Correlation between miRseq expression and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C16H4GVM
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/C16H4GVM
Overview
Introduction

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

Summary

Testing the association between 545 miRs and 15 clinical features across 478 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one miRs.

  • 1 miR correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-183

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-99A ,  HSA-LET-7C ,  HSA-MIR-3648 ,  HSA-MIR-10A ,  HSA-MIR-125B-2 ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-30A ,  HSA-MIR-126 ,  HSA-MIR-23C ,  HSA-MIR-361 ,  HSA-MIR-545 ,  ...

  • 7 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-30E ,  HSA-MIR-101-1 ,  HSA-MIR-150 ,  HSA-MIR-509-1 ,  HSA-MIR-504 ,  ...

  • 6 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-20B ,  HSA-MIR-363 ,  HSA-MIR-3136 ,  HSA-MIR-3687 ,  HSA-MIR-454 ,  ...

  • 7 miRs correlated to 'GENDER'.

    • HSA-MIR-375 ,  HSA-MIR-511-2 ,  HSA-MIR-146A ,  HSA-MIR-766 ,  HSA-MIR-511-1 ,  ...

  • 30 miRs correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • HSA-MIR-26A-1 ,  HSA-MIR-140 ,  HSA-MIR-628 ,  HSA-MIR-186 ,  HSA-MIR-616 ,  ...

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-17 ,  HSA-MIR-29A ,  HSA-MIR-92A-1 ,  HSA-MIR-92A-2 ,  HSA-MIR-3200 ,  ...

  • 30 miRs correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

    • HSA-MIR-99B ,  HSA-MIR-337 ,  HSA-MIR-125A ,  HSA-MIR-421 ,  HSA-MIR-653 ,  ...

  • 5 miRs correlated to 'RACE'.

    • HSA-MIR-1304 ,  HSA-MIR-1269 ,  HSA-MIR-412 ,  HSA-MIR-2277 ,  HSA-MIR-3130-1

  • No miRs correlated to 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', '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=1   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=7 higher stage N=0 lower stage N=7
PATHOLOGY_N_STAGE Spearman correlation test N=6 higher stage N=5 lower stage N=1
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=7 male N=7 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=3 lower score N=27
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test N=30 higher year_of_tobacco_smoking_onset N=8 lower year_of_tobacco_smoking_onset N=22
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
RACE Kruskal-Wallis test N=5        
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) 0-173.8 (median=22)
  censored N = 274
  death N = 203
     
  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-183 8.24e-05 0.045 0.454
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) 67.39 (8.6)
  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-99A 0.2064 6.598e-06 0.0022
HSA-LET-7C 0.2044 8.081e-06 0.0022
HSA-MIR-3648 -0.1924 2.966e-05 0.00539
HSA-MIR-10A 0.1818 7.472e-05 0.0092
HSA-MIR-125B-2 0.1781 0.0001068 0.0092
HSA-MIR-3687 -0.1803 0.0001184 0.0092
HSA-MIR-466 -0.2467 0.0001328 0.0092
HSA-MIR-3074 -0.1754 0.0001351 0.0092
HSA-MIR-421 -0.1672 0.0002922 0.0166
HSA-MIR-34C 0.1661 0.0003038 0.0166
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 3
  STAGE IA 83
  STAGE IB 144
  STAGE II 3
  STAGE IIA 62
  STAGE IIB 93
  STAGE III 3
  STAGE IIIA 61
  STAGE IIIB 16
  STAGE IV 6
     
  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-30A 6.19e-05 0.0337
HSA-MIR-126 0.0001508 0.0411
HSA-MIR-23C 0.0005062 0.092
HSA-MIR-361 0.001218 0.143
HSA-MIR-545 0.001492 0.143
HSA-MIR-508 0.001571 0.143
HSA-MIR-30E 0.002239 0.174
HSA-LET-7B 0.002681 0.183
HSA-MIR-184 0.003596 0.218
HSA-MIR-148A 0.004849 0.245
Clinical variable #4: 'PATHOLOGY_T_STAGE'

7 miRs related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.01 (0.74)
  N
  T1 106
  T2 281
  T3 69
  T4 22
     
  Significant markers N = 7
  pos. correlated 0
  neg. correlated 7
List of 7 miRs differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of 7 miRs significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-30E -0.1674 0.0002369 0.0954
HSA-MIR-101-1 -0.1629 0.0003501 0.0954
HSA-MIR-150 -0.1535 0.0007577 0.13
HSA-MIR-509-1 -0.1525 0.001143 0.13
HSA-MIR-504 -0.1731 0.001189 0.13
HSA-MIR-508 -0.1456 0.001485 0.135
HSA-MIR-551B -0.1409 0.002432 0.189
Clinical variable #5: 'PATHOLOGY_N_STAGE'

6 miRs related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.46 (0.69)
  N
  N0 301
  N1 127
  N2 40
  N3 4
     
  Significant markers N = 6
  pos. correlated 5
  neg. correlated 1
List of 6 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of 6 miRs significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-20B 0.1702 0.0002027 0.11
HSA-MIR-363 0.1569 0.0006313 0.145
HSA-MIR-3136 0.167 0.0009903 0.145
HSA-MIR-3687 0.1531 0.001063 0.145
HSA-MIR-454 0.1358 0.003117 0.299
HSA-MIR-30A -0.135 0.003291 0.299
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 390
  class1 6
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

7 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 124
  MALE 354
     
  Significant markers N = 7
  Higher in MALE 7
  Higher in FEMALE 0
List of 7 miRs differentially expressed by 'GENDER'

Table S13.  Get Full Table List of 7 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-375 17114 0.0002607 0.138 0.6101
HSA-MIR-511-2 17299 0.0005138 0.138 0.6048
HSA-MIR-146A 17537 0.0008624 0.138 0.6005
HSA-MIR-766 17596 0.001011 0.138 0.5991
HSA-MIR-511-1 17791 0.001689 0.166 0.5947
HSA-MIR-326 17408 0.001825 0.166 0.5945
HSA-MIR-141 25808 0.003549 0.276 0.5879
Clinical variable #8: 'RADIATION_THERAPY'

No miR related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 371
  YES 48
     
  Significant markers N = 0
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) 61.85 (41)
  Significant markers N = 30
  pos. correlated 3
  neg. correlated 27
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-26A-1 -0.3874 6.341e-07 0.000326
HSA-MIR-140 0.3763 1.195e-06 0.000326
HSA-MIR-628 -0.3516 6.338e-06 0.00115
HSA-MIR-186 -0.3369 1.603e-05 0.00208
HSA-MIR-616 -0.335 1.91e-05 0.00208
HSA-MIR-3647 -0.3256 3.16e-05 0.00287
HSA-MIR-148B -0.3221 3.889e-05 0.00302
HSA-MIR-3607 -0.3183 4.852e-05 0.00302
HSA-MIR-3653 -0.3178 4.989e-05 0.00302
HSA-MIR-3613 -0.3153 5.76e-05 0.00314
Clinical variable #10: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 13
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 6
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 458
     
  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-17 3.857e-05 0.021
HSA-MIR-29A 0.0001775 0.037
HSA-MIR-92A-1 0.0002083 0.037
HSA-MIR-92A-2 0.0002716 0.037
HSA-MIR-3200 0.0007479 0.0675
HSA-MIR-20A 0.0008813 0.0675
HSA-MIR-18A 0.000996 0.0675
HSA-MIR-25 0.00114 0.0675
HSA-MIR-212 0.001319 0.0675
HSA-MIR-1301 0.001332 0.0675
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) 53.34 (32)
  Significant markers N = 0
Clinical variable #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

30 miRs related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1960.54 (12)
  Significant markers N = 30
  pos. correlated 8
  neg. correlated 22
List of top 10 miRs differentially expressed by 'YEAR_OF_TOBACCO_SMOKING_ONSET'

Table S21.  Get Full Table List of top 10 miRs significantly correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-99B -0.239 2.462e-05 0.0117
HSA-MIR-337 -0.2316 4.425e-05 0.0117
HSA-MIR-125A -0.2268 6.434e-05 0.0117
HSA-MIR-421 0.2192 0.0001195 0.0163
HSA-MIR-653 -0.2117 0.0002103 0.0221
HSA-MIR-376A-1 -0.222 0.0002429 0.0221
HSA-MIR-539 -0.2052 0.000317 0.0247
HSA-MIR-3615 0.2013 0.0004123 0.0259
HSA-MIR-148A -0.2005 0.0004273 0.0259
HSA-MIR-134 -0.1944 0.0006404 0.0344
Clinical variable #13: 'RESIDUAL_TUMOR'

No miR related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 379
  R1 11
  R2 3
  RX 21
     
  Significant markers N = 0
Clinical variable #14: 'RACE'

5 miRs related to 'RACE'.

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

RACE Labels N
  ASIAN 9
  BLACK OR AFRICAN AMERICAN 30
  WHITE 333
     
  Significant markers N = 5
List of 5 miRs differentially expressed by 'RACE'

Table S24.  Get Full Table List of 5 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-1304 1.775e-05 0.00569
HSA-MIR-1269 2.088e-05 0.00569
HSA-MIR-412 0.001724 0.293
HSA-MIR-2277 0.002246 0.293
HSA-MIR-3130-1 0.002685 0.293
Clinical variable #15: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 7
  NOT HISPANIC OR LATINO 302
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 478

  • Number of miRs = 545

  • 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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