Correlation between mRNA expression and clinical features
Glioblastoma Multiforme (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C12B8W1C
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12B8W1C
Overview
Introduction

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

Summary

Testing the association between 12042 genes and 6 clinical features across 523 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 15 genes correlated to 'Time to Death'.

    • CLEC5A ,  RANBP17 ,  EFEMP2 ,  DIRAS3 ,  ATP5C1 ,  ...

  • 73 genes correlated to 'AGE'.

    • FBXO17 ,  KIAA0495 ,  RANBP17 ,  NOL3 ,  C14ORF45 ,  ...

  • 21 genes correlated to 'GENDER'.

    • DDX3Y ,  RPS4Y1 ,  JARID1D ,  EIF1AY ,  NLGN4Y ,  ...

  • 1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • TM4SF20

  • 497 genes correlated to 'HISTOLOGICAL.TYPE'.

    • CDH8 ,  KIAA0774 ,  RYR2 ,  KCNV1 ,  CLDN3 ,  ...

  • No genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'

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 Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test N=15 shorter survival N=10 longer survival N=5
AGE Spearman correlation test N=73 older N=61 younger N=12
GENDER t test N=21 male N=13 female N=8
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=1 lower score N=0
HISTOLOGICAL TYPE ANOVA test N=497        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
Clinical variable #1: 'Time to Death'

15 genes 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=9.9)
  censored N = 116
  death N = 407
     
  Significant markers N = 15
  associated with shorter survival 10
  associated with longer survival 5
List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

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

HazardRatio Wald_P Q C_index
CLEC5A 1.23 7.442e-08 9e-04 0.582
RANBP17 0.39 1.046e-07 0.0013 0.418
EFEMP2 1.29 1.15e-07 0.0014 0.54
DIRAS3 1.2 2.886e-07 0.0035 0.554
ATP5C1 0.62 3.108e-07 0.0037 0.447
SIGLEC9 2.2 4.912e-07 0.0059 0.56
HIST3H2A 0.83 4.99e-07 0.006 0.428
ZIC3 0.44 1.578e-06 0.019 0.438
TIMP1 1.21 1.876e-06 0.023 0.55
KIAA0495 1.63 2.181e-06 0.026 0.55

Figure S1.  Get High-res Image As an example, this figure shows the association of CLEC5A to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 7.44e-08 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

73 genes related to 'AGE'.

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

AGE Mean (SD) 57.75 (15)
  Significant markers N = 73
  pos. correlated 61
  neg. correlated 12
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

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

SpearmanCorr corrP Q
FBXO17 0.3036 1.29e-12 1.55e-08
KIAA0495 0.2924 9.085e-12 1.09e-07
RANBP17 -0.2884 1.781e-11 2.14e-07
NOL3 0.2798 7.302e-11 8.79e-07
C14ORF45 0.2744 1.744e-10 2.1e-06
TUSC3 -0.2738 1.904e-10 2.29e-06
DRG2 0.2629 1.03e-09 1.24e-05
C5ORF21 0.2502 6.598e-09 7.94e-05
C9ORF6 0.2476 9.508e-09 0.000114
PCNP 0.2448 1.409e-08 0.00017

Figure S2.  Get High-res Image As an example, this figure shows the association of FBXO17 to 'AGE'. P value = 1.29e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

21 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 205
  MALE 318
     
  Significant markers N = 21
  Higher in MALE 13
  Higher in FEMALE 8
List of top 10 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
DDX3Y 37.87 8.65e-149 1.04e-144 0.9583
RPS4Y1 40.71 3.227e-144 3.89e-140 0.9504
JARID1D 34.74 9.382e-138 1.13e-133 0.957
EIF1AY 34.83 4.969e-137 5.98e-133 0.9499
NLGN4Y 30.22 3.925e-111 4.73e-107 0.9385
UTY 26.81 3.743e-99 4.51e-95 0.9435
USP9Y 22.04 2.372e-75 2.86e-71 0.9337
CYORF15B 22.1 7.411e-75 8.92e-71 0.9434
ZFY 14.69 5.257e-41 6.33e-37 0.8441
HDHD1A -10.41 2.185e-22 2.63e-18 0.7637

Figure S3.  Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 8.65e-149 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 77.12 (14)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S8.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
TM4SF20 0.2356 2.62e-06 0.0316

Figure S4.  Get High-res Image As an example, this figure shows the association of TM4SF20 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 2.62e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

497 genes related to 'HISTOLOGICAL.TYPE'.

Table S9.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  GLIOBLASTOMA MULTIFORME (GBM) 4
  TREATED PRIMARY GBM 20
  UNTREATED PRIMARY (DE NOVO) GBM 499
     
  Significant markers N = 497
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
CDH8 8.038e-25 9.68e-21
KIAA0774 3.567e-24 4.3e-20
RYR2 1.32e-22 1.59e-18
KCNV1 1.206e-21 1.45e-17
CLDN3 4.634e-21 5.58e-17
VIP 5.605e-21 6.75e-17
DLGAP2 1.275e-20 1.53e-16
CHD5 5.928e-20 7.13e-16
PAX8 2.156e-19 2.59e-15
DOC2A 2.988e-19 3.6e-15

Figure S5.  Get High-res Image As an example, this figure shows the association of CDH8 to 'HISTOLOGICAL.TYPE'. P value = 8.04e-25 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S11.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 358
  YES 165
     
  Significant markers N = 0
Methods & Data
Input

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

  • Clinical data file = GBM-TP.clin.merged.picked.txt

  • Number of patients = 523

  • Number of genes = 12042

  • Number of clinical features = 6

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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