Correlation between gene methylation status and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C13T9GMJ
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/C13T9GMJ
Overview
Introduction

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

Summary

Testing the association between 16846 genes and 12 clinical features across 275 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'.

    • NKAIN3 ,  UTF1 ,  MFSD6L ,  PAX5 ,  GPR25 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ELOVL2 ,  ARHGAP1 ,  AAK1 ,  MRPL44 ,  SARS ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • DLX6 ,  DLEU2 ,  DSCR6 ,  FOXA2 ,  SUV420H1 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • DLX6 ,  FOXA2 ,  LBXCOR1 ,  DSCR6 ,  SUV420H1 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • LBXCOR1 ,  KLK7 ,  COL9A2 ,  NAGS ,  GATA4 ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  PRKRIR ,  MYST2 ,  C5ORF27 ,  NARFL ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • HCG4P6 ,  KANK4 ,  EPC2 ,  VSNL1 ,  TXNIP ,  ...

  • 30 genes correlated to 'RACE'.

    • DHRS7 ,  RCBTB2 ,  DARC ,  C14ORF167 ,  CS ,  ...

  • No genes correlated to 'PATHOLOGY_M_STAGE', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 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=9 younger N=21
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=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=12 lower score N=18
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
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=24.9)
  censored N = 234
  death N = 40
     
  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
NKAIN3 4.2e-12 7.1e-08 0.171
UTF1 3.07e-11 2.6e-07 0.806
MFSD6L 1.23e-10 6.9e-07 0.751
PAX5 2.37e-10 1e-06 0.789
GPR25 3.15e-10 1.1e-06 0.767
CLEC4G 4.99e-10 1.4e-06 0.731
GRM6 6.62e-10 1.5e-06 0.777
TTC22 7.35e-10 1.5e-06 0.705
PDHB 9.48e-10 1.8e-06 0.723
KANK4 1.08e-09 1.8e-06 0.768
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) 61.73 (12)
  Significant markers N = 30
  pos. correlated 9
  neg. correlated 21
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
ELOVL2 0.3942 1.792e-11 3.02e-07
ARHGAP1 -0.2962 7.166e-07 0.00438
AAK1 -0.2953 7.801e-07 0.00438
MRPL44 -0.2843 2.056e-06 0.00712
SARS -0.284 2.112e-06 0.00712
ARGFXP2 -0.2757 4.423e-06 0.0124
CSDAP1 0.2693 7.186e-06 0.0155
NTM 0.2691 7.346e-06 0.0155
FAM173B -0.2625 1.242e-05 0.0233
KLF9 -0.2572 1.878e-05 0.029
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 168
  STAGE II 18
  STAGE III 51
  STAGE IV 14
     
  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
DLX6 1.239e-13 2.09e-09
DLEU2 3.709e-12 3.12e-08
DSCR6 7.328e-12 3.9e-08
FOXA2 9.264e-12 3.9e-08
SUV420H1 3.24e-11 1.09e-07
NR2E1 4.388e-11 1.16e-07
LBXCOR1 4.823e-11 1.16e-07
TRIL 1.14e-10 2.4e-07
SLC18A3 1.783e-10 2.97e-07
DIDO1 1.987e-10 2.97e-07
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) 1.54 (0.85)
  N
  T1 187
  T2 26
  T3 58
  T4 2
     
  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
DLX6 0.4552 2.269e-15 3.82e-11
FOXA2 0.4367 3.88e-14 2.48e-10
LBXCOR1 0.4358 4.414e-14 2.48e-10
DSCR6 0.4211 3.666e-13 1.38e-09
SUV420H1 0.4204 4.09e-13 1.38e-09
ANAPC13 0.4136 1.054e-12 2.96e-09
SLC18A3 0.41 1.716e-12 4.13e-09
MFSD6L 0.4089 1.982e-12 4.17e-09
DIDO1 0.4073 2.484e-12 4.65e-09
ERN2 0.4045 3.595e-12 5.85e-09
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) 0.41 (0.59)
  N
  N0 48
  N1 23
  N2 4
     
  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
LBXCOR1 0.6379 7.521e-10 9.86e-06
KLK7 0.6323 1.171e-09 9.86e-06
COL9A2 0.6096 6.457e-09 3.63e-05
NAGS 0.5906 2.446e-08 0.000103
GATA4 0.5761 6.361e-08 0.000156
ZSCAN10 0.5759 6.438e-08 0.000156
HOXA13 0.5758 6.48e-08 0.000156
CLDN6 0.5674 1.105e-07 0.000209
KLK4 0.5673 1.117e-07 0.000209
RIOK3 0.5642 1.351e-07 0.000213
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 85
  class1 8
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 73
  MALE 202
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S13.  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 13389 5.194e-25 8.75e-21 0.908
PRKRIR 13238 7.497e-24 6.31e-20 0.8977
MYST2 12584 3.652e-19 2.05e-15 0.8534
C5ORF27 3078 1.654e-13 6.76e-10 0.7913
NARFL 3093 2.006e-13 6.76e-10 0.7902
PEMT 3228 1.106e-12 3.11e-09 0.7811
AOX1 3359 5.515e-12 1.33e-08 0.7722
PSMB7 3439 1.436e-11 3.02e-08 0.7668
SCGB1D2 3536 4.467e-11 8.36e-08 0.7602
ADARB2 3593 8.593e-11 1.35e-07 0.7563
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 88.82 (20)
  Significant markers N = 30
  pos. correlated 12
  neg. correlated 18
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S15.  Get Full Table List of top 10 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HCG4P6 -0.5083 2.771e-06 0.0131
KANK4 -0.5024 3.759e-06 0.0131
EPC2 0.5019 3.861e-06 0.0131
VSNL1 -0.5014 3.965e-06 0.0131
TXNIP -0.4983 4.641e-06 0.0131
RAB40C -0.497 4.943e-06 0.0131
PSMD14 0.4951 5.46e-06 0.0131
PSAP 0.4797 1.165e-05 0.0193
ZNF133 -0.477 1.322e-05 0.0193
MORN1 -0.472 1.674e-05 0.0193
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 32.86 (28)
  Significant markers N = 0
Clinical variable #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1971.85 (16)
  Significant markers N = 0
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 2
  ASIAN 6
  BLACK OR AFRICAN AMERICAN 54
  WHITE 198
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

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

kruskal_wallis_P Q
DHRS7 1.868e-18 3.15e-14
RCBTB2 2.554e-12 2.15e-08
DARC 1.901e-10 1.07e-06
C14ORF167 1.975e-09 8.32e-06
CS 3.38e-09 1.14e-05
SETD1A 1.064e-08 2.64e-05
NUDT13 1.111e-08 2.64e-05
RPS28 1.252e-08 2.64e-05
GRAPL 2.207e-08 4.13e-05
DENND4C 6.107e-08 0.000103
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 227
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 275

  • Number of genes = 16846

  • 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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