Correlation between gene methylation status and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1CJ8CJR
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/C1CJ8CJR
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 20141 genes and 12 clinical features across 315 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'.

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

  • 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • DPP6 ,  CLEC2L ,  ACTA1 ,  NEUROD2 ,  SOX8 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

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

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

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

  • 30 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  KIF4B ,  TLE1 ,  COX7C ,  ...

  • 1 gene correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

    • HUNK

  • 30 genes correlated to 'RACE'.

    • PLAGL2__1 ,  POFUT1__1 ,  CAPRIN1 ,  NEDD1 ,  MKRN1 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_N_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', '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=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=23 younger N=7
NEOPLASM_DISEASESTAGE 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=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=0        
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=1 lower year_of_tobacco_smoking_onset 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) 0.1-120.6 (median=29.5)
  censored N = 214
  death N = 100
     
  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) 61.5 (12)
  Significant markers N = 30
  pos. correlated 23
  neg. correlated 7
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
MRPS33 0.3496 1.733e-10 3.49e-06
DOK6 0.2924 1.251e-07 0.000681
SLC10A4 0.2902 1.582e-07 0.000681
ZYG11A 0.2884 1.894e-07 0.000681
RANBP17 0.2876 2.055e-07 0.000681
PVT1 -0.287 2.194e-07 0.000681
ADAMTS17 0.286 2.426e-07 0.000681
LYSMD2 -0.2849 2.706e-07 0.000681
HIST1H3B 0.2762 6.393e-07 0.00138
TSPYL5 0.2745 7.47e-07 0.00138
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

30 genes related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 153
  STAGE II 31
  STAGE III 75
  STAGE IV 56
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'NEOPLASM_DISEASESTAGE'

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

kruskal_wallis_P Q
DPP6 2.54e-18 5.12e-14
CLEC2L 1.416e-16 1.43e-12
ACTA1 1.006e-15 6.75e-12
NEUROD2 4.208e-15 2.12e-11
SOX8 8.681e-15 3.35e-11
OPRK1 9.992e-15 3.35e-11
FAM38B 7.274e-14 2.09e-10
RRM2 2.261e-13 5.69e-10
INSM2 2.897e-13 6.48e-10
MYO10 3.325e-13 6.7e-10
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) 1.9 (0.97)
  N
  T1 156
  T2 41
  T3 110
  T4 8
     
  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
DPP6 0.4867 3.849e-20 7.75e-16
CLEC2L 0.4802 1.416e-19 1.43e-15
ACTA1 0.4779 2.209e-19 1.48e-15
OPRK1 0.4436 1.275e-16 6.42e-13
RRM2 -0.4381 3.313e-16 1.33e-12
NEUROD2 0.4349 5.77e-16 1.94e-12
INSM2 0.4324 8.848e-16 2.55e-12
SLC35F1 0.4313 1.06e-15 2.67e-12
SOX8 0.4247 3.162e-15 7.08e-12
ALDH1A2 0.4104 3.156e-14 6.19e-11
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 131
  N1 9
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

Table S9.  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 S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
C20ORF112 9608 4.268e-10 8.6e-06 0.7747
HTR6 9417 3.774e-09 2.77e-05 0.7593
MYO10 9409 4.124e-09 2.77e-05 0.7587
CSDC2 9333 9.476e-09 4.77e-05 0.7525
STK24 9274 1.784e-08 7.19e-05 0.7478
FAM38B 9255 2.182e-08 7.33e-05 0.7463
ASB4 9188 4.398e-08 0.000127 0.7408
AJAP1 9174 5.082e-08 0.000128 0.7397
SPRY1 9139 7.274e-08 0.000163 0.7369
CALN1 9125 8.387e-08 0.000169 0.7358
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 113
  MALE 202
     
  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__1 22396 1.497e-45 1.51e-41 0.9812
UTP14C 22396 1.497e-45 1.51e-41 0.9812
KIF4B 3061 4.667e-27 3.13e-23 0.8659
TLE1 3725 3.562e-23 1.79e-19 0.8368
COX7C 4335 6.928e-20 2.79e-16 0.8101
CAV2 4460 3.032e-19 9.09e-16 0.8046
EIF4A1__1 4481 3.876e-19 9.09e-16 0.8037
SNORA48 4481 3.876e-19 9.09e-16 0.8037
C5ORF27 4485 4.061e-19 9.09e-16 0.8035
FRG1B 4511 5.497e-19 1.11e-15 0.8024
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 91.39 (9.3)
  Score N
  60 1
  70 1
  80 4
  90 16
  100 14
     
  Significant markers N = 0
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 28.93 (18)
  Significant markers N = 0
Clinical variable #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

One gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1979.62 (21)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene differentially expressed by 'YEAR_OF_TOBACCO_SMOKING_ONSET'

Table S16.  Get Full Table List of one gene significantly correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET' by Spearman correlation test

SpearmanCorr corrP Q
HUNK 0.994 5.296e-07 0.0107
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 1
  BLACK OR AFRICAN AMERICAN 45
  WHITE 266
     
  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
PLAGL2__1 2.288e-13 1.42e-09
POFUT1__1 2.288e-13 1.42e-09
CAPRIN1 3.041e-13 1.42e-09
NEDD1 4.163e-13 1.42e-09
MKRN1 4.762e-13 1.42e-09
GSTCD__1 4.932e-13 1.42e-09
INTS12 4.932e-13 1.42e-09
HELZ 8.109e-13 2.04e-09
C5ORF28 9.548e-13 2.05e-09
CEP192 1.016e-12 2.05e-09
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 10
  NOT HISPANIC OR LATINO 255
     
  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 = 315

  • Number of genes = 20141

  • Number of clinical features = 12

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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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