Glioblastoma Multiforme: Correlation between mRNAseq expression and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

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

Summary

Testing the association between 18203 genes and 6 clinical features across 159 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • CBARA1|10367

  • 27 genes correlated to 'GENDER'.

    • XIST|7503 ,  RPS4Y1|6192 ,  DDX3Y|8653 ,  KDM5D|8284 ,  USP9Y|8287 ,  ...

  • 1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • ZBTB33|10009

  • 4 genes correlated to 'NEOADJUVANT.THERAPY'.

    • NAIP|4671 ,  MYO15B|80022 ,  DDX10|1662 ,  C16ORF70|80262

  • No genes correlated to 'Time to Death', and 'KARNOFSKY.PERFORMANCE.SCORE'.

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=0        
AGE Spearman correlation test N=1 older N=0 younger N=1
GENDER t test N=27 male N=15 female N=12
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=1 yes N=0 no N=1
NEOADJUVANT THERAPY t test N=4 yes N=3 no N=1
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) 0.2-54 (median=8.8)
  censored N = 53
  death N = 106
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 59.77 (13)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'AGE' by Spearman correlation test

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

SpearmanCorr corrP Q
CBARA1|10367 -0.3796 8.048e-07 0.0147

Figure S1.  Get High-res Image As an example, this figure shows the association of CBARA1|10367 to 'AGE'. P value = 8.05e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

27 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 56
  MALE 103
     
  Significant markers N = 27
  Higher in MALE 15
  Higher in FEMALE 12
List of top 10 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
XIST|7503 -38.75 6.649e-57 1.21e-52 1
RPS4Y1|6192 49.77 8.603e-57 1.57e-52 1
DDX3Y|8653 51.38 1.114e-51 2.03e-47 1
KDM5D|8284 45.62 4.365e-51 7.94e-47 1
USP9Y|8287 51.16 5.952e-50 1.08e-45 1
CYORF15A|246126 44.58 5.64e-47 1.03e-42 1
TSIX|9383 -23.01 3.45e-43 6.28e-39 0.9977
ZFY|7544 49.76 1.33e-41 2.42e-37 1
EIF1AY|9086 42.31 8.049e-41 1.46e-36 1
PRKY|5616 26.57 4.237e-37 7.71e-33 0.9989

Figure S2.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 6.65e-57 with T-test analysis.

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

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

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 76.3 (15)
  Significant markers N = 0
Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 103
  YES 56
     
  Significant markers N = 1
  Higher in YES 0
  Higher in NO 1
List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8.  Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
ZBTB33|10009 -5.15 1.072e-06 0.0195 0.7243

Figure S3.  Get High-res Image As an example, this figure shows the association of ZBTB33|10009 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.07e-06 with T-test analysis.

Clinical variable #6: 'NEOADJUVANT.THERAPY'

4 genes related to 'NEOADJUVANT.THERAPY'.

Table S9.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 80
  YES 79
     
  Significant markers N = 4
  Higher in YES 3
  Higher in NO 1
List of 4 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S10.  Get Full Table List of 4 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
NAIP|4671 5.31 3.75e-07 0.00683 0.734
MYO15B|80022 5 1.601e-06 0.0291 0.7059
DDX10|1662 -4.95 2.021e-06 0.0368 0.6934
C16ORF70|80262 4.91 2.316e-06 0.0421 0.7215

Figure S4.  Get High-res Image As an example, this figure shows the association of NAIP|4671 to 'NEOADJUVANT.THERAPY'. P value = 3.75e-07 with T-test analysis.

Methods & Data
Input

  • Expresson data file = GBM.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 159

  • Number of genes = 18203

  • 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

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] 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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle