Correlation between gene methylation status and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1M9079P
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1M9079P
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 20136 genes and 6 clinical features across 357 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 866 genes correlated to 'AGE'.

    • TCHH ,  TRIM58 ,  SHISA2 ,  LOC150786 ,  FOXE3 ,  ...

  • 10 genes correlated to 'GENDER'.

    • ALG11__2 ,  UTP14C ,  POLDIP3 ,  RNU12 ,  KIF4B ,  ...

  • 52 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • PREP ,  TRIM26 ,  LNX1 ,  LAMB2L ,  DEGS1 ,  ...

  • 2101 genes correlated to 'HISTOLOGICAL.TYPE'.

    • MAPKAP1 ,  BVES ,  SLC2A4RG ,  REST ,  CBX2 ,  ...

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

    • HSPA13 ,  DAZL ,  PPP1R8 ,  HNRNPK__1 ,  MIR7-1 ,  ...

  • No genes correlated to 'Time to Death'

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=866 older N=348 younger N=518
GENDER t test N=10 male N=4 female N=6
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=52 higher score N=52 lower score N=0
HISTOLOGICAL TYPE ANOVA test N=2101        
RADIATIONS RADIATION REGIMENINDICATION t test N=624 yes N=376 no N=248
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) 0-211.2 (median=14.6)
  censored N = 290
  death N = 64
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

866 genes related to 'AGE'.

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

AGE Mean (SD) 43.46 (14)
  Significant markers N = 866
  pos. correlated 348
  neg. correlated 518
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
TCHH 0.5448 5.502e-29 1.11e-24
TRIM58 0.5378 3.757e-28 7.57e-24
SHISA2 0.5313 2.119e-27 4.27e-23
LOC150786 0.5258 9.044e-27 1.82e-22
FOXE3 0.5091 8.395e-25 1.69e-20
ADAMTSL3 0.5077 8.768e-25 1.77e-20
GALNT14 0.502 3.426e-24 6.9e-20
RELN 0.4972 1.079e-23 2.17e-19
SLC22A16 0.4938 2.409e-23 4.85e-19
TFAP2B 0.4927 3.13e-23 6.3e-19

Figure S1.  Get High-res Image As an example, this figure shows the association of TCHH to 'AGE'. P value = 5.5e-29 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

10 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 167
  MALE 190
     
  Significant markers N = 10
  Higher in MALE 4
  Higher in FEMALE 6
List of 10 genes differentially expressed by 'GENDER'

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

T(pos if higher in 'MALE') ttestP Q AUC
ALG11__2 33.65 7.068e-90 1.42e-85 0.983
UTP14C 33.65 7.068e-90 1.42e-85 0.983
POLDIP3 -20.17 1.121e-56 2.26e-52 0.9349
RNU12 -20.17 1.121e-56 2.26e-52 0.9349
KIF4B -11.1 5.943e-24 1.2e-19 0.7791
WBP11P1 9.24 3.531e-18 7.11e-14 0.761
B3GNT1__1 9.48 7.957e-18 1.6e-13 0.8276
TLE1 -6.48 3.313e-10 6.67e-06 0.7247
ZNF839 -6.35 7.226e-10 1.45e-05 0.7108
FRG1B -5.14 4.818e-07 0.0097 0.659

Figure S2.  Get High-res Image As an example, this figure shows the association of ALG11__2 to 'GENDER'. P value = 7.07e-90 with T-test analysis.

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

52 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 87.94 (12)
  Significant markers N = 52
  pos. correlated 52
  neg. correlated 0
List of top 10 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S7.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
PREP 0.3989 5.369e-09 0.000108
TRIM26 0.3929 9.492e-09 0.000191
LNX1 0.3636 1.304e-07 0.00263
LAMB2L 0.3633 1.337e-07 0.00269
DEGS1 0.3615 1.549e-07 0.00312
GXYLT1 0.3612 1.6e-07 0.00322
PTGFRN 0.3606 1.683e-07 0.00339
PTN 0.3596 1.83e-07 0.00368
ALS2CR4 0.3571 2.245e-07 0.00452
FAM131A 0.3559 2.476e-07 0.00498

Figure S3.  Get High-res Image As an example, this figure shows the association of PREP to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 5.37e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

2101 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 122
  OLIGOASTROCYTOMA 98
  OLIGODENDROGLIOMA 137
     
  Significant markers N = 2101
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
MAPKAP1 5.867e-31 1.18e-26
BVES 3.587e-28 7.22e-24
SLC2A4RG 4.917e-28 9.9e-24
REST 1.166e-26 2.35e-22
CBX2 3.073e-25 6.19e-21
GLIS3 4.17e-25 8.39e-21
CCDC88C 6.911e-25 1.39e-20
SNAPC2 1.103e-23 2.22e-19
ASAP1 1.629e-23 3.28e-19
P4HA1 4.191e-23 8.44e-19

Figure S4.  Get High-res Image As an example, this figure shows the association of MAPKAP1 to 'HISTOLOGICAL.TYPE'. P value = 5.87e-31 with ANOVA analysis.

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

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

Table S10.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 85
  YES 272
     
  Significant markers N = 624
  Higher in YES 376
  Higher in NO 248
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
HSPA13 12.96 1.468e-31 2.95e-27 0.7917
DAZL 11.13 1.897e-24 3.82e-20 0.77
PPP1R8 10.99 3.888e-23 7.83e-19 0.7978
HNRNPK__1 10.43 6.845e-22 1.38e-17 0.7614
MIR7-1 10.43 6.845e-22 1.38e-17 0.7614
ZFP91 -10.4 1.172e-21 2.36e-17 0.7578
ZFP91-CNTF -10.4 1.172e-21 2.36e-17 0.7578
ANKRD17 10.18 1.947e-21 3.92e-17 0.7594
AMY2B -10.12 2.656e-21 5.35e-17 0.7731
RNPC3__1 -10.12 2.656e-21 5.35e-17 0.7731

Figure S5.  Get High-res Image As an example, this figure shows the association of HSPA13 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.47e-31 with T-test analysis.

Methods & Data
Input

  • Expresson data file = LGG-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 357

  • Number of genes = 20136

  • 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

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)
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