Correlation between miRseq expression and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C10V8BTM
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/C10V8BTM
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 545 miRs and 15 clinical features across 469 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-326 ,  HSA-MIR-16-2 ,  HSA-MIR-187 ,  HSA-MIR-15B ,  HSA-MIR-500B ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-99A ,  HSA-LET-7C ,  HSA-MIR-10A ,  HSA-MIR-3648 ,  HSA-MIR-3687 ,  ...

  • 30 miRs correlated to 'NEOPLASM_DISEASESTAGE'.

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

  • 7 miRs correlated to 'PATHOLOGY_T_STAGE'.

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

  • 4 miRs correlated to 'PATHOLOGY_N_STAGE'.

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

  • 9 miRs correlated to 'GENDER'.

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

  • 30 miRs correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • HSA-MIR-26A-1 ,  HSA-MIR-628 ,  HSA-MIR-30E ,  HSA-MIR-140 ,  HSA-MIR-3647 ,  ...

  • 23 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 ,  ...

  • 3 miRs correlated to 'RACE'.

    • HSA-MIR-1304 ,  HSA-MIR-1269 ,  HSA-MIR-412

  • No miRs correlated to 'PATHOLOGY_M_STAGE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'NUMBER_PACK_YEARS_SMOKED', 'COMPLETENESS_OF_RESECTION', 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 shorter survival N=1 longer survival N=8
YEARS_TO_BIRTH Spearman correlation test N=30 older N=14 younger N=16
NEOPLASM_DISEASESTAGE 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=4 higher stage N=4 lower stage N=0
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=9 male N=9 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=5 lower score N=25
HISTOLOGICAL_TYPE Kruskal-Wallis test N=23        
RADIATIONS_RADIATION_REGIMENINDICATION Wilcoxon test   N=0        
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=9 lower year_of_tobacco_smoking_onset N=21
COMPLETENESS_OF_RESECTION Kruskal-Wallis test   N=0        
RACE Kruskal-Wallis test N=3        
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) 0-174.1 (median=19.3)
  censored N = 284
  death N = 184
     
  Significant markers N = 9
  associated with shorter survival 1
  associated with longer survival 8
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

HazardRatio Wald_P Q C_index
HSA-MIR-326 1.22 0.0001063 0.048 0.566
HSA-MIR-16-2 0.72 0.000176 0.048 0.42
HSA-MIR-187 0.89 0.0009402 0.15 0.43
HSA-MIR-15B 0.72 0.001081 0.15 0.422
HSA-MIR-500B 0.81 0.0026 0.28 0.457
HSA-MIR-15A 0.71 0.003875 0.3 0.452
HSA-MIR-374A 0.7 0.004442 0.3 0.472
HSA-MIR-362 0.81 0.004447 0.3 0.449
HSA-MIR-655 0.85 0.004877 0.3 0.452
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) 67.47 (8.6)
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
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 0.2011 1.353e-05 0.00417
HSA-LET-7C 0.1999 1.53e-05 0.00417
HSA-MIR-10A 0.1958 2.306e-05 0.00419
HSA-MIR-3648 -0.1867 5.948e-05 0.0081
HSA-MIR-3687 -0.1818 0.0001171 0.01
HSA-MIR-466 -0.2512 0.000118 0.01
HSA-MIR-125B-2 0.1776 0.0001287 0.01
HSA-MIR-421 -0.1749 0.0001714 0.0117
HSA-MIR-32 -0.1679 0.0002928 0.0177
HSA-MIR-3074 -0.1645 0.0003899 0.0212
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

30 miRs related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 3
  STAGE IA 83
  STAGE IB 142
  STAGE II 2
  STAGE IIA 60
  STAGE IIB 90
  STAGE III 3
  STAGE IIIA 60
  STAGE IIIB 18
  STAGE IV 6
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'NEOPLASM_DISEASESTAGE'

Table S6.  Get Full Table List of top 10 miRs differentially expressed by 'NEOPLASM_DISEASESTAGE'

kruskal_wallis_P Q
HSA-MIR-30A 4.369e-05 0.0238
HSA-MIR-23C 0.0001159 0.0316
HSA-MIR-126 0.0002405 0.0437
HSA-MIR-545 0.0006905 0.0821
HSA-MIR-361 0.000753 0.0821
HSA-MIR-30E 0.001694 0.127
HSA-LET-7B 0.001755 0.127
HSA-MIR-181A-1 0.001863 0.127
HSA-MIR-184 0.003897 0.236
HSA-MIR-508 0.004492 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 105
  T2 276
  T3 67
  T4 21
     
  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 -0.1697 0.0002226 0.0917
HSA-MIR-101-1 -0.1649 0.0003367 0.0917
HSA-MIR-150 -0.1592 0.0005394 0.098
HSA-MIR-504 -0.1809 0.0008036 0.109
HSA-MIR-509-1 -0.1498 0.001553 0.162
HSA-MIR-508 -0.1427 0.002032 0.162
HSA-MIR-551B -0.1443 0.002085 0.162
Clinical variable #5: 'PATHOLOGY_N_STAGE'

4 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 295
  N1 125
  N2 39
  N3 4
     
  Significant markers N = 4
  pos. correlated 4
  neg. correlated 0
List of 4 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of 4 miRs significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-20B 0.1677 0.0002898 0.125
HSA-MIR-3687 0.1597 0.0007131 0.125
HSA-MIR-3136 0.1712 0.0008294 0.125
HSA-MIR-363 0.1537 0.0009203 0.125
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 382
  class1 6
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

9 miRs related to 'GENDER'.

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

GENDER Labels N
  FEMALE 121
  MALE 348
     
  Significant markers N = 9
  Higher in MALE 9
  Higher in FEMALE 0
List of 9 miRs differentially expressed by 'GENDER'

Table S13.  Get Full Table List of 9 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 16150 0.0001344 0.0567 0.6165
HSA-MIR-511-2 16344 0.0002843 0.0567 0.6107
HSA-MIR-146A 16424 0.0003123 0.0567 0.61
HSA-MIR-511-1 16886 0.001174 0.121 0.599
HSA-MIR-766 16888 0.00118 0.121 0.5989
HSA-MIR-141 25155 0.001408 0.121 0.5974
HSA-MIR-326 16587 0.001552 0.121 0.597
HSA-MIR-200C 24813 0.003426 0.209 0.5893
HSA-MIR-155 17297 0.003443 0.209 0.5892
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

30 miRs related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 57.11 (41)
  Significant markers N = 30
  pos. correlated 5
  neg. correlated 25
List of top 10 miRs differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S15.  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 -0.6286 7.056e-12 3.85e-09
HSA-MIR-628 -0.5789 5.258e-10 1.43e-07
HSA-MIR-30E -0.5381 1.312e-08 2.22e-06
HSA-MIR-140 0.5351 1.633e-08 2.22e-06
HSA-MIR-3647 -0.5261 3.127e-08 3.41e-06
HSA-MIR-3607 -0.511 8.866e-08 8.05e-06
HSA-MIR-3653 -0.5023 1.582e-07 1.23e-05
HSA-MIR-3605 -0.4974 2.181e-07 1.49e-05
HSA-MIR-186 -0.494 2.724e-07 1.65e-05
HSA-MIR-660 0.4808 6.214e-07 3.39e-05
Clinical variable #9: 'HISTOLOGICAL_TYPE'

23 miRs related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 13
  LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA 1
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 6
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 448
     
  Significant markers N = 23
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-17 8.173e-05 0.0445
HSA-MIR-29A 0.0003573 0.0608
HSA-MIR-92A-1 0.0004023 0.0608
HSA-MIR-92A-2 0.0004462 0.0608
HSA-MIR-3200 0.001312 0.143
HSA-MIR-20A 0.001628 0.148
HSA-MIR-25 0.002265 0.149
HSA-MIR-18A 0.002384 0.149
HSA-MIR-212 0.002741 0.149
HSA-MIR-93 0.002976 0.149
Clinical variable #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

No miR related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

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

RADIATIONS_RADIATION_REGIMENINDICATION Labels N
  NO 12
  YES 457
     
  Significant markers N = 0
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) 53.29 (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) 1960.57 (12)
  Significant markers N = 30
  pos. correlated 9
  neg. correlated 21
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 -0.2411 2.216e-05 0.0121
HSA-MIR-337 -0.2283 6.07e-05 0.0143
HSA-MIR-125A -0.2243 8.19e-05 0.0143
HSA-MIR-421 0.2191 0.0001272 0.0143
HSA-MIR-653 -0.219 0.0001309 0.0143
HSA-MIR-539 -0.2063 0.0003062 0.0278
HSA-MIR-376A-1 -0.2146 0.0004129 0.0305
HSA-MIR-3615 0.1993 0.0004949 0.0305
HSA-MIR-148A -0.1987 0.0005037 0.0305
HSA-MIR-134 -0.1932 0.0007199 0.0392
Clinical variable #13: 'COMPLETENESS_OF_RESECTION'

No miR related to 'COMPLETENESS_OF_RESECTION'.

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

COMPLETENESS_OF_RESECTION Labels N
  R0 374
  R1 11
  R2 3
  RX 20
     
  Significant markers N = 0
Clinical variable #14: 'RACE'

3 miRs related to 'RACE'.

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

RACE Labels N
  ASIAN 9
  BLACK OR AFRICAN AMERICAN 30
  WHITE 324
     
  Significant markers N = 3
List of 3 miRs differentially expressed by 'RACE'

Table S24.  Get Full Table List of 3 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-1304 1.022e-05 0.00557
HSA-MIR-1269 2.507e-05 0.00683
HSA-MIR-412 0.001347 0.245
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 294
     
  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 = 469

  • Number of miRs = 545

  • Number of clinical features = 15

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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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