Correlation between miRseq expression and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1PV6J7X
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 572 miRs and 12 clinical features across 279 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one miRs.

  • 10 miRs correlated to 'AGE'.

    • HSA-MIR-376B ,  HSA-MIR-214 ,  HSA-MIR-377 ,  HSA-MIR-145 ,  HSA-MIR-337 ,  ...

  • 25 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

    • HSA-MIR-100 ,  HSA-MIR-199A-2 ,  HSA-MIR-199A-1 ,  HSA-MIR-199B ,  HSA-MIR-99A ,  ...

  • 44 miRs correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-199A-2 ,  HSA-MIR-199B ,  HSA-MIR-199A-1 ,  HSA-MIR-99A ,  HSA-MIR-3193 ,  ...

  • 9 miRs correlated to 'PATHOLOGY.N.STAGE'.

    • HSA-MIR-582 ,  HSA-MIR-100 ,  HSA-MIR-99A ,  HSA-MIR-545 ,  HSA-MIR-320A ,  ...

  • 29 miRs correlated to 'PATHOLOGY.M.STAGE'.

    • HSA-MIR-200C ,  HSA-MIR-151 ,  HSA-MIR-200B ,  HSA-MIR-21 ,  HSA-MIR-598 ,  ...

  • 4 miRs correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • HSA-MIR-338 ,  HSA-MIR-3655 ,  HSA-MIR-146B ,  HSA-MIR-142

  • 2 miRs correlated to 'NUMBER.OF.LYMPH.NODES'.

    • HSA-MIR-545 ,  HSA-MIR-100

  • 58 miRs correlated to 'RACE'.

    • HSA-MIR-212 ,  HSA-MIR-30D ,  HSA-MIR-664 ,  HSA-MIR-379 ,  HSA-MIR-598 ,  ...

  • No miRs correlated to 'Time to Death', 'GENDER', 'NUMBERPACKYEARSSMOKED', 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
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=10 older N=9 younger N=1
NEOPLASM DISEASESTAGE Kruskal-Wallis test N=25        
PATHOLOGY T STAGE Spearman correlation test N=44 higher stage N=20 lower stage N=24
PATHOLOGY N STAGE Spearman correlation test N=9 higher stage N=3 lower stage N=6
PATHOLOGY M STAGE Kruskal-Wallis test N=29        
GENDER Wilcoxon test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=4 higher score N=3 lower score N=1
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test N=2 higher number.of.lymph.nodes N=1 lower number.of.lymph.nodes N=1
RACE Kruskal-Wallis test N=58        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0.1-140.8 (median=9)
  censored N = 182
  death N = 89
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

10 miRs related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 67.82 (11)
  Significant markers N = 10
  pos. correlated 9
  neg. correlated 1
List of 10 miRs differentially expressed by 'AGE'

Table S3.  Get Full Table List of 10 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-376B 0.2326 0.0001325 0.0758
HSA-MIR-214 0.2268 0.0001365 0.0779
HSA-MIR-377 0.2271 0.0001672 0.0953
HSA-MIR-145 0.2181 0.0002488 0.142
HSA-MIR-337 0.2169 0.0002694 0.153
HSA-MIR-127 0.213 0.000348 0.197
HSA-MIR-3193 -0.2358 0.0003593 0.203
HSA-MIR-134 0.2116 0.000382 0.216
HSA-MIR-382 0.2104 0.0004124 0.233
HSA-MIR-655 0.2131 0.0004423 0.249
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

25 miRs related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE 0A 1
  STAGE I 2
  STAGE II 87
  STAGE III 93
  STAGE IV 92
     
  Significant markers N = 25
List of top 10 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'

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

ANOVA_P Q
HSA-MIR-100 6.158e-06 0.00352
HSA-MIR-199A-2 7.332e-06 0.00419
HSA-MIR-199A-1 1.019e-05 0.00581
HSA-MIR-199B 1.329e-05 0.00756
HSA-MIR-99A 2.189e-05 0.0124
HSA-LET-7C 3.931e-05 0.0223
HSA-MIR-125B-1 4.175e-05 0.0236
HSA-MIR-125B-2 4.768e-05 0.0269
HSA-MIR-214 7.033e-05 0.0397
HSA-MIR-874 7.311e-05 0.0412
Clinical variable #4: 'PATHOLOGY.T.STAGE'

44 miRs related to 'PATHOLOGY.T.STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.82 (0.7)
  N
  0 1
  1 1
  2 80
  3 136
  4 39
     
  Significant markers N = 44
  pos. correlated 20
  neg. correlated 24
List of top 10 miRs differentially expressed by 'PATHOLOGY.T.STAGE'

Table S7.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-199A-2 0.3345 3.91e-08 2.24e-05
HSA-MIR-199B 0.334 4.081e-08 2.33e-05
HSA-MIR-199A-1 0.3275 7.716e-08 4.4e-05
HSA-MIR-99A 0.2976 1.181e-06 0.000672
HSA-MIR-3193 -0.3109 4.875e-06 0.00277
HSA-MIR-200C -0.2789 5.645e-06 0.0032
HSA-MIR-382 0.2762 7.012e-06 0.00397
HSA-MIR-214 0.2747 7.902e-06 0.00446
HSA-MIR-125B-1 0.2742 8.211e-06 0.00463
HSA-MIR-1307 -0.2736 8.564e-06 0.00482
Clinical variable #5: 'PATHOLOGY.N.STAGE'

9 miRs related to 'PATHOLOGY.N.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.62 (0.92)
  N
  0 167
  1 27
  2 56
  3 7
     
  Significant markers N = 9
  pos. correlated 3
  neg. correlated 6
List of 9 miRs differentially expressed by 'PATHOLOGY.N.STAGE'

Table S9.  Get Full Table List of 9 miRs significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-582 -0.2805 4.938e-06 0.00282
HSA-MIR-100 0.2632 1.914e-05 0.0109
HSA-MIR-99A 0.2338 0.0001555 0.0887
HSA-MIR-545 -0.2458 0.0002471 0.141
HSA-MIR-320A -0.2265 0.0002519 0.143
HSA-MIR-125B-1 0.2248 0.0002807 0.159
HSA-MIR-10B -0.2243 0.0002901 0.164
HSA-MIR-1976 -0.2235 0.0003047 0.172
HSA-MIR-944 -0.2183 0.0004564 0.257
Clinical variable #6: 'PATHOLOGY.M.STAGE'

29 miRs related to 'PATHOLOGY.M.STAGE'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 131
  M1 7
  MX 140
     
  Significant markers N = 29
List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

Table S11.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
HSA-MIR-200C 6.064e-07 0.000347
HSA-MIR-151 1.202e-06 0.000687
HSA-MIR-200B 2.105e-06 0.0012
HSA-MIR-21 6.105e-06 0.00347
HSA-MIR-598 6.48e-06 0.00368
HSA-MIR-320A 6.689e-06 0.00379
HSA-MIR-330 8.937e-06 0.00506
HSA-MIR-429 1.035e-05 0.00585
HSA-MIR-199A-1 2.009e-05 0.0113
HSA-MIR-183 2.042e-05 0.0115
Clinical variable #7: 'GENDER'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 77
  MALE 202
     
  Significant markers N = 0
Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

4 miRs related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S13.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 81 (15)
  Significant markers N = 4
  pos. correlated 3
  neg. correlated 1
List of 4 miRs differentially expressed by 'KARNOFSKY.PERFORMANCE.SCORE'

Table S14.  Get Full Table List of 4 miRs significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-338 0.4639 1.17e-06 0.000669
HSA-MIR-3655 -0.5937 4.279e-05 0.0244
HSA-MIR-146B 0.3645 0.0001924 0.11
HSA-MIR-142 0.3408 0.0005203 0.296
Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No miR related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 38.82 (27)
  Significant markers N = 0
Clinical variable #10: 'NUMBER.OF.LYMPH.NODES'

2 miRs related to 'NUMBER.OF.LYMPH.NODES'.

Table S16.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.17 (7.6)
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 miRs differentially expressed by 'NUMBER.OF.LYMPH.NODES'

Table S17.  Get Full Table List of 2 miRs significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-545 -0.2934 5.839e-05 0.0334
HSA-MIR-100 0.2367 0.0004228 0.241
Clinical variable #11: 'RACE'

58 miRs related to 'RACE'.

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

RACE Labels N
  ASIAN 31
  BLACK OR AFRICAN AMERICAN 14
  WHITE 213
     
  Significant markers N = 58
List of top 10 miRs differentially expressed by 'RACE'

Table S19.  Get Full Table List of top 10 miRs differentially expressed by 'RACE'

ANOVA_P Q
HSA-MIR-212 4.276e-08 2.45e-05
HSA-MIR-30D 6.089e-08 3.48e-05
HSA-MIR-664 3.86e-07 0.00022
HSA-MIR-379 1.89e-06 0.00108
HSA-MIR-598 3.957e-06 0.00225
HSA-MIR-29C 4.508e-06 0.00256
HSA-MIR-30E 4.97e-06 0.00281
HSA-MIR-758 5.184e-06 0.00293
HSA-MIR-214 6.014e-06 0.00339
HSA-MIR-132 6.522e-06 0.00367
Clinical variable #12: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 5
  NOT HISPANIC OR LATINO 257
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = BLCA-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 279

  • Number of miRs = 572

  • Number of clinical features = 12

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

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] 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)