Correlation between gene methylation status and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1QJ7GH8
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/C1QJ7GH8
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 19808 genes and 12 clinical features across 274 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'.

    • ELOVL2 ,  ARHGAP1 ,  AAK1 ,  MRPL44 ,  NTM ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • DLX6 ,  DLX6AS ,  DLEU2 ,  DSCR6 ,  FOXA2 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • DLX6 ,  DLX6AS ,  FOXA2 ,  LBXCOR1 ,  DSCR6 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • LBXCOR1 ,  COL9A2 ,  KLK7 ,  HOXA13 ,  NAGS ,  ...

  • 30 genes correlated to 'GENDER'.

    • ALG11__2 ,  UTP14C ,  PRKRIR ,  MYST2 ,  C5ORF27 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

  • 30 genes correlated to 'RACE'.

    • DHRS7 ,  RCBTB2 ,  DARC ,  C14ORF167 ,  DHRS4 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', '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=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=11 younger N=19
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=11 lower score N=19
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'

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) 0.1-194.8 (median=24.1)
  censored N = 233
  death N = 40
     
  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) 61.68 (12)
  Significant markers N = 30
  pos. correlated 11
  neg. correlated 19
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
ELOVL2 0.3921 2.545e-11 5.04e-07
ARHGAP1 -0.3004 5.133e-07 0.00508
AAK1 -0.2926 1.04e-06 0.00553
MRPL44 -0.2918 1.116e-06 0.00553
NTM 0.2794 3.244e-06 0.0109
SARS -0.2793 3.294e-06 0.0109
ARGFXP2 -0.2731 5.716e-06 0.0142
RHOT1 -0.2731 5.716e-06 0.0142
FAM173B -0.2697 7.237e-06 0.0159
KLF9 -0.2673 8.77e-06 0.0174
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 168
  STAGE II 19
  STAGE III 51
  STAGE IV 14
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

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

kruskal_wallis_P Q
DLX6 8.121e-14 8.04e-10
DLX6AS 8.121e-14 8.04e-10
DLEU2 1.662e-12 1.1e-08
DSCR6 3.311e-12 1.64e-08
FOXA2 1.849e-11 7.33e-08
LBXCOR1 2.305e-11 7.61e-08
CHAT 4.115e-11 8.39e-08
SLC18A3 4.115e-11 8.39e-08
POU6F2 4.429e-11 8.39e-08
SUV420H1 4.563e-11 8.39e-08
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.54 (0.85)
  N
  T1 186
  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 S7.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
DLX6 0.4586 1.49e-15 1.48e-11
DLX6AS 0.4586 1.49e-15 1.48e-11
FOXA2 0.4361 4.691e-14 2.91e-10
LBXCOR1 0.4346 5.87e-14 2.91e-10
DSCR6 0.4244 2.561e-13 1.01e-09
SUV420H1 0.4196 5.025e-13 1.66e-09
ANAPC13 0.4157 8.682e-13 2.15e-09
CEP63 0.4157 8.682e-13 2.15e-09
CHAT 0.4087 2.25e-12 4.09e-09
SLC18A3 0.4087 2.25e-12 4.09e-09
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.44 (0.62)
  N
  N0 47
  N1 23
  N2 5
     
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

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

SpearmanCorr corrP Q
LBXCOR1 0.6184 3.37e-09 3.09e-05
COL9A2 0.6147 4.448e-09 3.09e-05
KLK7 0.614 4.684e-09 3.09e-05
HOXA13 0.5827 4.154e-08 0.000135
NAGS 0.581 4.627e-08 0.000135
PYY 0.581 4.627e-08 0.000135
ZSCAN10 0.5773 5.906e-08 0.000135
CLDN6 0.5768 6.088e-08 0.000135
KLK4 0.5742 7.185e-08 0.000135
GATA4 0.5738 7.404e-08 0.000135
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 84
  class1 8
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 73
  MALE 201
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S12.  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__2 13350 3.415e-25 3.38e-21 0.9098
UTP14C 13350 3.415e-25 3.38e-21 0.9098
PRKRIR 13224 3.241e-24 2.14e-20 0.9012
MYST2 12535 3.132e-19 1.55e-15 0.8543
C5ORF27 3076 2.036e-13 6.81e-10 0.7904
NARFL 3077 2.063e-13 6.81e-10 0.7903
PEMT 3226 1.363e-12 3.86e-09 0.7801
AOX1 3333 5.084e-12 1.26e-08 0.7728
PSMB7 3393 1.048e-11 2.31e-08 0.7688
SCGB1D2 3490 3.302e-11 6.54e-08 0.7621
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 88.82 (20)
  Significant markers N = 30
  pos. correlated 11
  neg. correlated 19
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S14.  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 0.0155
KANK4 -0.5024 3.759e-06 0.0155
EPC2 0.5019 3.861e-06 0.0155
VSNL1 -0.5014 3.965e-06 0.0155
TXNIP -0.4983 4.641e-06 0.0155
RAB40C -0.497 4.943e-06 0.0155
PSMD14 0.4951 5.46e-06 0.0155
PSAP 0.4797 1.165e-05 0.0216
ZNF133 -0.477 1.322e-05 0.0216
MORN1__1 -0.472 1.674e-05 0.0216
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

Table S15.  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 S16.  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 S17.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 2
  ASIAN 5
  BLACK OR AFRICAN AMERICAN 54
  WHITE 198
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S18.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
DHRS7 2.732e-18 5.41e-14
RCBTB2 2.886e-12 2.86e-08
DARC 3.843e-10 2.54e-06
C14ORF167 9.82e-10 3.89e-06
DHRS4 9.82e-10 3.89e-06
CS 3.583e-09 1.18e-05
NUDT13 7e-09 1.98e-05
NDUFA7__1 1.241e-08 2.73e-05
RPS28__1 1.241e-08 2.73e-05
SETD1A 1.973e-08 3.91e-05
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 226
     
  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 = 274

  • Number of genes = 19808

  • 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, 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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