Correlation between gene methylation status and clinical features
Stomach and Esophageal carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C10V8C8V
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/C10V8C8V
Overview
Introduction

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

Summary

Testing the association between 17398 genes and 12 clinical features across 580 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 12 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • INMT ,  CDH11 ,  GHRL ,  FUT10 ,  LCP1 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ARHGEF16 ,  CDK2AP2 ,  ARHGAP27 ,  MRPL43 ,  CLDN7 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • TOM1L2 ,  RGS12 ,  STX12 ,  C9ORF70 ,  LSM14B ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • RAB38 ,  PPM1J ,  SERTAD2 ,  TGFA ,  SRCRB4D ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • TRIM35 ,  ADAM15 ,  KRT14 ,  ADA ,  SC5DL ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • SNRNP27 ,  RYK ,  CCDC90B ,  ZC3H11A ,  CHURC1 ,  ...

  • 30 genes correlated to 'GENDER'.

    • KIF4B ,  FRG1B ,  CHTF8 ,  GPN1 ,  RIMBP3 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • TIFA ,  VANGL1 ,  TMEM159 ,  OLFML2B ,  GTF2IP1 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • DYNC2LI1 ,  PSMG1 ,  LOC148189 ,  KNTC1 ,  C1D ,  ...

  • 30 genes correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • TENC1 ,  NPTN ,  C15ORF57 ,  CTDSP1 ,  MAPK3 ,  ...

  • 30 genes correlated to 'RACE'.

    • ZYG11B ,  DCUN1D1 ,  AHRR ,  WDR6 ,  NICN1 ,  ...

  • 30 genes correlated to 'ETHNICITY'.

    • KIAA1731 ,  MRPL44 ,  BUD13 ,  CSNK1G3 ,  MMP3 ,  ...

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=0 younger N=30
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=30 lower stage N=0
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=29 lower stage N=1
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 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
NUMBER_PACK_YEARS_SMOKED Spearman correlation test N=30 higher number_pack_years_smoked N=1 lower number_pack_years_smoked N=29
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) 0.1-122.3 (median=14)
  censored N = 347
  death N = 232
     
  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
INMT 1.81e-05 0.2 0.444
CDH11 3.48e-05 0.2 0.412
GHRL 4.22e-05 0.2 0.445
FUT10 4.77e-05 0.2 0.563
LCP1 5.84e-05 0.2 0.486
CEACAM7 8.59e-05 0.22 0.424
ADCK5 8.75e-05 0.22 0.476
GUK1 0.000108 0.23 0.553
MRPL54 0.000152 0.23 0.554
PEX7 0.000165 0.23 0.48
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) 64.32 (11)
  Significant markers N = 30
  pos. correlated 0
  neg. correlated 30
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
ARHGEF16 -0.2927 9.575e-13 1.4e-08
CDK2AP2 -0.2899 1.608e-12 1.4e-08
ARHGAP27 -0.2836 5.013e-12 2.36e-08
MRPL43 -0.282 6.769e-12 2.36e-08
CLDN7 -0.2817 7.025e-12 2.36e-08
SLC12A7 -0.2809 8.124e-12 2.36e-08
CLIC1 -0.2791 1.115e-11 2.77e-08
SLC25A20 -0.2746 2.472e-11 5.38e-08
EPB42 -0.2735 2.976e-11 5.38e-08
CTBP1 -0.2727 3.512e-11 5.38e-08
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 10
  STAGE IA 20
  STAGE IB 41
  STAGE II 30
  STAGE IIA 88
  STAGE IIB 86
  STAGE III 28
  STAGE IIIA 89
  STAGE IIIB 71
  STAGE IIIC 45
  STAGE IV 38
  STAGE IVA 4
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

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

kruskal_wallis_P Q
TOM1L2 2.988e-11 3.73e-07
RGS12 4.284e-11 3.73e-07
STX12 1.402e-09 8.13e-06
C9ORF70 3.907e-09 1.28e-05
LSM14B 4.339e-09 1.28e-05
LSG1 4.826e-09 1.28e-05
TDH 5.132e-09 1.28e-05
MUC17 5.987e-09 1.3e-05
CTNNBIP1 7.624e-09 1.42e-05
CERK 8.184e-09 1.42e-05
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.8 (0.88)
  N
  T0 1
  T1 52
  T2 121
  T3 274
  T4 115
     
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
RAB38 0.3242 3.009e-15 5.23e-11
PPM1J 0.2929 1.387e-12 1.21e-08
SERTAD2 0.2812 1.077e-11 4.87e-08
TGFA 0.281 1.119e-11 4.87e-08
SRCRB4D 0.278 1.88e-11 5.71e-08
PVRL4 0.2778 1.969e-11 5.71e-08
LOC284837 0.276 2.641e-11 6.32e-08
PRR7 0.2749 3.217e-11 6.32e-08
C15ORF62 0.2743 3.575e-11 6.32e-08
LPAR2 0.2742 3.631e-11 6.32e-08
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 1.13 (1.1)
  N
  N0 201
  N1 170
  N2 92
  N3 91
     
  Significant markers N = 30
  pos. correlated 29
  neg. correlated 1
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

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

SpearmanCorr corrP Q
TRIM35 0.2226 1.199e-07 0.00112
ADAM15 0.2178 2.499e-07 0.00112
KRT14 0.2169 2.53e-07 0.00112
ADA 0.2148 3.314e-07 0.00112
SC5DL 0.2145 3.445e-07 0.00112
PODNL1 0.2131 4.107e-07 0.00112
MICAL3 0.2121 4.7e-07 0.00112
LOC342346 0.2114 5.149e-07 0.00112
SECTM1 0.2082 7.669e-07 0.00144
C22ORF9 0.2076 8.291e-07 0.00144
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 489
  class1 32
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

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

W(pos if higher in 'class1') wilcoxontestP Q AUC
SNRNP27 11393 1.524e-05 0.235 0.7281
RYK 11231 3.645e-05 0.235 0.7177
CCDC90B 11176 4.86e-05 0.235 0.7142
ZC3H11A 11150 5.561e-05 0.235 0.7126
CHURC1 11063 8.663e-05 0.235 0.707
METTL2B 11004 0.0001163 0.235 0.7032
HIST1H2AH 10956 0.0001321 0.235 0.7016
PUS10 10976 0.0001335 0.235 0.7014
DDX47 10936 0.0001624 0.235 0.6989
LUZP6 10551 0.0001642 0.235 0.7018
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 163
  MALE 417
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S14.  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
KIF4B 12816 1.831e-31 3.19e-27 0.8114
FRG1B 17383 5.605e-20 4.88e-16 0.7443
CHTF8 49594 7.715e-18 4.47e-14 0.7296
GPN1 18910 9.575e-17 4.16e-13 0.7218
RIMBP3 45145 7.686e-10 2.67e-06 0.6642
NCRNA00116 23090 1.904e-09 5.52e-06 0.6603
ISOC2 44197 1.816e-08 4.51e-05 0.6502
MSL3L2 23890 2.625e-08 5.71e-05 0.6485
PSMD5 24009 3.816e-08 7.38e-05 0.6468
DDX43 24074 4.673e-08 8.13e-05 0.6458
Clinical variable #8: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 426
  YES 118
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

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

W(pos if higher in 'YES') wilcoxontestP Q AUC
TIFA 17667 7.75e-07 0.00697 0.6485
VANGL1 17677 8.018e-07 0.00697 0.6483
TMEM159 17937 1.909e-06 0.00838 0.6432
OLFML2B 32328 1.928e-06 0.00838 0.6431
GTF2IP1 18112 3.367e-06 0.0117 0.6397
FAM40A 18345 7.028e-06 0.0204 0.6351
PPARD 18571 1.404e-05 0.0308 0.6306
ADAM15 18253 1.588e-05 0.0308 0.6303
ARMC5 18690 2.003e-05 0.0308 0.6282
POLR2H 18690 2.003e-05 0.0308 0.6282
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 73.82 (16)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S18.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
DYNC2LI1 0.6992 3.316e-11 5.77e-07
PSMG1 0.6791 1.942e-10 1.69e-06
LOC148189 0.671 3.81e-10 2.21e-06
KNTC1 0.6597 9.443e-10 3.78e-06
C1D 0.6579 1.085e-09 3.78e-06
NR2C1 0.6438 3.167e-09 9.18e-06
PPFIA2 0.6391 4.484e-09 1.11e-05
ABCD2 0.6356 5.774e-09 1.12e-05
KIAA0562 0.6355 5.84e-09 1.12e-05
C2ORF73 0.6341 6.429e-09 1.12e-05
Clinical variable #10: 'NUMBER_PACK_YEARS_SMOKED'

30 genes related to 'NUMBER_PACK_YEARS_SMOKED'.

Table S19.  Basic characteristics of clinical feature: 'NUMBER_PACK_YEARS_SMOKED'

NUMBER_PACK_YEARS_SMOKED Mean (SD) 34.48 (22)
  Significant markers N = 30
  pos. correlated 1
  neg. correlated 29
List of top 10 genes differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

Table S20.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_PACK_YEARS_SMOKED' by Spearman correlation test

SpearmanCorr corrP Q
TENC1 -0.4841 4.416e-07 0.00768
NPTN -0.4545 2.595e-06 0.0226
C15ORF57 -0.4432 5.464e-06 0.027
CTDSP1 -0.4381 6.423e-06 0.027
MAPK3 -0.4346 7.757e-06 0.027
CIRBP -0.4252 1.274e-05 0.0369
BSG -0.4208 1.601e-05 0.0398
MAP2K3 -0.4143 2.229e-05 0.0485
ILK -0.4079 3.052e-05 0.0541
GPRC5A -0.4029 3.908e-05 0.0541
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

Table S21.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 135
  BLACK OR AFRICAN AMERICAN 18
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 1
  WHITE 367
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S22.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
ZYG11B 1.892e-24 3.29e-20
DCUN1D1 4.525e-17 3.94e-13
AHRR 1.094e-16 5.04e-13
WDR6 1.353e-16 5.04e-13
NICN1 1.448e-16 5.04e-13
MAPKAPK5 4.644e-15 1.35e-11
TYW3 1.583e-14 3.93e-11
PM20D1 2.082e-14 4.53e-11
GATM 2.398e-13 4.64e-10
PLAA 2.706e-13 4.71e-10
Clinical variable #12: 'ETHNICITY'

30 genes related to 'ETHNICITY'.

Table S23.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 11
  NOT HISPANIC OR LATINO 381
     
  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 = STES-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 580

  • Number of genes = 17398

  • Number of clinical features = 12

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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