Correlation between gene methylation status and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1RB73WW
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/C1RB73WW
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 19971 genes and 11 clinical features across 497 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

  • 11 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • GAS2L3 ,  LIPA ,  ADH6 ,  SLC9A9 ,  CHRNB2 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • INA ,  FZD9 ,  OXT ,  CECR6 ,  EPO ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • TACC2 ,  FGD4 ,  TEPP ,  ABCC10 ,  FAM13C ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • C11ORF10 ,  FEN1 ,  MIR611 ,  KIAA0922 ,  EIF4A3 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • KIAA0922 ,  LOC595101 ,  DLGAP5 ,  RPS6KL1 ,  MME ,  ...

  • 30 genes correlated to 'RESIDUAL_TUMOR'.

    • RIC3 ,  HNF1B ,  KIAA1751 ,  FRYL ,  ARGLU1 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • KIAA0922 ,  C11ORF10 ,  FEN1 ,  MIR611 ,  EIF4A3 ,  ...

  • 30 genes correlated to 'GLEASON_SCORE'.

    • FAM13C ,  KIAA0922 ,  PLK1 ,  OR51A7 ,  C11ORF10 ,  ...

  • 30 genes correlated to 'PSA_VALUE'.

    • OR2A7 ,  OVOL1 ,  C6ORF41__1 ,  LOC100270746__1 ,  COMMD4 ,  ...

  • No genes correlated to 'HISTOLOGICAL_TYPE', and 'RACE'.

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=11 shorter survival N=10 longer survival N=1
YEARS_TO_BIRTH Spearman correlation test N=30 older N=30 younger N=0
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=23 lower stage N=7
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=30 n0 N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Wilcoxon test   N=0        
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=0 lower number_of_lymph_nodes N=30
GLEASON_SCORE Spearman correlation test N=30 higher score N=12 lower score N=18
PSA_VALUE Spearman correlation test N=30 higher psa_value N=7 lower psa_value N=23
RACE Kruskal-Wallis test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

11 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) 0.7-165.2 (median=28.8)
  censored N = 486
  death N = 10
     
  Significant markers N = 11
  associated with shorter survival 10
  associated with longer survival 1
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

HazardRatio Wald_P Q C_index
GAS2L3 1.6e+22 6.006e-06 0.12 0.795
LIPA 86000001 1.874e-05 0.17 0.657
ADH6 0 3.127e-05 0.17 0.184
SLC9A9 201 4.168e-05 0.17 0.707
CHRNB2 40000000001 4.601e-05 0.17 0.747
IPO9 5.2e+15 5.023e-05 0.17 0.724
ZMYND10 14001 7.035e-05 0.2 0.607
RNH1 1.7e+16 0.0001055 0.25 0.624
IGSF9B 4101 0.0001109 0.25 0.729
KCNG1 10001 0.000152 0.28 0.714
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) 61.02 (6.8)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
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
INA 0.2767 5.415e-10 1.08e-05
FZD9 0.2649 3.016e-09 3.01e-05
OXT 0.2557 1.076e-08 7.17e-05
CECR6 0.2415 7.078e-08 0.000257
EPO 0.2409 7.596e-08 0.000257
SOX11 0.2408 7.723e-08 0.000257
TMEM74 0.2378 1.133e-07 0.000319
C17ORF104 0.2368 1.277e-07 0.000319
NKX2-5 0.235 1.6e-07 0.000344
RPP25 0.2344 1.723e-07 0.000344
Clinical variable #3: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 2.64 (0.52)
  N
  T2 189
  T3 292
  T4 10
     
  Significant markers N = 30
  pos. correlated 23
  neg. correlated 7
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S6.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
TACC2 0.3584 2.527e-16 3.22e-12
FGD4 0.3572 3.22e-16 3.22e-12
TEPP 0.3345 2.653e-14 1.77e-10
ABCC10 0.3324 3.959e-14 1.86e-10
FAM13C 0.3315 4.647e-14 1.86e-10
KIAA0922 -0.3301 6.012e-14 2e-10
INPP5K 0.3219 2.665e-13 7.6e-10
KBTBD11 0.3164 7.499e-13 1.83e-09
FAM55B 0.3155 8.239e-13 1.83e-09
SATB1 0.3104 2.463e-12 4.92e-09
Clinical variable #4: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Labels N
  N0 345
  N1 79
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
C11ORF10 6508 4.296e-13 2.24e-09 0.7612
FEN1 6508 4.296e-13 2.24e-09 0.7612
MIR611 6508 4.296e-13 2.24e-09 0.7612
KIAA0922 6514 4.494e-13 2.24e-09 0.761
EIF4A3 7443 3.089e-10 1.23e-06 0.7269
GUCA1B 7525 5.273e-10 1.76e-06 0.7239
HN1 7755 2.277e-09 5.18e-06 0.7155
ERP29__1 7759 2.335e-09 5.18e-06 0.7153
TMEM116__1 7759 2.335e-09 5.18e-06 0.7153
DHX9 7890 5.243e-09 1.05e-05 0.7105
Clinical variable #5: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

Table S9.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 383
  YES 57
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

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

W(pos if higher in 'YES') wilcoxontestP Q AUC
KIAA0922 5917 2.406e-08 0.00048 0.729
LOC595101 6396 4.531e-07 0.00452 0.707
DLGAP5 6726 2.914e-06 0.0194 0.6919
RPS6KL1 6806 4.487e-06 0.0224 0.6882
MME 7026 1.413e-05 0.0564 0.6782
MAPKAPK2 7099 2.041e-05 0.0591 0.6748
PLK1 7102 2.072e-05 0.0591 0.6747
SFXN5 7175 2.974e-05 0.0664 0.6713
SAMD1 7184 3.108e-05 0.0664 0.6709
SNORD17__1 7237 4.021e-05 0.0664 0.6685
Clinical variable #6: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PROSTATE ADENOCARCINOMA OTHER SUBTYPE 15
  PROSTATE ADENOCARCINOMA ACINAR TYPE 482
     
  Significant markers N = 0
Clinical variable #7: 'RESIDUAL_TUMOR'

30 genes related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 315
  R1 146
  R2 5
  RX 15
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
RIC3 2.077e-07 0.00415
HNF1B 8.108e-07 0.00707
KIAA1751 1.823e-06 0.00707
FRYL 1.852e-06 0.00707
ARGLU1 2.078e-06 0.00707
CYP7B1 2.247e-06 0.00707
MAST2 2.81e-06 0.00707
LOC93622 3.121e-06 0.00707
PGS1 3.335e-06 0.00707
CLDN9 3.677e-06 0.00707
Clinical variable #8: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 0.44 (1.4)
  Significant markers N = 30
  pos. correlated 0
  neg. correlated 30
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

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

SpearmanCorr corrP Q
KIAA0922 -0.3735 6.94e-15 8.95e-11
C11ORF10 -0.3681 1.793e-14 8.95e-11
FEN1 -0.3681 1.793e-14 8.95e-11
MIR611 -0.3681 1.793e-14 8.95e-11
EIF4A3 -0.332 6.678e-12 2.67e-08
GUCA1B -0.319 4.663e-11 1.55e-07
HN1 -0.3103 1.634e-10 4.66e-07
CORO7 -0.3046 3.669e-10 8.5e-07
LOC93622 -0.3026 4.842e-10 8.5e-07
SIN3B -0.3024 4.953e-10 8.5e-07
Clinical variable #9: 'GLEASON_SCORE'

30 genes related to 'GLEASON_SCORE'.

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

GLEASON_SCORE Mean (SD) 7.61 (1)
  Score N
  6 45
  7 248
  8 64
  9 136
  10 4
     
  Significant markers N = 30
  pos. correlated 12
  neg. correlated 18
List of top 10 genes differentially expressed by 'GLEASON_SCORE'

Table S17.  Get Full Table List of top 10 genes significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
FAM13C 0.4307 7.347e-24 1.47e-19
KIAA0922 -0.4203 1.092e-22 1.09e-18
PLK1 -0.4096 1.568e-21 1.04e-17
OR51A7 -0.3922 1.003e-19 5.01e-16
C11ORF10 -0.3864 3.818e-19 1.09e-15
FEN1 -0.3864 3.818e-19 1.09e-15
MIR611 -0.3864 3.818e-19 1.09e-15
FGD4 0.3857 4.477e-19 1.12e-15
PTPRN2 0.3829 8.352e-19 1.85e-15
CYP7B1 0.3808 1.354e-18 2.7e-15
Clinical variable #10: 'PSA_VALUE'

30 genes related to 'PSA_VALUE'.

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

PSA_VALUE Mean (SD) 1.79 (16)
  Significant markers N = 30
  pos. correlated 7
  neg. correlated 23
List of top 10 genes differentially expressed by 'PSA_VALUE'

Table S19.  Get Full Table List of top 10 genes significantly correlated to 'PSA_VALUE' by Spearman correlation test

SpearmanCorr corrP Q
OR2A7 -0.2279 1.361e-06 0.0118
OVOL1 -0.2239 2.1e-06 0.0118
C6ORF41__1 0.2184 3.748e-06 0.0118
LOC100270746__1 0.2184 3.748e-06 0.0118
COMMD4 0.2164 4.623e-06 0.0118
BAIAP2__1 0.2147 5.518e-06 0.0118
SNORD17__1 -0.2139 5.982e-06 0.0118
SNX5__1 -0.2139 5.982e-06 0.0118
ATF5__1 -0.2131 6.482e-06 0.0118
IL4I1__1 -0.2131 6.482e-06 0.0118
Clinical variable #11: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 7
  WHITE 147
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 497

  • Number of genes = 19971

  • Number of clinical features = 11

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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