Correlation between gene methylation status and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1F47N6P
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/C1F47N6P
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 255 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 ,  MRPL44 ,  ARGFXP2 ,  RHOT1 ,  ...

  • 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • DLX6 ,  DLX6AS ,  FOXA2 ,  DLEU2 ,  TMEM132B ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • FOXA2 ,  DLX6 ,  DLX6AS ,  DIDO1 ,  TMEM132B ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • LBXCOR1 ,  KLK7 ,  HOXA13 ,  ANKRD20A4 ,  SLC9A3 ,  ...

  • 30 genes correlated to 'GENDER'.

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

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • GREB1 ,  ZNF610 ,  ZNF251 ,  LUZP6 ,  MTPN ,  ...

  • 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=7 younger N=23
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=24 lower stage N=6
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=20 lower score N=10
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=20.2)
  censored N = 222
  death N = 32
     
  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) 61.61 (12)
  Significant markers N = 30
  pos. correlated 7
  neg. correlated 23
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 0.384 3.305e-10 6.55e-06
ARHGAP1 -0.3144 3.854e-07 0.00382
MRPL44 -0.2934 2.363e-06 0.0102
ARGFXP2 -0.2923 2.581e-06 0.0102
RHOT1 -0.2923 2.581e-06 0.0102
FAM173B -0.2861 4.286e-06 0.0141
SARS -0.2833 5.342e-06 0.0151
KLF9 -0.278 8.14e-06 0.018
NTM 0.2773 8.564e-06 0.018
AAK1 -0.2766 9.09e-06 0.018
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 164
  STAGE II 18
  STAGE III 48
  STAGE IV 13
     
  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
DLX6 6.588e-13 6.36e-09
DLX6AS 6.588e-13 6.36e-09
FOXA2 9.638e-13 6.36e-09
DLEU2 5.028e-12 2.49e-08
TMEM132B 6.495e-12 2.57e-08
DSCR6 2.81e-11 9.28e-08
LBXCOR1 5.879e-11 1.66e-07
NSD1 1.897e-10 4.7e-07
FLOT2 2.891e-10 5.39e-07
LOC148824 3.222e-10 5.39e-07
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) 1.53 (0.85)
  N
  T1 171
  T2 22
  T3 52
  T4 2
     
  Significant markers N = 30
  pos. correlated 24
  neg. correlated 6
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
FOXA2 0.4709 4.914e-15 9.73e-11
DLX6 0.4599 2.502e-14 1.65e-10
DLX6AS 0.4599 2.502e-14 1.65e-10
DIDO1 0.4406 3.759e-13 1.58e-09
TMEM132B -0.4401 3.99e-13 1.58e-09
LBXCOR1 0.4352 7.769e-13 2.56e-09
DSCR6 0.432 1.188e-12 3.36e-09
GPR150 0.422 4.362e-12 1.08e-08
GNASAS -0.4204 5.378e-12 1.16e-08
FLOT2 0.4189 6.484e-12 1.16e-08
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) 0.43 (0.61)
  N
  N0 43
  N1 21
  N2 4
     
  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 0.6337 6.638e-09 7e-05
KLK7 0.6328 7.067e-09 7e-05
HOXA13 0.6103 3.292e-08 0.000217
ANKRD20A4 0.5925 1.019e-07 0.000357
SLC9A3 0.5911 1.116e-07 0.000357
RIOK3 0.5893 1.246e-07 0.000357
COL9A2 0.586 1.52e-07 0.000357
PLEKHF2 0.585 1.612e-07 0.000357
ZSCAN10 0.5849 1.623e-07 0.000357
CPA5 0.579 2.305e-07 0.000457
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 81
  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 69
  MALE 186
     
  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 11682 8.205e-24 8.13e-20 0.9102
UTP14C 11682 8.205e-24 8.13e-20 0.9102
PRKRIR 11507 2.32e-22 1.53e-18 0.8966
MYST2 10980 2.794e-18 1.38e-14 0.8555
C5ORF27 2655 6.541e-13 2.59e-09 0.7931
NARFL 2714 1.484e-12 4.9e-09 0.7885
AOX1 2787 4.021e-12 1.14e-08 0.7828
PEMT 2835 7.663e-12 1.9e-08 0.7791
ADARB2 3049 1.229e-10 2.45e-07 0.7624
PFKL 3055 1.325e-10 2.45e-07 0.762
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) 93.67 (9.9)
  Score N
  40 1
  70 1
  80 4
  90 21
  100 33
     
  Significant markers N = 30
  pos. correlated 20
  neg. correlated 10
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
GREB1 0.5543 4.32e-06 0.0856
ZNF610 -0.5387 8.984e-06 0.089
ZNF251 -0.4936 6.146e-05 0.147
LUZP6 0.4922 6.497e-05 0.147
MTPN 0.4922 6.497e-05 0.147
NPHP4 -0.4911 6.767e-05 0.147
PTPRE 0.4859 8.295e-05 0.147
APOBEC3H 0.485 8.601e-05 0.147
CD46 0.483 9.29e-05 0.147
CHAF1B 0.4813 9.897e-05 0.147
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) 32.72 (29)
  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) 1972.02 (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 53
  WHITE 180
     
  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 3.465e-17 6.86e-13
RCBTB2 4.791e-12 4.75e-08
DARC 3.557e-09 1.42e-05
C14ORF167 3.582e-09 1.42e-05
DHRS4 3.582e-09 1.42e-05
SETD1A 1.6e-08 5.28e-05
CS 2.33e-08 6.08e-05
NUDT13 2.456e-08 6.08e-05
TP53 4.518e-08 7.76e-05
WRAP53 4.518e-08 7.76e-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 206
     
  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 = 255

  • Number of genes = 19808

  • 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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