Correlation between mRNA expression and clinical features
Glioblastoma Multiforme (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
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 mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1KP8114
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 12042 genes and 8 clinical features across 525 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one genes.

  • 155 genes correlated to 'AGE'.

    • FBXO17 ,  KIAA0495 ,  RANBP17 ,  NOL3 ,  TUSC3 ,  ...

  • 9 genes correlated to 'GENDER'.

    • JARID1D ,  CYORF15B ,  HDHD1A ,  UTX ,  JARID1C ,  ...

  • 3 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • TM4SF20 ,  ZBP1 ,  WRNIP1

  • 119 genes correlated to 'HISTOLOGICAL.TYPE'.

    • PDIA6 ,  NPM1 ,  HNRPF ,  ALDH18A1 ,  NPM3 ,  ...

  • 9 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • STAG2 ,  RPL13 ,  SLC37A4 ,  CTPS2 ,  SH3TC2 ,  ...

  • 2 genes correlated to 'RACE'.

    • CRYBB2 ,  PSPH

  • No genes correlated to 'Time to Death', 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 genes that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=155 older N=133 younger N=22
GENDER Wilcoxon test N=9 male N=9 female N=0
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=3 higher score N=3 lower score N=0
HISTOLOGICAL TYPE Kruskal-Wallis test N=119        
RADIATIONS RADIATION REGIMENINDICATION Wilcoxon test N=9 yes N=9 no N=0
RACE Kruskal-Wallis test N=2        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time to Death'

No gene 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=10.4)
  censored N = 80
  death N = 445
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

155 genes related to 'AGE'.

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

AGE Mean (SD) 57.68 (15)
  Significant markers N = 155
  pos. correlated 133
  neg. correlated 22
List of top 10 genes differentially expressed by 'AGE'

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

SpearmanCorr corrP Q
FBXO17 0.3074 5.924e-13 7.13e-09
KIAA0495 0.2949 5.437e-12 6.55e-08
RANBP17 -0.2916 9.501e-12 1.14e-07
NOL3 0.2831 3.903e-11 4.7e-07
TUSC3 -0.2761 1.217e-10 1.46e-06
C14ORF45 0.2726 2.115e-10 2.55e-06
DRG2 0.2617 1.147e-09 1.38e-05
C5ORF21 0.2498 6.579e-09 7.92e-05
CBR1 0.2455 1.203e-08 0.000145
H2AFY2 -0.2455 1.206e-08 0.000145
Clinical variable #3: 'GENDER'

9 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 205
  MALE 320
     
  Significant markers N = 9
  Higher in MALE 9
  Higher in FEMALE 0
List of 9 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 9 genes differentially expressed by 'GENDER'. 15 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
JARID1D 62786 5.665e-70 6.82e-66 0.9571
CYORF15B 61883 6.228e-66 7.5e-62 0.9433
HDHD1A 15478 1.702e-24 2.05e-20 0.7641
UTX 20401 2.639e-13 3.17e-09 0.689
JARID1C 20621 6.875e-13 8.27e-09 0.6857
CXORF15 24198 3.927e-07 0.00472 0.6311
GPR88 41229 6.68e-07 0.00803 0.6285
RCAN2 40921 1.678e-06 0.0202 0.6238
ADAM20 25476 1.569e-05 0.189 0.6116
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

3 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 77.22 (14)
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 genes differentially expressed by 'KARNOFSKY.PERFORMANCE.SCORE'

Table S7.  Get Full Table List of 3 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
TM4SF20 0.2445 9.617e-07 0.0116
ZBP1 0.2284 4.929e-06 0.0594
WRNIP1 0.2148 1.789e-05 0.215
Clinical variable #5: 'HISTOLOGICAL.TYPE'

119 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  GLIOBLASTOMA MULTIFORME (GBM) 6
  TREATED PRIMARY GBM 20
  UNTREATED PRIMARY (DE NOVO) GBM 499
     
  Significant markers N = 119
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

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

ANOVA_P Q
PDIA6 1.192e-07 0.00144
NPM1 1.195e-07 0.00144
HNRPF 1.385e-07 0.00167
ALDH18A1 1.721e-07 0.00207
NPM3 2.897e-07 0.00349
SSR2 3.4e-07 0.00409
TCTN3 5.104e-07 0.00614
ABT1 6.676e-07 0.00803
SLC1A5 6.683e-07 0.00804
MRPS16 6.819e-07 0.00821
Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

9 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 359
  YES 166
     
  Significant markers N = 9
  Higher in YES 9
  Higher in NO 0
List of 9 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S11.  Get Full Table List of 9 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

W(pos if higher in 'YES') wilcoxontestP Q AUC
STAG2 22266 3.173e-06 0.0382 0.6264
RPL13 22288 3.39e-06 0.0408 0.626
SLC37A4 22542 7.172e-06 0.0863 0.6217
CTPS2 22598 8.433e-06 0.102 0.6208
SH3TC2 36852 1.272e-05 0.153 0.6184
H1F0 22762 1.346e-05 0.162 0.618
STT3A 22839 1.672e-05 0.201 0.6168
MAP7D3 22854 1.743e-05 0.21 0.6165
NUTF2 22865 1.797e-05 0.216 0.6163
Clinical variable #7: 'RACE'

2 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 13
  BLACK OR AFRICAN AMERICAN 31
  WHITE 462
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'RACE'

Table S13.  Get Full Table List of 2 genes differentially expressed by 'RACE'

ANOVA_P Q
CRYBB2 5.444e-07 0.00656
PSPH 1.078e-05 0.13
Clinical variable #8: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 438
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = GBM-TP.medianexp.txt

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

  • Number of patients = 525

  • Number of genes = 12042

  • 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)