Correlation between gene methylation status and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1JQ105W
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/C1JQ105W
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 19974 genes and 12 clinical features across 783 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • KIAA1143 ,  KIF15 ,  LGALS8 ,  EGR2 ,  MEX3C ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • FAM98C ,  PSPN ,  RFFL ,  UBE2Z ,  CLP1 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • MYOC ,  C1QL3 ,  FAM5C ,  PCDHGA1__13 ,  PCDHGA10__5 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • TCP11L1 ,  ADA ,  KIAA0182 ,  ARHGEF3__1 ,  SPATA12 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • PRELID1 ,  RAB24 ,  RFX1 ,  DDT__1 ,  DDTL ,  ...

  • 30 genes correlated to 'GENDER'.

    • WNT7B ,  ARSG ,  STC1 ,  LGALS1 ,  PRRT3 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • SCD ,  FADS1 ,  MCAM ,  PRCD ,  ITGA1__1 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • TCP11L1 ,  SEPT8 ,  CCDC80 ,  ABCC8 ,  PARP1 ,  ...

  • 30 genes correlated to 'RACE'.

    • DHRS7 ,  PPP1R15B ,  ISCA1 ,  ZNF639 ,  EIF2AK4 ,  ...

  • 30 genes correlated to 'ETHNICITY'.

    • LRRC66 ,  SCFD2 ,  WDFY3__1 ,  SNORD1C ,  ANKRD13C ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', and 'RADIATION_THERAPY'.

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=27 younger N=3
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=26 lower stage N=4
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=16 lower stage N=14
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=16 lower number_of_lymph_nodes N=14
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test N=30 not hispanic or latino N=30 hispanic or latino 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) 0-282.9 (median=27.1)
  censored N = 679
  death N = 103
     
  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) 58.17 (13)
  Significant markers N = 30
  pos. correlated 27
  neg. correlated 3
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
KIAA1143 0.3279 9.283e-21 9.27e-17
KIF15 0.3279 9.283e-21 9.27e-17
LGALS8 -0.2996 1.961e-17 1.31e-13
EGR2 0.2898 2.296e-16 1.15e-12
MEX3C 0.2838 1e-15 3.99e-12
CACNA2D1 0.279 3.125e-15 1.04e-11
BMPER 0.2703 2.346e-14 5.79e-11
C20ORF199 0.2698 2.609e-14 5.79e-11
SNORD12 0.2698 2.609e-14 5.79e-11
TNFSF11 0.2674 4.472e-14 8.93e-11
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 61
  STAGE IA 62
  STAGE IB 5
  STAGE II 6
  STAGE IIA 244
  STAGE IIB 187
  STAGE III 2
  STAGE IIIA 126
  STAGE IIIB 19
  STAGE IIIC 53
  STAGE IV 11
  STAGE X 5
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

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

kruskal_wallis_P Q
FAM98C 5.014e-06 0.0846
PSPN 1.865e-05 0.0846
RFFL 2.206e-05 0.0846
UBE2Z 3.055e-05 0.0846
CLP1 3.348e-05 0.0846
CC2D1A__1 3.374e-05 0.0846
DLL4 3.757e-05 0.0846
C15ORF63__1 4.229e-05 0.0846
SERINC4__1 4.229e-05 0.0846
CYFIP1 5.019e-05 0.0846
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) 1.94 (0.72)
  N
  T1 201
  T2 446
  T3 109
  T4 24
     
  Significant markers N = 30
  pos. correlated 26
  neg. correlated 4
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
MYOC -0.1566 1.118e-05 0.0279
C1QL3 0.149 2.951e-05 0.0279
FAM5C 0.1489 2.993e-05 0.0279
PCDHGA1__13 0.1478 3.427e-05 0.0279
PCDHGA10__5 0.1478 3.427e-05 0.0279
PCDHGA11__4 0.1478 3.427e-05 0.0279
PCDHGA12__3 0.1478 3.427e-05 0.0279
PCDHGA2__13 0.1478 3.427e-05 0.0279
PCDHGA3__12 0.1478 3.427e-05 0.0279
PCDHGA4__11 0.1478 3.427e-05 0.0279
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) 0.82 (0.92)
  N
  N0 350
  N1 269
  N2 95
  N3 57
     
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
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
TCP11L1 0.1721 1.543e-06 0.0198
ADA 0.1699 2.09e-06 0.0198
KIAA0182 -0.1674 2.973e-06 0.0198
ARHGEF3__1 0.1637 5.017e-06 0.02
SPATA12 0.1637 5.017e-06 0.02
LOC100286844 -0.1568 1.226e-05 0.038
SEPT8 -0.1554 1.458e-05 0.038
RFFL 0.155 1.543e-05 0.038
PDE9A 0.1538 1.803e-05 0.038
CYFIP1 -0.1524 2.144e-05 0.038
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 609
  class1 13
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
PRELID1 1312 3.672e-05 0.299 0.8343
RAB24 1312 3.672e-05 0.299 0.8343
RFX1 1415 7.292e-05 0.299 0.8213
DDT__1 1525 0.0001477 0.299 0.8074
DDTL 1525 0.0001477 0.299 0.8074
EHMT1__1 1566 0.0001907 0.299 0.8022
FLJ40292 1566 0.0001907 0.299 0.8022
KDM3B 6332 0.0002012 0.299 0.8011
FAM160B1 6318 0.0002336 0.299 0.798
NUP50 6318 0.0002336 0.299 0.798
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 774
  MALE 9
     
  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
WNT7B 525 1.165e-05 0.105 0.9246
ARSG 652 2.72e-05 0.105 0.9064
STC1 691 3.611e-05 0.105 0.9002
LGALS1 722 4.278e-05 0.105 0.8964
PRRT3 724 4.333e-05 0.105 0.8961
SLC16A12 740 4.828e-05 0.105 0.8936
C15ORF62 6167 6.956e-05 0.105 0.8853
DNAJC17 6167 6.956e-05 0.105 0.8853
SLC25A10 802 7.087e-05 0.105 0.8849
BAIAP2__1 813 7.588e-05 0.105 0.8833
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 309
  YES 391
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  INFILTRATING CARCINOMA NOS 1
  INFILTRATING DUCTAL CARCINOMA 513
  INFILTRATING LOBULAR CARCINOMA 178
  MEDULLARY CARCINOMA 6
  METAPLASTIC CARCINOMA 9
  MIXED HISTOLOGY (PLEASE SPECIFY) 25
  MUCINOUS CARCINOMA 15
  OTHER SPECIFY 35
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S16.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
SCD 6.297e-22 1.26e-17
FADS1 8.869e-21 8.86e-17
MCAM 2.914e-20 1.94e-16
PRCD 4.254e-20 2.12e-16
ITGA1__1 9.582e-20 3.19e-16
PELO__1 9.582e-20 3.19e-16
PRPF40A 4.368e-19 1.25e-15
PABPC4 5.776e-19 1.44e-15
PCGF6 1.426e-18 3.16e-15
SOD1 2.152e-18 4.3e-15
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) 2.6 (4.9)
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
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
TCP11L1 0.1977 9.269e-08 0.00185
SEPT8 -0.1689 5.346e-06 0.0432
CCDC80 -0.1624 1.219e-05 0.0432
ABCC8 0.1608 1.496e-05 0.0432
PARP1 0.1606 1.526e-05 0.0432
KIAA0182 -0.1602 1.602e-05 0.0432
ADA 0.159 1.873e-05 0.0432
SCAPER -0.1586 1.965e-05 0.0432
IL21R 0.158 2.113e-05 0.0432
BTG1 0.1576 2.212e-05 0.0432
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 38
  BLACK OR AFRICAN AMERICAN 160
  WHITE 569
     
  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
DHRS7 7.32e-37 1.46e-32
PPP1R15B 2.278e-36 2.27e-32
ISCA1 5.038e-33 3.35e-29
ZNF639 2.95e-31 1.47e-27
EIF2AK4 1.208e-29 4.83e-26
TOMM34 1.319e-28 4.39e-25
SCAMP5 6.021e-28 1.72e-24
RHD 2.233e-27 5.58e-24
FYTTD1 3.32e-27 6.63e-24
KIAA0226 3.32e-27 6.63e-24
Clinical variable #12: 'ETHNICITY'

30 genes related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 38
  NOT HISPANIC OR LATINO 676
     
  Significant markers N = 30
  Higher in NOT HISPANIC OR LATINO 30
  Higher in HISPANIC OR LATINO 0
List of top 10 genes differentially expressed by 'ETHNICITY'

Methods & Data
Input

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

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

  • Number of patients = 783

  • Number of genes = 19974

  • Number of clinical features = 12

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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