Correlation between mRNAseq expression and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1N29TX3
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Brain Lower Grade Glioma (Primary solid tumor cohort) - 21 April 2013: Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1N29TX3
Overview
Introduction

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

Summary

Testing the association between 18364 genes and 7 clinical features across 206 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 357 genes correlated to 'Time to Death'.

    • RANBP17|64901 ,  ZRANB1|54764 ,  CBARA1|10367 ,  PPA1|5464 ,  MRPS16|51021 ,  ...

  • 81 genes correlated to 'AGE'.

    • PRSS35|167681 ,  SIM2|6493 ,  SRXN1|140809 ,  TRMT2B|79979 ,  GRPEL2|134266 ,  ...

  • 30 genes correlated to 'GENDER'.

    • XIST|7503 ,  ZFY|7544 ,  RPS4Y1|6192 ,  PRKY|5616 ,  DDX3Y|8653 ,  ...

  • 2633 genes correlated to 'HISTOLOGICAL.TYPE'.

    • AK2|204 ,  TXNDC12|51060 ,  CAP1|10487 ,  RHOC|389 ,  WLS|79971 ,  ...

  • 216 genes correlated to 'HISTOLOGICCLASSIFICATION'.

    • IGFBP2|3485 ,  AURKA|6790 ,  CD101|9398 ,  PTTG1|9232 ,  C4ORF12|404201 ,  ...

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

    • ZNF362|149076 ,  ZDHHC21|340481 ,  PHC2|1912 ,  MAD2L2|10459 ,  STARD4|134429 ,  ...

  • No genes correlated to '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=357 shorter survival N=153 longer survival N=204
AGE Spearman correlation test N=81 older N=36 younger N=45
GENDER t test N=30 male N=17 female N=13
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=2633        
HISTOLOGICCLASSIFICATION t test N=216 grade iii N=151 grade ii N=65
RADIATIONS RADIATION REGIMENINDICATION t test N=8 yes N=2 no N=6
Clinical variable #1: 'Time to Death'

357 genes related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0-211.2 (median=13.4)
  censored N = 154
  death N = 51
     
  Significant markers N = 357
  associated with shorter survival 153
  associated with longer survival 204
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
RANBP17|64901 0.62 6.307e-12 1.2e-07 0.333
ZRANB1|54764 0.12 1.832e-11 3.4e-07 0.242
CBARA1|10367 0.14 1.838e-11 3.4e-07 0.248
PPA1|5464 0.13 6.82e-11 1.3e-06 0.266
MRPS16|51021 0.04 8.126e-11 1.5e-06 0.286
CUEDC2|79004 0.09 9.864e-11 1.8e-06 0.258
LOC254559|254559 0.53 1.356e-10 2.5e-06 0.246
NTNG2|84628 0.48 1.536e-10 2.8e-06 0.225
ACTR1A|10121 0.09 1.684e-10 3.1e-06 0.266
ZNF217|7764 3.7 1.744e-10 3.2e-06 0.761

Figure S1.  Get High-res Image As an example, this figure shows the association of RANBP17|64901 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 6.31e-12 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

81 genes related to 'AGE'.

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

AGE Mean (SD) 43.17 (13)
  Significant markers N = 81
  pos. correlated 36
  neg. correlated 45
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
PRSS35|167681 -0.4292 1.215e-10 2.23e-06
SIM2|6493 0.4074 1.217e-09 2.23e-05
SRXN1|140809 0.4027 1.969e-09 3.62e-05
TRMT2B|79979 0.4002 2.529e-09 4.64e-05
GRPEL2|134266 0.3968 3.539e-09 6.5e-05
RBM17|84991 -0.395 4.234e-09 7.77e-05
SFRP2|6423 -0.3916 5.89e-09 0.000108
SYT6|148281 -0.3886 7.843e-09 0.000144
LSG1|55341 0.3839 1.221e-08 0.000224
PARP3|10039 0.3802 1.732e-08 0.000318

Figure S2.  Get High-res Image As an example, this figure shows the association of PRSS35|167681 to 'AGE'. P value = 1.21e-10 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 89
  MALE 117
     
  Significant markers N = 30
  Higher in MALE 17
  Higher in FEMALE 13
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
XIST|7503 -56.4 4.721e-123 8.67e-119 1
ZFY|7544 64.24 6.673e-95 1.22e-90 1
RPS4Y1|6192 57.15 9.674e-80 1.78e-75 1
PRKY|5616 34.16 3.579e-72 6.57e-68 0.9998
DDX3Y|8653 67.75 1.202e-69 2.21e-65 1
KDM5D|8284 66.36 9.546e-69 1.75e-64 1
NLGN4Y|22829 34.34 1.808e-68 3.32e-64 0.9965
USP9Y|8287 69.81 2.508e-68 4.6e-64 1
TSIX|9383 -26.61 3.123e-60 5.73e-56 1
EIF1AY|9086 71.19 7.588e-51 1.39e-46 1

Figure S3.  Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 4.72e-123 with T-test analysis.

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

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

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 88.24 (10)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

2633 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 62
  OLIGOASTROCYTOMA 54
  OLIGODENDROGLIOMA 89
     
  Significant markers N = 2633
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
AK2|204 1.481e-21 2.72e-17
TXNDC12|51060 1.794e-21 3.29e-17
CAP1|10487 7.385e-21 1.36e-16
RHOC|389 7.938e-21 1.46e-16
WLS|79971 1.327e-20 2.44e-16
TRAPPC3|27095 1.731e-20 3.18e-16
SEP15|9403 2.452e-20 4.5e-16
PAFAH2|5051 3.458e-20 6.35e-16
GNG5|2787 6.748e-20 1.24e-15
WDR77|79084 1.116e-19 2.05e-15

Figure S4.  Get High-res Image As an example, this figure shows the association of AK2|204 to 'HISTOLOGICAL.TYPE'. P value = 1.48e-21 with ANOVA analysis.

Clinical variable #6: 'HISTOLOGICCLASSIFICATION'

216 genes related to 'HISTOLOGICCLASSIFICATION'.

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

HISTOLOGICCLASSIFICATION Labels N
  GRADE II 94
  GRADE III 111
     
  Significant markers N = 216
  Higher in GRADE III 151
  Higher in GRADE II 65
List of top 10 genes differentially expressed by 'HISTOLOGICCLASSIFICATION'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICCLASSIFICATION'

T(pos if higher in 'GRADE III') ttestP Q AUC
IGFBP2|3485 6.38 1.528e-09 2.81e-05 0.7095
AURKA|6790 6.16 3.964e-09 7.28e-05 0.7285
CD101|9398 6.07 6.533e-09 0.00012 0.7142
PTTG1|9232 5.9 1.494e-08 0.000274 0.72
C4ORF12|404201 -5.89 1.698e-08 0.000312 0.7128
TOR1B|27348 5.89 1.864e-08 0.000342 0.6907
CDCA8|55143 5.85 1.942e-08 0.000357 0.7099
FAM46B|115572 5.8 2.61e-08 0.000479 0.7152
WEE1|7465 5.79 2.704e-08 0.000496 0.7114
KPNA2|3838 5.79 2.706e-08 0.000497 0.7118

Figure S5.  Get High-res Image As an example, this figure shows the association of IGFBP2|3485 to 'HISTOLOGICCLASSIFICATION'. P value = 1.53e-09 with T-test analysis.

Clinical variable #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S12.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 102
  YES 104
     
  Significant markers N = 8
  Higher in YES 2
  Higher in NO 6
List of 8 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S13.  Get Full Table List of 8 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
ZNF362|149076 -5.45 1.427e-07 0.00262 0.7044
ZDHHC21|340481 5.45 1.494e-07 0.00274 0.7171
PHC2|1912 -5.37 2.135e-07 0.00392 0.7015
MAD2L2|10459 -5.37 2.157e-07 0.00396 0.7025
STARD4|134429 5.3 3.038e-07 0.00558 0.7073
ANP32A|8125 -5.03 1.049e-06 0.0193 0.7036
ARMC7|79637 -5.01 1.193e-06 0.0219 0.676
BTF3L4|91408 -4.88 2.187e-06 0.0401 0.6828

Figure S6.  Get High-res Image As an example, this figure shows the association of ZNF362|149076 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.43e-07 with T-test analysis.

Methods & Data
Input

  • Expresson data file = LGG-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 206

  • Number of genes = 18364

  • 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

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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