Correlation between gene methylation status and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1TM79FX
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/C1TM79FX
Overview
Introduction

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

Summary

Testing the association between 16886 genes and 13 clinical features across 412 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 11 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • PLS1 ,  PCMTD2 ,  PLCD3 ,  EGFR ,  ZNF83 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • KLHL31 ,  PLVAP ,  RPS6KA1 ,  KIF15 ,  PRR7 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • GATA6 ,  ZBP1 ,  NAT1 ,  ARAP2 ,  ABCG2 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • THRA ,  GATA6 ,  CXCR2 ,  HSH2D ,  SAMD10 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • LDHB ,  NCRNA00188 ,  MRPS28 ,  MST1R ,  MIR17HG ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  C2ORF7 ,  KIF4B ,  C6ORF174 ,  MDH1B ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • FBXL14 ,  PIK3C2B ,  RHOT2 ,  C8ORF44 ,  KIAA1671 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • TULP4 ,  KCNH4 ,  ENTPD3 ,  BET3L ,  MFSD2A ,  ...

  • 1 gene correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • HMMR

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • MIR17HG ,  IGF2R ,  MRPS28 ,  SILV ,  KCNG1 ,  ...

  • 30 genes correlated to 'RACE'.

    • SCAMP5 ,  LOC100133161 ,  BTF3 ,  SPRED2 ,  SETMAR ,  ...

  • No genes correlated to '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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=22 younger N=8
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=19 lower stage N=11
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
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=28 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
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=18 lower number_of_lymph_nodes N=12
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) 0.4-166 (median=17.6)
  censored N = 229
  death N = 182
     
  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
PLS1 2.08e-07 0.0035 0.608
PCMTD2 5.23e-06 0.039 0.574
PLCD3 6.95e-06 0.039 0.55
EGFR 1.39e-05 0.059 0.366
ZNF83 3.69e-05 0.077 0.553
LCTL 3.77e-05 0.077 0.42
EFHD2 3.86e-05 0.077 0.599
SETD8 3.87e-05 0.077 0.525
C16ORF79 4.08e-05 0.077 0.501
CDH23 5.07e-05 0.084 0.6
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) 68.08 (11)
  Significant markers N = 30
  pos. correlated 22
  neg. correlated 8
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
KLHL31 0.2554 1.522e-07 0.00186
PLVAP -0.2472 3.877e-07 0.00186
RPS6KA1 0.2454 4.736e-07 0.00186
KIF15 0.2439 5.597e-07 0.00186
PRR7 0.2427 6.38e-07 0.00186
CCDC21 0.2417 7.11e-07 0.00186
F2RL1 0.2409 7.727e-07 0.00186
KLHL8 0.2377 1.093e-06 0.00231
LGALS9 0.235 1.504e-06 0.00275
MCF2L 0.2326 1.866e-06 0.00275
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 2
  STAGE II 131
  STAGE III 141
  STAGE IV 136
     
  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
GATA6 2.481e-12 4.19e-08
ZBP1 1.179e-11 9.95e-08
NAT1 2.765e-11 1.33e-07
ARAP2 3.144e-11 1.33e-07
ABCG2 5.788e-11 1.6e-07
FGF1 5.864e-11 1.6e-07
THRA 6.618e-11 1.6e-07
GBP4 5.214e-10 1.1e-06
NTN3 6.843e-10 1.28e-06
ENPP3 7.814e-10 1.32e-06
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) 2.82 (0.7)
  N
  T0 1
  T1 3
  T2 120
  T3 196
  T4 59
     
  Significant markers N = 30
  pos. correlated 19
  neg. correlated 11
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
THRA -0.332 3.333e-11 5.63e-07
GATA6 -0.3259 7.928e-11 6.69e-07
CXCR2 0.3142 3.937e-10 2.22e-06
HSH2D 0.3066 1.083e-09 4.55e-06
SAMD10 0.3038 1.558e-09 4.55e-06
TBX4 -0.3025 1.838e-09 4.55e-06
C21ORF49 0.3023 1.887e-09 4.55e-06
LGALS9C 0.3005 2.392e-09 5.05e-06
COL1A1 -0.2982 3.194e-09 5.99e-06
ABCG2 0.2945 5.067e-09 8.12e-06
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 Mean (SD) 0.6 (0.88)
  N
  N0 239
  N1 47
  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 S10.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
LDHB 0.2649 2.33e-07 0.00192
NCRNA00188 0.264 2.58e-07 0.00192
MRPS28 0.2614 3.407e-07 0.00192
MST1R 0.255 6.687e-07 0.00282
MIR17HG 0.247 1.508e-06 0.00509
TULP4 0.2442 1.995e-06 0.00561
RUNX1 0.2426 2.418e-06 0.00583
CSF2 0.2389 3.351e-06 0.00632
CIITA 0.2389 3.366e-06 0.00632
COL17A1 0.2357 4.559e-06 0.00729
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 196
  class1 11
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 108
  MALE 304
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S13.  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 31600 2.768e-46 4.67e-42 0.9625
C2ORF7 27019 1.978e-23 1.67e-19 0.8229
KIF4B 6864 2.577e-19 1.45e-15 0.7909
C6ORF174 10530 3.082e-08 0.00013 0.6793
MDH1B 10986 3.261e-07 0.0011 0.6654
LOC219347 11347 1.86e-06 0.00523 0.6544
NUP210 11539 4.486e-06 0.0108 0.6485
APITD1 11577 5.32e-06 0.0112 0.6474
NR1I2 11640 7.04e-06 0.0125 0.6455
DSCR6 11652 7.423e-06 0.0125 0.6451
Clinical variable #8: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 366
  YES 20
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
FBXL14 1474 6.856e-06 0.0486 0.7986
PIK3C2B 1489 7.923e-06 0.0486 0.7966
RHOT2 1553 1.454e-05 0.0486 0.7878
C8ORF44 1560 1.552e-05 0.0486 0.7869
KIAA1671 1563 1.596e-05 0.0486 0.7865
PLA2G2A 1584 1.94e-05 0.0486 0.7836
ZNF132 1588 2.013e-05 0.0486 0.7831
WNT7B 1609 2.44e-05 0.0515 0.7802
C7ORF51 1641 3.261e-05 0.0538 0.7758
RND1 1646 3.411e-05 0.0538 0.7751
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 83.09 (14)
  Significant markers N = 30
  pos. correlated 28
  neg. correlated 2
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

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

SpearmanCorr corrP Q
TULP4 0.4665 1.037e-08 8.8e-05
KCNH4 0.4665 1.043e-08 8.8e-05
ENTPD3 0.4472 4.801e-08 0.000227
BET3L 0.4443 6.015e-08 0.000227
MFSD2A 0.4428 6.735e-08 0.000227
STXBP3 0.4356 1.158e-07 0.000326
S1PR5 0.4317 1.543e-07 0.000348
PLCD3 0.4302 1.719e-07 0.000348
FUCA1 0.4278 2.041e-07 0.000348
C12ORF51 0.4259 2.343e-07 0.000348
Clinical variable #10: 'NUMBER_PACK_YEARS_SMOKED'

One gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 39.04 (53)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

Table S19.  Get Full Table List of one gene significantly correlated to 'NUMBER_PACK_YEARS_SMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HMMR -0.3278 5.215e-07 0.00881
Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.09 (7)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S21.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
MIR17HG 0.261 5.167e-06 0.0684
IGF2R 0.2557 8.105e-06 0.0684
MRPS28 0.2439 2.144e-05 0.0916
SILV -0.2431 2.286e-05 0.0916
KCNG1 -0.2409 2.711e-05 0.0916
VASN -0.2338 4.741e-05 0.0935
ZMYND12 -0.2334 4.889e-05 0.0935
RFC1 0.2318 5.512e-05 0.0935
GALNT3 0.231 5.851e-05 0.0935
NCRNA00188 0.2288 6.921e-05 0.0935
Clinical variable #12: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 44
  BLACK OR AFRICAN AMERICAN 23
  WHITE 327
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S23.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
SCAMP5 9.619e-15 1.62e-10
LOC100133161 5.393e-12 4.15e-08
BTF3 7.455e-12 4.15e-08
SPRED2 9.833e-12 4.15e-08
SETMAR 1.458e-11 4.22e-08
SH3YL1 1.499e-11 4.22e-08
C10ORF58 1.845e-11 4.45e-08
AGPHD1 2.312e-11 4.88e-08
ISCA1 3.095e-11 5.81e-08
SLC44A3 4.32e-11 7.2e-08
Clinical variable #13: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S24.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 9
  NOT HISPANIC OR LATINO 371
     
  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 = 412

  • Number of genes = 16886

  • Number of clinical features = 13

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)
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