Correlation between miR expression and clinical features
Glioblastoma Multiforme (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1VQ31D0
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miR expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1VQ31D0
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 534 miRs and 8 clinical features across 561 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one miRs.

  • 10 miRs correlated to 'AGE'.

    • HSA-MIR-210 ,  HSA-MIR-148A ,  HSA-MIR-552 ,  HSA-MIR-29B ,  HSA-MIR-34A ,  ...

  • 2 miRs correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-130B ,  HSA-MIR-302A*

  • 2 miRs correlated to 'RACE'.

    • HSA-MIR-624 ,  HSA-MIR-551A

  • No miRs correlated to 'Time to Death', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 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=4 younger N=6
GENDER Wilcoxon test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE Kruskal-Wallis test N=2        
RADIATIONS RADIATION REGIMENINDICATION Wilcoxon test   N=0        
RACE Kruskal-Wallis test N=2        
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) 0.1-127.6 (median=10.3)
  censored N = 100
  death N = 461
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

10 miRs related to 'AGE'.

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

AGE Mean (SD) 57.89 (14)
  Significant markers N = 10
  pos. correlated 4
  neg. correlated 6
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-210 0.2045 1.041e-06 0.000556
HSA-MIR-148A 0.194 3.663e-06 0.00195
HSA-MIR-552 -0.1662 7.64e-05 0.0406
HSA-MIR-29B 0.1645 9.084e-05 0.0482
HSA-MIR-34A 0.1601 0.0001393 0.0739
HSA-MIR-625 -0.16 0.0001408 0.0745
HSA-MIR-340 -0.1527 0.0002847 0.15
HSA-MIR-17-3P -0.1488 0.0004067 0.214
HSA-MIR-17-5P -0.1482 0.0004269 0.225
HSA-MIR-620 -0.1453 0.0005537 0.291
Clinical variable #3: 'GENDER'

No miR related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 218
  MALE 343
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 77.48 (15)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

2 miRs related to 'HISTOLOGICAL.TYPE'.

Table S6.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  GLIOBLASTOMA MULTIFORME (GBM) 9
  TREATED PRIMARY GBM 20
  UNTREATED PRIMARY (DE NOVO) GBM 532
     
  Significant markers N = 2
List of 2 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

Table S7.  Get Full Table List of 2 miRs differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-130B 0.0003662 0.196
HSA-MIR-302A* 0.0004448 0.237
Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No miR related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S8.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 389
  YES 172
     
  Significant markers N = 0
Clinical variable #7: 'RACE'

2 miRs related to 'RACE'.

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

RACE Labels N
  ASIAN 13
  BLACK OR AFRICAN AMERICAN 30
  WHITE 493
     
  Significant markers N = 2
List of 2 miRs differentially expressed by 'RACE'

Table S10.  Get Full Table List of 2 miRs differentially expressed by 'RACE'

ANOVA_P Q
HSA-MIR-624 6.577e-05 0.0351
HSA-MIR-551A 0.0005126 0.273
Clinical variable #8: 'ETHNICITY'

No miR related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 459
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = GBM-TP.mirna__h_mirna_8x15k__unc_edu__Level_3__unc_DWD_Batch_adjusted__data.data.txt

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

  • Number of patients = 561

  • Number of miRs = 534

  • 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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