Correlation between gene methylation status and clinical features
Glioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C15X2891
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15X2891
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features. The input file "GBMLGG-TP.meth.by_min_clin_corr.data.txt" is generated in the pipeline Methylation_Preprocess in stddata run.

Summary

Testing the association between 17098 genes and 9 clinical features across 653 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • A2M ,  A4GALT ,  A4GNT ,  AACSL ,  AAGAB ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • HCN1 ,  LOC150786 ,  TRIM58 ,  TCHH ,  RELN ,  ...

  • 30 genes correlated to 'TUMOR_TISSUE_SITE'.

    • COL1A1 ,  C5ORF62 ,  HMGB2 ,  CHRNB1 ,  LOC257358 ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  KIF4B ,  WBP11P1 ,  TLE1 ,  LOC389791 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • SIGLEC9 ,  SMCP ,  FOXS1 ,  CCL3 ,  HSPG2 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • PIK3AP1 ,  FKBP11 ,  CCDC23 ,  TWF2 ,  RCAN1 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • EMP1 ,  HTR3A ,  NEDD9 ,  MREG ,  LOC257358 ,  ...

  • 30 genes correlated to 'RACE'.

    • C6ORF52 ,  RNF135 ,  LOC253039 ,  ISCA1 ,  EIF3D ,  ...

  • 30 genes correlated to 'ETHNICITY'.

    • MAPKAPK5 ,  ASNSD1 ,  SNRPB2 ,  KIAA0556 ,  GALR1 ,  ...

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
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test N=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=22 younger N=8
TUMOR_TISSUE_SITE Wilcoxon test N=30 central nervous system N=30 brain N=0
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=30 lower score N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test N=30 not hispanic or latino N=30 hispanic or latino N=0
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 genes related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1.  Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0-211.2 (median=18.8)
  censored N = 434
  death N = 218
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
A2M 0 0 0.202
A4GALT 0 0 0.262
A4GNT 0 0 0.187
AACSL 0 0 0.2
AAGAB 0 0 0.269
AANAT 0 0 0.278
ABCA1 0 0 0.261
ABCA12 0 0 0.214
ABCA13 0 0 0.297
ABCA5 0 0 0.219
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 46.52 (15)
  Significant markers N = 30
  pos. correlated 22
  neg. correlated 8
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
HCN1 0.6454 4.223e-78 7.22e-74
LOC150786 0.6421 4.617e-77 3.95e-73
TRIM58 0.6349 7.436e-75 4.24e-71
TCHH 0.6251 5.887e-72 2.52e-68
RELN 0.5982 1.542e-64 5.27e-61
RANBP17 0.5908 1.37e-62 3.9e-59
RYR2 0.5903 1.842e-62 4.5e-59
NEFM 0.5797 8.242e-60 1.76e-56
LOC100132707 -0.5721 6.154e-58 1.17e-54
PGR 0.5628 9.882e-56 1.69e-52
Clinical variable #3: 'TUMOR_TISSUE_SITE'

30 genes related to 'TUMOR_TISSUE_SITE'.

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

TUMOR_TISSUE_SITE Labels N
  BRAIN 138
  CENTRAL NERVOUS SYSTEM 515
     
  Significant markers N = 30
  Higher in CENTRAL NERVOUS SYSTEM 30
  Higher in BRAIN 0
List of top 10 genes differentially expressed by 'TUMOR_TISSUE_SITE'

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

W(pos if higher in 'CENTRAL NERVOUS SYSTEM') wilcoxontestP Q AUC
COL1A1 66962 2.13e-57 3.64e-53 0.9422
C5ORF62 66775 9.726e-57 8.31e-53 0.9396
HMGB2 66702 1.755e-56 9.64e-53 0.9385
CHRNB1 66671 2.255e-56 9.64e-53 0.9381
LOC257358 66620 3.401e-56 1.16e-52 0.9374
SGOL1 66517 7.789e-56 2.22e-52 0.9359
NPFFR1 66436 1.491e-55 3.64e-52 0.9348
TGM5 66333 3.399e-55 6.67e-52 0.9333
COL18A1 66329 3.509e-55 6.67e-52 0.9333
NEK10 66295 4.603e-55 7.87e-52 0.9328
Clinical variable #4: 'GENDER'

30 genes related to 'GENDER'.

Table S7.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 288
  MALE 365
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
UTP14C 102391 2.928e-96 5.01e-92 0.974
KIF4B 25445 9.515e-30 8.13e-26 0.7579
WBP11P1 75493 9.612e-22 5.48e-18 0.7182
TLE1 30165 8.264e-21 3.53e-17 0.713
LOC389791 74841 1.295e-20 4.43e-17 0.712
ZC3H14 30685 6.305e-20 1.8e-16 0.7081
UBAP2 34148 1.447e-14 3.53e-11 0.6752
ZNF839 34460 3.975e-14 8.5e-11 0.6722
FRG1B 35159 3.601e-13 6.84e-10 0.6655
VKORC1 68813 1.12e-11 1.92e-08 0.6546
Clinical variable #5: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 204
  YES 404
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
SIGLEC9 23031 6.242e-19 6.12e-15 0.7206
SMCP 23202 1.32e-18 6.12e-15 0.7185
FOXS1 23264 1.729e-18 6.12e-15 0.7177
CCL3 23268 1.759e-18 6.12e-15 0.7177
HSPG2 23272 1.79e-18 6.12e-15 0.7176
MYCT1 23477 4.338e-18 1.24e-14 0.7151
TPRG1 23641 8.745e-18 1.97e-14 0.7132
HTR3A 23653 9.203e-18 1.97e-14 0.713
GOLGA6A 23752 1.4e-17 2.66e-14 0.7118
SPOCD1 23782 1.59e-17 2.72e-14 0.7114
Clinical variable #6: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

Table S11.  Basic characteristics of clinical feature: 'KARNOFSKY_PERFORMANCE_SCORE'

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 84.11 (15)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S12.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
PIK3AP1 0.3841 1.195e-15 1.8e-11
FKBP11 0.3787 3.169e-15 1.8e-11
CCDC23 0.3786 3.225e-15 1.8e-11
TWF2 0.3761 5.088e-15 1.8e-11
RCAN1 0.3752 5.912e-15 1.8e-11
LMO4 0.3736 7.975e-15 1.8e-11
HS6ST1 0.3722 1.006e-14 1.8e-11
IFI16 0.3718 1.082e-14 1.8e-11
CDCP1 0.3718 1.084e-14 1.8e-11
DEGS1 0.3717 1.107e-14 1.8e-11
Clinical variable #7: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

Table S13.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  ASTROCYTOMA 194
  GLIOBLASTOMA MULTIFORME (GBM) 21
  OLIGOASTROCYTOMA 130
  OLIGODENDROGLIOMA 191
  TREATED PRIMARY GBM 1
  UNTREATED PRIMARY (DE NOVO) GBM 116
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
EMP1 7.183e-71 1.23e-66
HTR3A 5.255e-66 4.49e-62
NEDD9 1.752e-65 9.99e-62
MREG 3.112e-65 1.33e-61
LOC257358 2.932e-64 1e-60
REST 6.94e-64 1.98e-60
SLC2A4RG 2.562e-63 6.26e-60
CD79A 1.121e-62 2.4e-59
LIMS1 3.59e-62 6.82e-59
PHYH 5.683e-62 9.72e-59
Clinical variable #8: 'RACE'

30 genes related to 'RACE'.

Table S15.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 45
  WHITE 582
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S16.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
C6ORF52 6.275e-17 1.07e-12
RNF135 3.401e-15 2.91e-11
LOC253039 1.396e-14 7.95e-11
ISCA1 1.71e-10 6.66e-07
EIF3D 1.949e-10 6.66e-07
CS 2.902e-09 8.27e-06
LOC349114 6.077e-09 1.48e-05
NOC4L 1.017e-08 2.06e-05
UBTF 1.083e-08 2.06e-05
PGM2 1.322e-08 2.26e-05
Clinical variable #9: 'ETHNICITY'

30 genes related to 'ETHNICITY'.

Table S17.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 33
  NOT HISPANIC OR LATINO 544
     
  Significant markers N = 30
  Higher in NOT HISPANIC OR LATINO 30
  Higher in HISPANIC OR LATINO 0
List of top 10 genes differentially expressed by 'ETHNICITY'

Methods & Data
Input

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

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

  • Number of patients = 653

  • Number of genes = 17098

  • Number of clinical features = 9

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[4] 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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