Correlation between gene methylation status and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1HD7T47
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/C1HD7T47
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 20044 genes and 6 clinical features across 276 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 437 genes correlated to 'AGE'.

    • TRIM58 ,  SHISA2 ,  TCHH ,  LOC150786 ,  GALNT14 ,  ...

  • 15 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  POLDIP3 ,  RNU12 ,  WBP11P1 ,  ...

  • 1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • ELOVL4

  • 1257 genes correlated to 'HISTOLOGICAL.TYPE'.

    • BVES ,  MAPKAP1 ,  SLC2A4RG ,  REST ,  CCDC88C ,  ...

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

    • HSPA13 ,  ANKRD28 ,  FYTTD1 ,  KIAA0226 ,  DAZL ,  ...

  • 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=437 older N=231 younger N=206
GENDER t test N=15 male N=5 female N=10
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=1 lower score N=0
HISTOLOGICAL TYPE ANOVA test N=1257        
RADIATIONS RADIATION REGIMENINDICATION t test N=140 yes N=86 no N=54
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=15.3)
  censored N = 220
  death N = 55
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

437 genes related to 'AGE'.

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

AGE Mean (SD) 43 (13)
  Significant markers N = 437
  pos. correlated 231
  neg. correlated 206
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
TRIM58 0.5629 1.793e-24 3.59e-20
SHISA2 0.5417 1.904e-22 3.82e-18
TCHH 0.5402 2.627e-22 5.26e-18
LOC150786 0.5159 3.554e-20 7.12e-16
GALNT14 0.5146 4.596e-20 9.21e-16
FOXE3 0.5051 3.823e-19 7.66e-15
HOXD8 0.4995 8.025e-19 1.61e-14
TFAP2B 0.4946 1.955e-18 3.92e-14
RELN 0.485 1.083e-17 2.17e-13
PAX9 0.4826 1.668e-17 3.34e-13

Figure S1.  Get High-res Image As an example, this figure shows the association of TRIM58 to 'AGE'. P value = 1.79e-24 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

15 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 128
  MALE 148
     
  Significant markers N = 15
  Higher in MALE 5
  Higher in FEMALE 10
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
ALG11__1 27.85 2.154e-66 4.32e-62 0.979
UTP14C 27.85 2.154e-66 4.32e-62 0.979
POLDIP3 -18.58 1.609e-46 3.22e-42 0.9405
RNU12 -18.58 1.609e-46 3.22e-42 0.9405
WBP11P1 9.48 2.142e-18 4.29e-14 0.8143
TFDP1 -9.33 4.645e-18 9.31e-14 0.8708
KIF4B -9.38 9.668e-18 1.94e-13 0.7695
HAX1 -9.01 2.753e-16 5.52e-12 0.7959
ZNF839 -7.42 1.714e-12 3.43e-08 0.7693
LOC389791 6.43 5.688e-10 1.14e-05 0.7238

Figure S2.  Get High-res Image As an example, this figure shows the association of ALG11__1 to 'GENDER'. P value = 2.15e-66 with T-test analysis.

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

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

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

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 88.09 (11)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S7.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
ELOVL4 0.4032 1.133e-06 0.0227

Figure S3.  Get High-res Image As an example, this figure shows the association of ELOVL4 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 1.13e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

1257 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 89
  OLIGOASTROCYTOMA 75
  OLIGODENDROGLIOMA 112
     
  Significant markers N = 1257
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 9.51e-22 1.91e-17
MAPKAP1 9.39e-21 1.88e-16
SLC2A4RG 3.34e-20 6.69e-16
REST 6.393e-20 1.28e-15
CCDC88C 9.009e-20 1.81e-15
CBX2 1.021e-19 2.05e-15
CTGF 4.272e-19 8.56e-15
GLIS3 2.083e-18 4.17e-14
EMP1 9.121e-18 1.83e-13
TMC6 1.909e-17 3.82e-13

Figure S4.  Get High-res Image As an example, this figure shows the association of BVES to 'HISTOLOGICAL.TYPE'. P value = 9.51e-22 with ANOVA analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 85
  YES 191
     
  Significant markers N = 140
  Higher in YES 86
  Higher in NO 54
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 8.33 3.95e-15 7.92e-11 0.7126
ANKRD28 7.87 8.606e-14 1.72e-09 0.7143
FYTTD1 6.91 3.804e-11 7.62e-07 0.6955
KIAA0226 6.91 3.804e-11 7.62e-07 0.6955
DAZL 6.9 3.938e-11 7.89e-07 0.6896
PPP1R8 6.79 8.213e-11 1.65e-06 0.7259
FLRT3 6.76 8.755e-11 1.75e-06 0.702
MACROD2 6.76 8.755e-11 1.75e-06 0.702
AASDH 6.66 1.957e-10 3.92e-06 0.7097
PCYOX1 6.57 2.497e-10 5e-06 0.6325

Figure S5.  Get High-res Image As an example, this figure shows the association of HSPA13 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 3.95e-15 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 = 276

  • Number of genes = 20044

  • 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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