Correlation between gene methylation status and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C15Q4V15
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/C15Q4V15
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 19914 genes and 12 clinical features across 393 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • PRR7 ,  F2RL1 ,  RPS6KA1 ,  KLHL31 ,  GSDMB ,  ...

  • 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • ABCG2 ,  ZBP1 ,  GATA6 ,  NR1D1 ,  THRA ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • NR1D1 ,  THRA ,  PRPF6 ,  SAMD10 ,  CXCR2 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • LDHB ,  MRPS28 ,  MST1R ,  NCRNA00188 ,  SNORD49A ,  ...

  • 30 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  C2ORF7 ,  CCT7 ,  KIF4B ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • KCNH4 ,  TULP4 ,  ENTPD3 ,  GPR4 ,  BET3L ,  ...

  • 15 genes correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • HMMR ,  NUDCD2 ,  ATXN2L ,  NFYB ,  ATE1 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • MRPS28 ,  MIR17 ,  MIR17HG ,  MIR18A ,  MIR19A ,  ...

  • 30 genes correlated to 'RACE'.

    • SCAMP5 ,  BTF3 ,  SH3YL1 ,  SPRED2 ,  C10ORF58 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_M_STAGE', 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=28 younger N=2
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=20 lower stage N=10
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=23 lower stage N=7
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=29 lower score N=1
NUMBER_PACK_YEARS_SMOKED Spearman correlation test N=15 higher number_pack_years_smoked N=0 lower number_pack_years_smoked N=15
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=19 lower number_of_lymph_nodes N=11
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) 0.1-166 (median=15.6)
  censored N = 236
  death N = 156
     
  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) 67.76 (11)
  Significant markers N = 30
  pos. correlated 28
  neg. correlated 2
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
PRR7 0.2685 6.685e-08 0.000453
F2RL1 0.2675 7.572e-08 0.000453
RPS6KA1 0.2661 8.897e-08 0.000453
KLHL31 0.2659 9.092e-08 0.000453
GSDMB 0.2554 2.95e-07 0.000962
KLHL8 0.2548 3.179e-07 0.000962
C14ORF72 0.2542 3.383e-07 0.000962
RAB26 0.2518 4.391e-07 0.00109
TNS3 -0.2495 5.611e-07 0.00116
LGALS9 0.2495 5.815e-07 0.00116
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 4
  STAGE II 126
  STAGE III 128
  STAGE IV 130
     
  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
ABCG2 2.576e-11 2.46e-07
ZBP1 2.841e-11 2.46e-07
GATA6 3.701e-11 2.46e-07
NR1D1 1.119e-10 4.46e-07
THRA 1.119e-10 4.46e-07
ENPP3__2 1.975e-10 5.96e-07
NAT1 2.097e-10 5.96e-07
ARAP2 2.843e-10 7.08e-07
CXCR2 3.767e-10 8.34e-07
FGF1 6.873e-10 1.37e-06
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) 2.79 (0.72)
  N
  T0 2
  T1 3
  T2 117
  T3 185
  T4 54
     
  Significant markers N = 30
  pos. correlated 20
  neg. correlated 10
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
NR1D1 -0.33 1.28e-10 1.27e-06
THRA -0.33 1.28e-10 1.27e-06
PRPF6 0.3161 8.129e-10 3.14e-06
SAMD10 0.3161 8.129e-10 3.14e-06
CXCR2 0.3149 9.413e-10 3.14e-06
GATA6 -0.3149 9.456e-10 3.14e-06
TBX4 -0.3059 2.956e-09 7.47e-06
ABCG2 0.3049 3.323e-09 7.47e-06
HSH2D 0.3048 3.377e-09 7.47e-06
LRP1 -0.3023 4.602e-09 9.07e-06
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) 0.62 (0.9)
  N
  N0 227
  N1 42
  N2 76
  N3 8
     
  Significant markers N = 30
  pos. correlated 23
  neg. correlated 7
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
LDHB 0.2701 2.56e-07 0.00239
MRPS28 0.2646 4.542e-07 0.00239
MST1R 0.2618 6.084e-07 0.00239
NCRNA00188 0.2589 8.154e-07 0.00239
SNORD49A 0.2589 8.154e-07 0.00239
SNORD65 0.2589 8.154e-07 0.00239
DAB2 -0.2586 8.415e-07 0.00239
SHISA3 0.2481 2.38e-06 0.00388
RUNX1 0.2463 2.906e-06 0.00388
TADA2B__1 0.2459 2.934e-06 0.00388
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 189
  class1 10
     
  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 102
  MALE 291
     
  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 28515 1.258e-43 1.25e-39 0.9607
UTP14C 28515 1.258e-43 1.25e-39 0.9607
C2ORF7 24339 6.54e-22 3.26e-18 0.82
CCT7 24339 6.54e-22 3.26e-18 0.82
KIF4B 6324 6.301e-18 2.51e-14 0.7869
C6ORF174 9549 8.317e-08 0.000276 0.6783
ALDH3B2 9845 4.186e-07 0.00119 0.6683
C10ORF57 9895 5.453e-07 0.00121 0.6666
LOC219347 9895 5.453e-07 0.00121 0.6666
FASTKD2__1 10041 1.164e-06 0.00211 0.6617
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) 82.78 (14)
  Significant markers N = 30
  pos. correlated 29
  neg. correlated 1
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
KCNH4 0.4983 2.895e-09 5.1e-05
TULP4 0.4914 5.122e-09 5.1e-05
ENTPD3 0.4757 1.813e-08 0.00012
GPR4 0.4674 3.436e-08 0.000167
BET3L 0.4617 5.283e-08 0.000167
FAM26D 0.4617 5.283e-08 0.000167
MFSD2A 0.4603 5.883e-08 0.000167
RIPK4 0.4556 8.322e-08 0.000207
ACBD4 0.4499 1.257e-07 0.000238
PLCD3 0.4499 1.257e-07 0.000238
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

15 genes related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 38.79 (54)
  Significant markers N = 15
  pos. correlated 0
  neg. correlated 15
List of top 10 genes differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

Table S16.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_PACK_YEARS_SMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HMMR -0.3491 1.582e-07 0.00158
NUDCD2 -0.3491 1.582e-07 0.00158
ATXN2L -0.2757 4.332e-05 0.246
NFYB -0.2713 5.786e-05 0.246
ATE1 -0.2653 8.534e-05 0.246
DENND4A -0.2625 0.000102 0.246
IL24 -0.2601 0.0001182 0.246
MIER1 -0.2598 0.0001207 0.246
WDR78 -0.2598 0.0001207 0.246
CAPNS1 -0.2595 0.0001234 0.246
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.02 (6.9)
  Significant markers N = 30
  pos. correlated 19
  neg. correlated 11
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S18.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
MRPS28 0.2642 6.375e-06 0.0167
MIR17 0.2635 6.746e-06 0.0167
MIR17HG 0.2635 6.746e-06 0.0167
MIR18A 0.2635 6.746e-06 0.0167
MIR19A 0.2635 6.746e-06 0.0167
MIR19B1 0.2635 6.746e-06 0.0167
MIR20A 0.2635 6.746e-06 0.0167
MIR92A1 0.2635 6.746e-06 0.0167
CORO7 -0.2609 8.364e-06 0.0167
VASN -0.2609 8.364e-06 0.0167
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 43
  BLACK OR AFRICAN AMERICAN 22
  WHITE 311
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S20.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
SCAMP5 3.269e-15 6.51e-11
BTF3 2.232e-11 1.31e-07
SH3YL1 2.43e-11 1.31e-07
SPRED2 2.622e-11 1.31e-07
C10ORF58 3.629e-11 1.39e-07
SETMAR 4.8e-11 1.39e-07
LOC100133161 6.082e-11 1.39e-07
COBLL1 6.164e-11 1.39e-07
SH3BP1 7.34e-11 1.39e-07
SLC44A3 8.478e-11 1.39e-07
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S21.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 9
  NOT HISPANIC OR LATINO 354
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 393

  • Number of genes = 19914

  • 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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