Correlation between gene methylation status and clinical features
Brain Lower Grade Glioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C1PC31TC
Overview
Introduction

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

Summary

Testing the association between 17093 genes and 8 clinical features across 515 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • AAA1 ,  ABCC1 ,  ABCC3 ,  ABCC6P2 ,  ACACB ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • TCHH ,  TRIM58 ,  LOC150786 ,  SHISA2 ,  RELN ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  POLDIP3 ,  KIF4B ,  WBP11P1 ,  LOC389791 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • SIGLEC9 ,  CCL3 ,  MYCT1 ,  KCNK18 ,  TPRG1 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • PHTF1 ,  UBC ,  HS6ST1 ,  DEGS1 ,  PIK3AP1 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • REST ,  MAPKAP1 ,  C2ORF67 ,  GLIS3 ,  SLC2A4RG ,  ...

  • 8 genes correlated to 'RACE'.

    • C6ORF52 ,  RNF135 ,  LOC253039 ,  ENTPD6 ,  ISCA1 ,  ...

  • 5 genes correlated to 'ETHNICITY'.

    • THAP1 ,  ASNSD1 ,  SNRPB2 ,  MRPS22 ,  NUP133

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=26 younger N=4
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=8        
ETHNICITY Wilcoxon test N=5 not hispanic or latino N=5 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=22.3)
  censored N = 388
  death N = 126
     
  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
AAA1 0 0 0.256
ABCC1 0 0 0.228
ABCC3 0 0 0.205
ABCC6P2 0 0 0.281
ACACB 0 0 0.248
ACOX2 0 0 0.262
ACSBG1 0 0 0.213
ACSS3 0 0 0.301
ACTL6B 0 0 0.747
ACTR3 0 0 0.247
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) 42.93 (13)
  Significant markers N = 30
  pos. correlated 26
  neg. correlated 4
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
TCHH 0.5688 2.172e-45 3.71e-41
TRIM58 0.5602 8.304e-44 7.1e-40
LOC150786 0.5234 1.685e-37 9.6e-34
SHISA2 0.5191 8.264e-37 3.53e-33
RELN 0.5046 1.475e-34 5.04e-31
FOXE3 0.4993 1.215e-33 3.46e-30
ADAMTSL3 0.4917 1.193e-32 2.91e-29
SSTR4 0.4732 4.779e-30 1.02e-26
TFAP2B 0.4719 7.287e-30 1.38e-26
KLRG2 0.4715 8.359e-30 1.43e-26
Clinical variable #3: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 230
  MALE 285
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S6.  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 64314 9.943e-79 1.7e-74 0.9811
POLDIP3 5627 8.214e-59 7.02e-55 0.9142
KIF4B 16066 2.468e-23 1.41e-19 0.7549
WBP11P1 48367 1.59e-20 6.79e-17 0.7379
LOC389791 47946 1.624e-19 5.55e-16 0.7314
TLE1 17852 6.202e-19 1.77e-15 0.7277
ZC3H14 19608 4.42e-15 1.08e-11 0.7009
ZNF839 20425 1.898e-13 4.06e-10 0.6884
MRPL42 21211 8.045e-12 1.53e-08 0.6753
ZNF770 21527 2.093e-11 3.58e-08 0.6716
Clinical variable #4: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 186
  YES 296
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
SIGLEC9 16869 8.07e-13 6.96e-09 0.6936
CCL3 16871 8.149e-13 6.96e-09 0.6936
MYCT1 17149 3.128e-12 1.54e-08 0.6885
KCNK18 17179 3.609e-12 1.54e-08 0.688
TPRG1 17237 4.753e-12 1.62e-08 0.6869
FOXS1 17295 6.252e-12 1.67e-08 0.6859
SMCP 17328 7.302e-12 1.67e-08 0.6853
HSPG2 17342 7.797e-12 1.67e-08 0.685
CEACAM21 17451 1.297e-11 2.14e-08 0.683
LRRC15 17462 1.365e-11 2.14e-08 0.6828
Clinical variable #5: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 86.64 (13)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S10.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
PHTF1 0.3063 4.57e-08 0.000781
UBC 0.2913 2.027e-07 0.00115
HS6ST1 0.2886 2.67e-07 0.00115
DEGS1 0.2862 3.366e-07 0.00115
PIK3AP1 0.2851 3.754e-07 0.00115
PREP 0.2823 4.947e-07 0.00115
CCDC136 0.2812 5.502e-07 0.00115
RCAN1 0.2808 5.731e-07 0.00115
TWF2 0.2797 6.37e-07 0.00115
CDCP1 0.2791 6.722e-07 0.00115
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  ASTROCYTOMA 194
  OLIGOASTROCYTOMA 130
  OLIGODENDROGLIOMA 191
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
REST 5.16e-38 8.82e-34
MAPKAP1 2.178e-33 1.86e-29
C2ORF67 6.636e-32 3.78e-28
GLIS3 4.735e-31 2.02e-27
SLC2A4RG 8.814e-31 3.01e-27
BVES 4.255e-30 1.19e-26
ARID1A 4.892e-30 1.19e-26
TMC8 1.223e-29 2.61e-26
CBX2 2.709e-29 5.14e-26
EMP1 4.333e-29 7.41e-26
Clinical variable #7: 'RACE'

8 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 21
  WHITE 475
     
  Significant markers N = 8
List of 8 genes differentially expressed by 'RACE'

Table S14.  Get Full Table List of 8 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
C6ORF52 5.799e-09 9.91e-05
RNF135 3.362e-08 0.000287
LOC253039 2.949e-07 0.00168
ENTPD6 5.862e-06 0.025
ISCA1 5.645e-05 0.185
LOC645676 7.246e-05 0.185
DHRS7 8.268e-05 0.185
PGM2 8.639e-05 0.185
Clinical variable #8: 'ETHNICITY'

5 genes related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 32
  NOT HISPANIC OR LATINO 449
     
  Significant markers N = 5
  Higher in NOT HISPANIC OR LATINO 5
  Higher in HISPANIC OR LATINO 0
List of 5 genes differentially expressed by 'ETHNICITY'

Table S16.  Get Full Table List of 5 genes differentially expressed by 'ETHNICITY'

W(pos if higher in 'NOT HISPANIC OR LATINO') wilcoxontestP Q AUC
THAP1 c("3966", "2.282e-05") c("3966", "2.282e-05") 0.223 0.724
ASNSD1 c("3989", "2.61e-05") c("3989", "2.61e-05") 0.223 0.7224
SNRPB2 c("4139", "6.134e-05") c("4139", "6.134e-05") 0.285 0.7119
MRPS22 c("4154", "6.667e-05") c("4154", "6.667e-05") 0.285 0.7109
NUP133 c("4199", "8.543e-05") c("4199", "8.543e-05") 0.292 0.7078
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 = 515

  • Number of genes = 17093

  • Number of clinical features = 8

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)