Correlation between gene methylation status and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1513W4N
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 gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1513W4N
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 17401 genes and 7 clinical features across 198 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 444 genes correlated to 'Time to Death'.

    • HS3ST4 ,  SSTR1 ,  RAB6C ,  ZNF492 ,  GALNT14 ,  ...

  • 146 genes correlated to 'AGE'.

    • CD163L1 ,  HOXD8 ,  LOC150786 ,  ADAMTSL3 ,  PAX9 ,  ...

  • 10 genes correlated to 'GENDER'.

    • UTP14C ,  POLDIP3 ,  GLUD1 ,  WBP11P1 ,  TFDP1 ,  ...

  • 877 genes correlated to 'HISTOLOGICAL.TYPE'.

    • BVES ,  REST ,  MAPKAP1 ,  SNAPC2 ,  SLC2A4RG ,  ...

  • 57 genes correlated to 'HISTOLOGICCLASSIFICATION'.

    • SLC11A2 ,  FMOD ,  ADAM19 ,  PRDM1 ,  SCGB1A1 ,  ...

  • No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', and '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=444 shorter survival N=76 longer survival N=368
AGE Spearman correlation test N=146 older N=118 younger N=28
GENDER t test N=10 male N=4 female N=6
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=877        
HISTOLOGICCLASSIFICATION t test N=57 grade iii N=3 grade ii N=54
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
Clinical variable #1: 'Time to Death'

444 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 = 152
  death N = 45
     
  Significant markers N = 444
  associated with shorter survival 76
  associated with longer survival 368
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
HS3ST4 191 3.061e-12 5.3e-08 0.77
SSTR1 161 1.954e-11 3.4e-07 0.782
RAB6C 2401 3.114e-11 5.4e-07 0.801
ZNF492 86 3.509e-11 6.1e-07 0.675
GALNT14 121 6.793e-11 1.2e-06 0.793
HPD 0 6.994e-11 1.2e-06 0.293
IRF2 0 7.467e-11 1.3e-06 0.321
CD274 0.01 8.081e-11 1.4e-06 0.283
LPAR3 171 1.012e-10 1.8e-06 0.75
ATF3 0 1.472e-10 2.6e-06 0.277

Figure S1.  Get High-res Image As an example, this figure shows the association of HS3ST4 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 3.06e-12 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

146 genes related to 'AGE'.

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

AGE Mean (SD) 43.01 (13)
  Significant markers N = 146
  pos. correlated 118
  neg. correlated 28
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
CD163L1 0.541 1.899e-16 3.3e-12
HOXD8 0.528 1.31e-15 2.28e-11
LOC150786 0.5218 3.209e-15 5.58e-11
ADAMTSL3 0.5028 4.405e-14 7.66e-10
PAX9 0.4997 6.628e-14 1.15e-09
SLC18A2 0.4947 1.278e-13 2.22e-09
GALNT14 0.4835 5.35e-13 9.31e-09
RAB6C 0.4805 7.876e-13 1.37e-08
SSTR4 0.474 1.738e-12 3.02e-08
HOXD11 0.4659 4.637e-12 8.06e-08

Figure S2.  Get High-res Image As an example, this figure shows the association of CD163L1 to 'AGE'. P value = 1.9e-16 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

10 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 87
  MALE 111
     
  Significant markers N = 10
  Higher in MALE 4
  Higher in FEMALE 6
List of 10 genes differentially expressed by 'GENDER'

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

T(pos if higher in 'MALE') ttestP Q AUC
UTP14C 20.98 1.893e-40 3.29e-36 0.9765
POLDIP3 -15.04 1.41e-30 2.45e-26 0.941
GLUD1 -10.5 1.188e-20 2.07e-16 0.8288
WBP11P1 8.41 1.752e-14 3.05e-10 0.8308
TFDP1 -7.32 7.71e-12 1.34e-07 0.8671
FAM35A -7.01 9.371e-11 1.63e-06 0.7708
KIF4B -6.94 1.181e-10 2.05e-06 0.7522
ZNF839 -5.84 2.583e-08 0.000449 0.7724
CCDC121 5.48 1.629e-07 0.00283 0.7258
AES 5.34 2.56e-07 0.00445 0.7116

Figure S3.  Get High-res Image As an example, this figure shows the association of UTP14C to 'GENDER'. P value = 1.89e-40 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) 88.28 (11)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

877 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 58
  OLIGOASTROCYTOMA 52
  OLIGODENDROGLIOMA 87
     
  Significant markers N = 877
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
BVES 9.878e-17 1.72e-12
REST 6.399e-16 1.11e-11
MAPKAP1 2.878e-15 5.01e-11
SNAPC2 3.397e-15 5.91e-11
SLC2A4RG 8.053e-15 1.4e-10
GLIS3 9.227e-15 1.61e-10
S100PBP 1.956e-14 3.4e-10
EMP1 5.592e-14 9.73e-10
CBX2 5.991e-14 1.04e-09
NFIA 6.401e-14 1.11e-09

Figure S4.  Get High-res Image As an example, this figure shows the association of BVES to 'HISTOLOGICAL.TYPE'. P value = 9.88e-17 with ANOVA analysis.

Clinical variable #6: 'HISTOLOGICCLASSIFICATION'

57 genes related to 'HISTOLOGICCLASSIFICATION'.

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

HISTOLOGICCLASSIFICATION Labels N
  GRADE II 91
  GRADE III 106
     
  Significant markers N = 57
  Higher in GRADE III 3
  Higher in GRADE II 54
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
SLC11A2 5.68 6.049e-08 0.00105 0.6889
FMOD -5.51 1.562e-07 0.00272 0.6706
ADAM19 -5.43 1.905e-07 0.00332 0.7028
PRDM1 -5.46 2.092e-07 0.00364 0.7019
SCGB1A1 -5.44 2.216e-07 0.00386 0.6815
MUC12 -5.32 3.037e-07 0.00528 0.7182
A4GNT -5.34 3.039e-07 0.00529 0.6756
LDLRAD2 -5.35 3.196e-07 0.00556 0.6767
LCE1E -5.24 4.288e-07 0.00746 0.6968
NR5A1 -5.23 4.705e-07 0.00818 0.6865

Figure S5.  Get High-res Image As an example, this figure shows the association of SLC11A2 to 'HISTOLOGICCLASSIFICATION'. P value = 6.05e-08 with T-test analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 98
  YES 100
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = LGG-TP.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 198

  • Number of genes = 17401

  • 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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