Correlation between mRNAseq expression and clinical features
Glioblastoma Multiforme (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1FX789D
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 18210 genes and 7 clinical features across 152 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

  • 4 genes correlated to 'AGE'.

    • LOC84856|84856 ,  CBARA1|10367 ,  NOL3|8996 ,  SSH3|54961

  • 5 genes correlated to 'GENDER'.

    • CYORF15A|246126 ,  HDHD1A|8226 ,  NCRNA00183|554203 ,  CYORF15B|84663 ,  FRG1B|284802

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

    • ZNF677|342926 ,  LOC80054|80054 ,  SP3|6670 ,  IPO7|10527 ,  TIAF1|9220 ,  ...

  • No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', 'RACE', 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=4 older N=2 younger N=2
GENDER Wilcoxon test N=5 male N=5 female N=0
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION Wilcoxon test N=7 yes N=7 no N=0
RACE Kruskal-Wallis test   N=0        
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) 0.2-54 (median=9.1)
  censored N = 35
  death N = 117
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

4 genes related to 'AGE'.

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

AGE Mean (SD) 59.74 (14)
  Significant markers N = 4
  pos. correlated 2
  neg. correlated 2
List of 4 genes differentially expressed by 'AGE'

Table S3.  Get Full Table List of 4 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
LOC84856|84856 -0.3698 3.461e-06 0.063
CBARA1|10367 -0.3613 4.793e-06 0.0873
NOL3|8996 0.3484 1.087e-05 0.198
SSH3|54961 0.3481 1.112e-05 0.202
Clinical variable #3: 'GENDER'

5 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 53
  MALE 99
     
  Significant markers N = 5
  Higher in MALE 5
  Higher in FEMALE 0
List of 5 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 5 genes differentially expressed by 'GENDER'. 24 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
CYORF15A|246126 3465 1.748e-18 3.18e-14 1
HDHD1A|8226 614 8.019e-15 1.46e-10 0.883
NCRNA00183|554203 625 1.121e-14 2.04e-10 0.8809
CYORF15B|84663 2376 3.492e-14 6.35e-10 1
FRG1B|284802 3743 1.516e-05 0.276 0.7134
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) 75.75 (14)
  Significant markers N = 0
Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 102
  YES 50
     
  Significant markers N = 7
  Higher in YES 7
  Higher in NO 0
List of 7 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8.  Get Full Table List of 7 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

W(pos if higher in 'YES') wilcoxontestP Q AUC
ZNF677|342926 1332 1.801e-06 0.0328 0.7388
LOC80054|80054 3703 6.195e-06 0.113 0.7261
SP3|6670 1423 9.978e-06 0.182 0.721
IPO7|10527 1436 1.262e-05 0.23 0.7184
TIAF1|9220 3663 1.284e-05 0.234 0.7182
CEMP1|752014 3658 1.405e-05 0.256 0.7173
LPCAT4|254531 3652 1.563e-05 0.285 0.7161
Clinical variable #6: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 5
  BLACK OR AFRICAN AMERICAN 10
  WHITE 136
     
  Significant markers N = 0
Clinical variable #7: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S10.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 3
  NOT HISPANIC OR LATINO 125
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = GBM-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 152

  • Number of genes = 18210

  • Number of clinical features = 7

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)