Correlation between gene methylation status and clinical features
Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C10K2800
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/C10K2800
Overview
Introduction

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

Summary

Testing the association between 16988 genes and 14 clinical features across 660 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 11 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • C7ORF41 ,  GPR25 ,  LBXCOR1 ,  LOC100192379 ,  MSI2 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ZNF233 ,  DGKE ,  TRPM4 ,  REC8 ,  ARHGAP1 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • IRX1 ,  CDO1 ,  SOX30 ,  GPR25 ,  MAFA ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • CDO1 ,  IRX1 ,  SOX30 ,  GPR25 ,  ZNF132 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • FOXC2 ,  SLC6A3 ,  TLX1 ,  ARL10 ,  DENND1C ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • HS3ST2 ,  ADCYAP1 ,  SDR16C5 ,  DRD1 ,  SOX8 ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  TLE1 ,  KIF4B ,  WBP11P1 ,  FRG1B ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • EPS15L1 ,  PLLP ,  KIAA1984 ,  SIGIRR ,  CAMK2B ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • CCND1 ,  NOL3 ,  TMCC1 ,  VIM ,  APEH ,  ...

  • 1 gene correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

    • LOC349196

  • 30 genes correlated to 'RACE'.

    • DHRS7 ,  INTS12 ,  RPL23AP7 ,  C18ORF54 ,  DARC ,  ...

  • No genes correlated to 'RADIATION_THERAPY', 'NUMBER_PACK_YEARS_SMOKED', 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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=14 younger N=16
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=29 lower stage N=1
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=30 lower stage N=0
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=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=2 lower score N=28
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test N=1 higher year_of_tobacco_smoking_onset N=0 lower year_of_tobacco_smoking_onset N=1
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   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) 0.1-194.8 (median=30.5)
  censored N = 504
  death N = 155
     
  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
C7ORF41 0 0 0.714
GPR25 0 0 0.706
LBXCOR1 0 0 0.712
LOC100192379 0 0 0.69
MSI2 0 0 0.705
SLC6A7 0 0 0.711
SOX8 0 0 0.711
TMEM25 0 0 0.719
ZIC4 0 0 0.71
BCR 1.11e-16 1.9e-13 0.7
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) 60.53 (13)
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
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
ZNF233 0.265 5.457e-12 9.27e-08
DGKE 0.2527 5.358e-11 4.55e-07
TRPM4 0.2452 2.024e-10 1.07e-06
REC8 0.2439 2.525e-10 1.07e-06
ARHGAP1 -0.2404 4.577e-10 1.55e-06
SLC25A22 -0.2382 6.701e-10 1.55e-06
KLF9 -0.2376 7.361e-10 1.55e-06
RALGAPA2 0.2374 7.667e-10 1.55e-06
DDO -0.2364 9.077e-10 1.55e-06
ITGAE -0.2363 9.123e-10 1.55e-06
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 344
  STAGE II 74
  STAGE III 138
  STAGE IV 79
     
  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
IRX1 2.143e-31 1.95e-27
CDO1 2.295e-31 1.95e-27
SOX30 4.552e-28 2.58e-24
GPR25 3.649e-27 1.55e-23
MAFA 1.495e-26 5.08e-23
SPAG6 2.658e-26 7.52e-23
NEIL1 8.806e-26 2.14e-22
USP44 6.448e-25 1.37e-21
ADCYAP1 7.83e-25 1.48e-21
SOX8 8.962e-25 1.52e-21
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) 1.76 (0.93)
  N
  T1 367
  T2 92
  T3 187
  T4 12
     
  Significant markers N = 30
  pos. correlated 29
  neg. correlated 1
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
CDO1 0.4595 1.088e-35 1.85e-31
IRX1 0.4482 7.728e-34 6.56e-30
SOX30 0.4274 1.356e-30 7.68e-27
GPR25 0.4202 1.542e-29 6.55e-26
ZNF132 0.4162 5.973e-29 2.03e-25
SPAG6 0.4096 5.209e-28 1.47e-24
USP44 0.4072 1.142e-27 2.77e-24
MAFA 0.405 2.291e-27 4.86e-24
RRM2 -0.3972 2.709e-26 5.11e-23
ABCC8 0.3941 7.07e-26 1.2e-22
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) 0.18 (0.44)
  N
  N0 221
  N1 34
  N2 6
     
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
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
FOXC2 0.4357 1.625e-13 2.76e-09
SLC6A3 0.4289 4.22e-13 3.58e-09
TLX1 0.4242 8.026e-13 4.55e-09
ARL10 0.4129 3.61e-12 1.42e-08
DENND1C 0.4118 4.187e-12 1.42e-08
C2ORF55 0.4063 8.496e-12 2.35e-08
C4ORF38 0.4053 9.697e-12 2.35e-08
FOXE1 0.3949 3.606e-11 7.66e-08
FGF5 0.3852 1.173e-10 2.21e-07
MOGAT3 0.3834 1.454e-10 2.47e-07
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 353
  class1 63
     
  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
HS3ST2 16982 2.589e-11 2.25e-07 0.7636
ADCYAP1 16979 2.65e-11 2.25e-07 0.7635
SDR16C5 16847 7.285e-11 2.92e-07 0.7575
DRD1 16819 9.003e-11 2.92e-07 0.7563
SOX8 16807 9.855e-11 2.92e-07 0.7557
CHRNA3 16801 1.031e-10 2.92e-07 0.7555
ZC3HAV1L 5536 2.141e-10 5.01e-07 0.7511
CDO1 16679 2.556e-10 5.01e-07 0.75
ZNF229 16674 2.652e-10 5.01e-07 0.7498
LBXCOR1 16621 3.91e-10 6.26e-07 0.7474
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 214
  MALE 446
     
  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
UTP14C 90427 2.026e-77 3.44e-73 0.9474
TLE1 15733 3.097e-44 2.63e-40 0.8352
KIF4B 19762 3.347e-34 1.9e-30 0.7929
WBP11P1 73083 1.951e-28 8.29e-25 0.7657
FRG1B 22787 1.522e-27 4.65e-24 0.7613
C5ORF27 22803 1.643e-27 4.65e-24 0.7611
SCGB1D2 23187 1.015e-26 2.46e-23 0.7571
FKBP5 25432 2.451e-22 5.2e-19 0.7335
PEMT 26352 1.165e-20 2.2e-17 0.7239
GREB1 27906 5.518e-18 9.37e-15 0.7076
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 383
  YES 3
     
  Significant markers N = 0
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 89.93 (18)
  Significant markers N = 30
  pos. correlated 2
  neg. correlated 28
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S17.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
EPS15L1 -0.4279 2.505e-07 0.00219
PLLP -0.4275 2.582e-07 0.00219
KIAA1984 -0.411 8.117e-07 0.0046
SIGIRR -0.3998 1.707e-06 0.00725
CAMK2B -0.3946 2.389e-06 0.00729
C11ORF93 -0.3905 3.095e-06 0.00729
LHFPL2 0.3899 3.214e-06 0.00729
ZNF662 -0.3862 4.058e-06 0.00729
C4ORF36 -0.3856 4.204e-06 0.00729
C1ORF210 -0.3845 4.491e-06 0.00729
Clinical variable #10: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

Table S18.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  KIDNEY CHROMOPHOBE 66
  KIDNEY CLEAR CELL RENAL CARCINOMA 319
  KIDNEY PAPILLARY RENAL CELL CARCINOMA 275
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S19.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
CCND1 3.346e-96 5.68e-92
NOL3 5.847e-93 4.97e-89
TMCC1 3.082e-91 1.39e-87
VIM 3.493e-91 1.39e-87
APEH 4.088e-91 1.39e-87
ZNF395 4.091e-90 9.25e-87
FTO 4.113e-90 9.25e-87
KSR1 4.357e-90 9.25e-87
ZNF704 1.594e-89 3.01e-86
UBE2N 4.218e-89 7.17e-86
Clinical variable #11: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

Table S20.  Basic characteristics of clinical feature: 'NUMBER_PACK_YEARS_SMOKED'

NUMBER_PACK_YEARS_SMOKED Mean (SD) 31.27 (25)
  Significant markers N = 0
Clinical variable #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

One gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1973.03 (16)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene differentially expressed by 'YEAR_OF_TOBACCO_SMOKING_ONSET'

Table S22.  Get Full Table List of one gene significantly correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET' by Spearman correlation test

SpearmanCorr corrP Q
LOC349196 -0.5352 1.273e-06 0.0216
Clinical variable #13: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 2
  ASIAN 9
  BLACK OR AFRICAN AMERICAN 107
  WHITE 522
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S24.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
DHRS7 1.487e-29 2.53e-25
INTS12 7.741e-20 5.98e-16
RPL23AP7 1.056e-19 5.98e-16
C18ORF54 5.288e-16 2.23e-12
DARC 6.561e-16 2.23e-12
RPS28 9.749e-16 2.76e-12
XRCC6 4.521e-15 1.1e-11
CCDC117 7.519e-15 1.6e-11
PSMD5 9.403e-15 1.77e-11
LOC349114 3.558e-14 6.04e-11
Clinical variable #14: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S25.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 26
  NOT HISPANIC OR LATINO 519
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 660

  • Number of genes = 16988

  • Number of clinical features = 14

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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