMERICAN 17
  WHITE 182
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RACE'

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

kruskal_wallis_P Q
HSA-MIR-23C 7.755e-15 4.22e-12
HSA-MIR-3130-1 3.082e-12 8.38e-10
HSA-MIR-532 6.074e-07 0.00011
HSA-MIR-30D 1.367e-06 0.000186
HSA-MIR-1304 2.484e-05 0.0027
HSA-MIR-627 3.709e-05 0.00336
HSA-MIR-511-1 5.525e-05 0.00374
HSA-MIR-511-2 6.473e-05 0.00374
HSA-MIR-548J 6.566e-05 0.00374
HSA-MIR-26B 6.879e-05 0.00374
Clinical variable #12: 'ETHNICITY'

5 miRs related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 18
  NOT HISPANIC OR LATINO 335
     
  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 S20.  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-19A c("1440", "0.0001891") c("1440", "0.0001891") 0.103 0.7612
HSA-MIR-618 c("1546", "0.001034") c("1546", "0.001034") 0.184 0.7299
HSA-MIR-340 c("1637", "0.001091") c("1637", "0.001091") 0.184 0.7285
HSA-MIR-3607 c("4367", "0.001353") c("4367", "0.001353") 0.184 0.7242
HSA-MIR-508 c("4244", "0.002674") c("4244", "0.002674") 0.291 0.7102
Methods & Data
Input
  • Expresson data file = LIHC-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 372

  • Number of miRs = 544

  • 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)