Correlation between gene methylation status and clinical features
Kidney Renal Clear Cell Carcinoma (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/C1251HM2
Overview
Introduction

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

Summary

Testing the association between 17087 genes and 12 clinical features across 319 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • TLL2 ,  DUSP22 ,  MYO10 ,  PUM1 ,  SOX8 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • MRPS33 ,  SLC10A4 ,  RANBP17 ,  DOK6 ,  ZYG11A ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • DPP6 ,  CLEC2L ,  SOX8 ,  OPRK1 ,  ACTA1 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • DPP6 ,  CLEC2L ,  ACTA1 ,  OPRK1 ,  RRM2 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • KDELR3 ,  MYB ,  MYLK2 ,  TACC3 ,  ARHGAP9 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • C20ORF112 ,  HTR6 ,  MYO10 ,  CSDC2 ,  STK24 ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  KIF4B ,  TLE1 ,  COX7C ,  C5ORF27 ,  ...

  • 2 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • MCTP1 ,  ADAMTSL2

  • 1 gene correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • LOC100130557

  • 30 genes correlated to 'RACE'.

    • CAPRIN1 ,  NEDD1 ,  POFUT1 ,  C5ORF28 ,  INTS12 ,  ...

  • No genes correlated to '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=25 younger N=5
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 Wilcoxon test N=30 n1 N=30 n0 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
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=2 higher score N=0 lower score N=2
NUMBER_PACK_YEARS_SMOKED Spearman correlation test N=1 higher number_pack_years_smoked N=0 lower number_pack_years_smoked N=1
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) 0.1-149.2 (median=35.6)
  censored N = 213
  death N = 105
     
  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
TLL2 1.52e-10 2.6e-06 0.316
DUSP22 4.32e-10 3.7e-06 0.359
MYO10 9.42e-10 5.4e-06 0.67
PUM1 1.79e-09 6.2e-06 0.672
SOX8 1.81e-09 6.2e-06 0.676
C12ORF49 2.44e-09 6.9e-06 0.624
TMCC3 6.13e-09 1.5e-05 0.656
DAK 7.61e-09 1.6e-05 0.667
SUSD5 1.07e-08 2e-05 0.589
CLEC2L 1.51e-08 2.6e-05 0.674
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) 61.37 (12)
  Significant markers N = 30
  pos. correlated 25
  neg. correlated 5
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
MRPS33 0.3416 3.677e-10 6.28e-06
SLC10A4 0.3025 3.575e-08 0.000305
RANBP17 0.2935 9.352e-08 0.000533
DOK6 0.2847 2.307e-07 0.000708
ZYG11A 0.2846 2.327e-07 0.000708
PVT1 -0.284 2.486e-07 0.000708
PCOLCE2 -0.2789 4.154e-07 0.000819
FOXD4L6 0.2776 4.724e-07 0.000819
HIST1H3B 0.2771 4.939e-07 0.000819
LYSMD2 -0.2764 5.308e-07 0.000819
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 155
  STAGE II 31
  STAGE III 73
  STAGE IV 59
     
  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
DPP6 1.11e-18 1.9e-14
CLEC2L 2.342e-17 2e-13
SOX8 2.027e-15 9.03e-12
OPRK1 2.114e-15 9.03e-12
ACTA1 5.972e-15 2.04e-11
NEUROD2 8.311e-15 2.37e-11
INSM2 1.164e-14 2.84e-11
RRM2 4.483e-14 9.58e-11
CRHBP 1.182e-13 2.17e-10
SOX17 1.269e-13 2.17e-10
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) 1.9 (0.97)
  N
  T1 159
  T2 41
  T3 111
  T4 8
     
  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
DPP6 0.4892 1.345e-20 2.3e-16
CLEC2L 0.4843 3.658e-20 3.13e-16
ACTA1 0.4588 5.232e-18 2.98e-14
OPRK1 0.449 3.11e-17 1.33e-13
RRM2 -0.4441 7.534e-17 2.57e-13
INSM2 0.4428 9.48e-17 2.7e-13
SLC35F1 0.4322 5.996e-16 1.46e-12
SOX8 0.4302 8.342e-16 1.78e-12
NEUROD2 0.4257 1.779e-15 3.38e-12
ALDH1A2 0.4187 5.722e-15 9.78e-12
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 Labels N
  N0 133
  N1 8
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
KDELR3 113 0.0001917 0.257 0.8938
MYB 130 0.000346 0.257 0.8778
MYLK2 134 0.0003963 0.257 0.8741
TACC3 137 0.0004385 0.257 0.8712
ARHGAP9 141 0.0005012 0.257 0.8675
SNORA8 142 0.0005181 0.257 0.8665
RIT1 920 0.0005536 0.257 0.8647
ATP8B4 146 0.0005913 0.257 0.8628
CAPZA1 148 0.0006314 0.257 0.8609
ARPC2 150 0.000674 0.257 0.859
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 234
  class1 53
     
  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
C20ORF112 9604 4.472e-10 7.64e-06 0.7744
HTR6 9412 3.989e-09 2.32e-05 0.7589
MYO10 9410 4.078e-09 2.32e-05 0.7587
CSDC2 9340 8.784e-09 3.75e-05 0.7531
STK24 9274 1.784e-08 6.1e-05 0.7478
AJAP1 9164 5.633e-08 0.00014 0.7389
ASB4 9162 5.749e-08 0.00014 0.7388
SPRY1 9135 7.577e-08 0.00015 0.7366
CALN1 9131 7.891e-08 0.00015 0.7363
SOX8 9073 1.415e-07 0.000236 0.7316
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 114
  MALE 205
     
  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 22915 6.427e-46 1.1e-41 0.9805
KIF4B 2946 1.766e-28 1.51e-24 0.8739
TLE1 3815 2.092e-23 1.19e-19 0.8368
COX7C 4560 1.799e-19 5.82e-16 0.8049
C5ORF27 4565 1.906e-19 5.82e-16 0.8047
FRG1B 4571 2.043e-19 5.82e-16 0.8044
DNAJB13 4648 4.949e-19 1.21e-15 0.8011
CAV2 4720 1.122e-18 2.4e-15 0.798
CCDC146 4763 1.823e-18 3.46e-15 0.7962
CHTF8 18428 1.331e-17 2.27e-14 0.7885
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

2 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 89.56 (16)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S16.  Get Full Table List of 2 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
MCTP1 -0.7633 1.079e-09 1.84e-05
ADAMTSL2 -0.6008 1.281e-05 0.109
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

One gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 29 (16)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

Table S18.  Get Full Table List of one gene significantly correlated to 'NUMBER_PACK_YEARS_SMOKED' by Spearman correlation test

SpearmanCorr corrP Q
LOC100130557 -0.8178 1.062e-05 0.181
Clinical variable #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1978.18 (18)
  Significant markers N = 0
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 1
  BLACK OR AFRICAN AMERICAN 49
  WHITE 266
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
CAPRIN1 3.415e-15 5.84e-11
NEDD1 1.228e-14 7.31e-11
POFUT1 1.477e-14 7.31e-11
C5ORF28 2.064e-14 7.31e-11
INTS12 2.631e-14 7.31e-11
CDC20 2.68e-14 7.31e-11
MKRN1 2.993e-14 7.31e-11
HELZ 4.614e-14 9.15e-11
CEP192 4.818e-14 9.15e-11
UEVLD 1.141e-13 1.94e-10
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S22.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 10
  NOT HISPANIC OR LATINO 260
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = KIRC-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 319

  • Number of genes = 17087

  • 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)