Correlation between gene methylation status and clinical features
Glioma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1QR4W47
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QR4W47
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 20122 genes and 9 clinical features across 610 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

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

  • 30 genes correlated to 'PRIMARY_SITE_OF_DISEASE'.

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

  • 30 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  KIF4B ,  WBP11P1 ,  TLE1 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • LMO4 ,  RCAN1 ,  STXBP4 ,  BTBD16 ,  PREP ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • EMP1 ,  HTR3A ,  MREG ,  NEDD9 ,  SLC2A4RG ,  ...

  • 30 genes correlated to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

    • EFCAB7__2 ,  ITGB3BP__1 ,  PPP1R8 ,  CEP120 ,  SCYL2__1 ,  ...

  • 30 genes correlated to 'RACE'.

    • C6ORF52__1 ,  PAK1IP1__1 ,  RNF135 ,  LOC253039 ,  PSMD5 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', and 'ETHNICITY'.

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=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=23 younger N=7
PRIMARY_SITE_OF_DISEASE Wilcoxon test N=30 central nervous system N=30 brain N=0
GENDER Wilcoxon test N=30 male N=30 female 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        
RADIATIONS_RADIATION_REGIMENINDICATION Wilcoxon test N=30 yes N=30 no N=0
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene 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=16.3)
  censored N = 430
  death N = 179
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 46.69 (15)
  Significant markers N = 30
  pos. correlated 23
  neg. correlated 7
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

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

SpearmanCorr corrP Q
HCN1 0.6415 6.739e-72 1.36e-67
LOC150786 0.6378 7.58e-71 7.63e-67
TRIM58 0.6325 2.303e-69 1.54e-65
TCHH 0.6196 7.523e-66 3.78e-62
RELN 0.5917 8.468e-59 3.41e-55
RYR2 0.591 1.259e-58 4.22e-55
RANBP17 0.5855 2.591e-57 7.45e-54
NEFM 0.5739 1.202e-54 3.02e-51
LOC100132707 -0.5661 6.954e-53 1.55e-49
PGR 0.56 1.457e-51 2.93e-48
Clinical variable #3: 'PRIMARY_SITE_OF_DISEASE'

30 genes related to 'PRIMARY_SITE_OF_DISEASE'.

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

PRIMARY_SITE_OF_DISEASE Labels N
  BRAIN 135
  CENTRAL NERVOUS SYSTEM 475
     
  Significant markers N = 30
  Higher in CENTRAL NERVOUS SYSTEM 30
  Higher in BRAIN 0
List of top 10 genes differentially expressed by 'PRIMARY_SITE_OF_DISEASE'

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

W(pos if higher in 'CENTRAL NERVOUS SYSTEM') wilcoxontestP Q AUC
COL1A1 60555 5.158e-56 1.04e-51 0.9443
C5ORF62 60442 1.385e-55 1.39e-51 0.9426
LOC257358 60295 4.981e-55 3.03e-51 0.9403
HMGB2 60273 6.029e-55 3.03e-51 0.9399
CHRNB1 60124 2.188e-54 8.5e-51 0.9376
NPFFR1 60107 2.534e-54 8.5e-51 0.9373
TGM5 60082 3.144e-54 9.04e-51 0.937
CDCP1 60063 3.702e-54 9.31e-51 0.9367
SGOL1 60033 4.793e-54 1.07e-50 0.9362
EMP1 60009 5.892e-54 1.12e-50 0.9358
Clinical variable #4: 'GENDER'

30 genes related to 'GENDER'.

Table S6.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 265
  MALE 345
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S7.  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
ALG11__1 88850 6.282e-89 6.32e-85 0.9718
UTP14C 88850 6.282e-89 6.32e-85 0.9718
KIF4B 21620 5.959e-29 4e-25 0.7635
WBP11P1 66430 7.836e-22 3.94e-18 0.7266
TLE1 25718 1.915e-20 7.71e-17 0.7187
LOC389791 65118 2.385e-19 6.85e-16 0.7123
PTGES2 65118 2.385e-19 6.85e-16 0.7123
ZC3H14 27044 5.044e-18 1.27e-14 0.7042
UBAP2 29383 3.782e-14 8.46e-11 0.6786
ZNF839 29584 7.713e-14 1.55e-10 0.6764
Clinical variable #5: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

Table S8.  Basic characteristics of clinical feature: 'KARNOFSKY_PERFORMANCE_SCORE'

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 85.15 (14)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S9.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
LMO4 0.4126 1.965e-16 2.77e-12
RCAN1 0.4107 2.751e-16 2.77e-12
STXBP4 0.405 7.75e-16 3.4e-12
BTBD16 0.4043 8.795e-16 3.4e-12
PREP 0.4041 9.093e-16 3.4e-12
KCNN4 0.4034 1.015e-15 3.4e-12
ITPK1 0.4008 1.613e-15 3.87e-12
NCRNA00203 0.4008 1.613e-15 3.87e-12
IFI16 0.4001 1.839e-15 3.87e-12
C3AR1 0.3993 2.105e-15 3.87e-12
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  ASTROCYTOMA 176
  GLIOBLASTOMA MULTIFORME (GBM) 18
  OLIGOASTROCYTOMA 119
  OLIGODENDROGLIOMA 180
  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 S11.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
EMP1 2.055e-67 4.14e-63
HTR3A 7.348e-64 7.39e-60
MREG 5.031e-63 3.37e-59
NEDD9 2.397e-62 1.21e-58
SLC2A4RG 1.044e-61 4.2e-58
LOC257358 1.38e-61 4.63e-58
REST 2.617e-61 7.52e-58
CD79A 7.782e-61 1.96e-57
GJC1 7.638e-60 1.71e-56
PHYH 1.635e-59 3.29e-56
Clinical variable #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

30 genes related to 'RADIATIONS_RADIATION_REGIMENINDICATION'.

Table S12.  Basic characteristics of clinical feature: 'RADIATIONS_RADIATION_REGIMENINDICATION'

RADIATIONS_RADIATION_REGIMENINDICATION Labels N
  NO 177
  YES 433
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATIONS_RADIATION_REGIMENINDICATION'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS_RADIATION_REGIMENINDICATION'

W(pos if higher in 'YES') wilcoxontestP Q AUC
EFCAB7__2 63725 9.786e-39 9.85e-35 0.8362
ITGB3BP__1 63725 9.786e-39 9.85e-35 0.8362
PPP1R8 63813 4.242e-38 2.85e-34 0.8326
CEP120 59517 1.249e-36 6.28e-33 0.8302
SCYL2__1 63147 3.19e-36 1.28e-32 0.8239
PIGF__1 59882 2.075e-35 6.96e-32 0.8241
PKN2 62303 9.685e-35 2.78e-31 0.8175
BIRC6 62521 1.669e-34 4.2e-31 0.8158
UBR5 61094 3.342e-34 7.47e-31 0.8165
TFEC 61530 2.221e-33 4.47e-30 0.8111
Clinical variable #8: 'RACE'

30 genes related to 'RACE'.

Table S14.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 38
  WHITE 545
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
C6ORF52__1 6.181e-15 6.22e-11
PAK1IP1__1 6.181e-15 6.22e-11
RNF135 1.816e-13 1.22e-09
LOC253039 1.056e-12 4.25e-09
PSMD5 1.056e-12 4.25e-09
EIF3D 1.007e-10 3.38e-07
ISCA1 1.575e-09 4.53e-06
LOC349114 2.1e-08 5.28e-05
CCDC117 5.386e-08 0.00012
PGM2 8.427e-08 0.00017
Clinical variable #9: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S16.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 19
  NOT HISPANIC OR LATINO 514
     
  Significant markers N = 0
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 = 610

  • Number of genes = 20122

  • Number of clinical features = 9

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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, 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

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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