Correlation between miRseq expression and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1M32TH3
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/C1M32TH3
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 546 miRs and 8 clinical features across 397 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one miRs.

  • 67 miRs correlated to 'Time to Death'.

    • HSA-MIR-155 ,  HSA-MIR-10A ,  HSA-MIR-346 ,  HSA-MIR-15B ,  HSA-MIR-196B ,  ...

  • 41 miRs correlated to 'AGE'.

    • HSA-MIR-34A ,  HSA-MIR-155 ,  HSA-MIR-25 ,  HSA-MIR-10A ,  HSA-MIR-126 ,  ...

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

    • HSA-MIR-148A ,  HSA-MIR-222 ,  HSA-MIR-135B ,  HSA-MIR-221 ,  HSA-MIR-432 ,  ...

  • 94 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-1262 ,  HSA-MIR-592 ,  HSA-MIR-186 ,  HSA-MIR-3065 ,  HSA-MIR-219-1 ,  ...

  • 53 miRs correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • HSA-MIR-1274B ,  HSA-MIR-628 ,  HSA-MIR-3130-1 ,  HSA-MIR-30E ,  HSA-MIR-9-3 ,  ...

  • 1 miR correlated to 'RACE'.

    • HSA-MIR-1304

  • No miRs correlated to 'GENDER', 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=67 shorter survival N=62 longer survival N=5
AGE Spearman correlation test N=41 older N=33 younger N=8
GENDER Wilcoxon test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=8 higher score N=0 lower score N=8
HISTOLOGICAL TYPE Kruskal-Wallis test N=94        
RADIATIONS RADIATION REGIMENINDICATION Wilcoxon test N=53 yes N=53 no N=0
RACE Kruskal-Wallis test N=1        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time to Death'

67 miRs related to 'Time to Death'.

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

Time to Death Duration (Months) 0-211.2 (median=15)
  censored N = 323
  death N = 70
     
  Significant markers N = 67
  associated with shorter survival 62
  associated with longer survival 5
List of top 10 miRs differentially expressed by 'Time to Death'

Table S2.  Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-155 1.87 4.252e-14 2.3e-11 0.787
HSA-MIR-10A 1.34 3.779e-11 2.1e-08 0.714
HSA-MIR-346 0.59 4.889e-10 2.7e-07 0.33
HSA-MIR-15B 1.89 8.416e-10 4.6e-07 0.781
HSA-MIR-196B 1.22 4.321e-09 2.3e-06 0.715
HSA-MIR-148A 1.58 1.638e-08 8.9e-06 0.751
HSA-MIR-9-1 0.41 8.078e-08 4.4e-05 0.242
HSA-MIR-9-2 0.41 8.185e-08 4.4e-05 0.242
HSA-MIR-23A 1.86 6.298e-07 0.00034 0.714
HSA-MIR-3677 1.45 6.76e-07 0.00036 0.75
Clinical variable #2: 'AGE'

41 miRs related to 'AGE'.

Table S3.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 43.28 (13)
  Significant markers N = 41
  pos. correlated 33
  neg. correlated 8
List of top 10 miRs differentially expressed by 'AGE'

Table S4.  Get Full Table List of top 10 miRs significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-34A 0.3236 3.944e-11 2.15e-08
HSA-MIR-155 0.2671 6.534e-08 3.56e-05
HSA-MIR-25 0.266 7.459e-08 4.06e-05
HSA-MIR-10A 0.2589 1.675e-07 9.1e-05
HSA-MIR-126 0.2579 1.876e-07 0.000102
HSA-MIR-146A 0.2486 5.284e-07 0.000286
HSA-MIR-10B 0.2418 1.084e-06 0.000585
HSA-MIR-2115 0.2507 1.269e-06 0.000684
HSA-MIR-664 0.2356 2.873e-06 0.00155
HSA-MIR-429 0.272 2.93e-06 0.00157
Clinical variable #3: 'GENDER'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 178
  MALE 219
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 87.99 (12)
  Significant markers N = 8
  pos. correlated 0
  neg. correlated 8
List of 8 miRs differentially expressed by 'KARNOFSKY.PERFORMANCE.SCORE'

Table S7.  Get Full Table List of 8 miRs significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-148A -0.2653 4.801e-05 0.0262
HSA-MIR-222 -0.2598 6.949e-05 0.0379
HSA-MIR-135B -0.2619 0.0002246 0.122
HSA-MIR-221 -0.2359 0.0003175 0.172
HSA-MIR-432 -0.2332 0.0003721 0.202
HSA-MIR-142 -0.2308 0.0004301 0.233
HSA-MIR-337 -0.2288 0.0004837 0.261
HSA-MIR-493 -0.2306 0.0005348 0.288
Clinical variable #5: 'HISTOLOGICAL.TYPE'

94 miRs related to 'HISTOLOGICAL.TYPE'.

Table S8.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 142
  OLIGOASTROCYTOMA 102
  OLIGODENDROGLIOMA 153
     
  Significant markers N = 94
List of top 10 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S9.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-1262 7.847e-15 4.28e-12
HSA-MIR-592 4.233e-14 2.31e-11
HSA-MIR-186 1.164e-13 6.33e-11
HSA-MIR-3065 1.114e-12 6.05e-10
HSA-MIR-219-1 1.453e-12 7.87e-10
HSA-MIR-3074 3.626e-12 1.96e-09
HSA-MIR-576 4.179e-12 2.26e-09
HSA-MIR-21 6.299e-11 3.4e-08
HSA-MIR-505 6.834e-11 3.68e-08
HSA-MIR-301A 7.47e-11 4.01e-08
Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

53 miRs related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S10.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 88
  YES 309
     
  Significant markers N = 53
  Higher in YES 53
  Higher in NO 0
List of top 10 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S11.  Get Full Table List of top 10 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

W(pos if higher in 'YES') wilcoxontestP Q AUC
HSA-MIR-1274B 6864 3.67e-10 2e-07 0.7214
HSA-MIR-628 7698 5.296e-10 2.89e-07 0.7169
HSA-MIR-3130-1 6674 3.247e-09 1.77e-06 0.7144
HSA-MIR-30E 8534 9.834e-08 5.34e-05 0.6862
HSA-MIR-9-3 18485 2.639e-07 0.000143 0.6798
HSA-MIR-424 8776 3.876e-07 0.00021 0.6773
HSA-MIR-3677 8850 5.823e-07 0.000314 0.6745
HSA-MIR-331 8852 5.886e-07 0.000317 0.6745
HSA-MIR-500A 18327 6.319e-07 0.00034 0.674
HSA-MIR-1262 8529 1.406e-06 0.000755 0.6705
Clinical variable #7: 'RACE'

One miR related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 14
  WHITE 373
     
  Significant markers N = 1
List of one miR differentially expressed by 'RACE'

Table S13.  Get Full Table List of one miR differentially expressed by 'RACE'

ANOVA_P Q
HSA-MIR-1304 1.219e-05 0.00666
Clinical variable #8: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 15
  NOT HISPANIC OR LATINO 357
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 397

  • Number of miRs = 546

  • Number of clinical features = 8

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

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

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

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