Correlation between gene methylation status and clinical features
Colon Adenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1MW2G96
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/C1MW2G96
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 19824 genes and 13 clinical features across 292 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'.

    • GPR1 ,  OTUD7A ,  C1QL1 ,  SHISA3 ,  SCGB3A1 ,  ...

  • 1 gene correlated to 'PATHOLOGIC_STAGE'.

    • UBE2L6

  • 1 gene correlated to 'PATHOLOGY_T_STAGE'.

    • PHYHIP

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • CASP1__1 ,  APOL1 ,  UBE2L6 ,  IL12RB1 ,  ASPHD2 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • ARAP2 ,  KIF13B ,  PBK ,  EBF1 ,  RHOBTB2 ,  ...

  • 30 genes correlated to 'GENDER'.

    • GPX1 ,  KIF4B ,  POLDIP3 ,  RNU12 ,  MIR220B ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • POFUT1 ,  POLR1D ,  BCL2L1 ,  EPN2 ,  REG4 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • CASP1__1 ,  UBE2L6 ,  APOL1 ,  IL12RB1 ,  CSGALNACT1 ,  ...

  • 30 genes correlated to 'RACE'.

    • SMARCC2 ,  LOC100133161 ,  GSTCD__1 ,  INTS12__1 ,  UBTF ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'RADIATION_THERAPY', 'RESIDUAL_TUMOR', 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=30 younger N=0
PATHOLOGIC_STAGE Kruskal-Wallis test N=1        
PATHOLOGY_T_STAGE Spearman correlation test N=1 higher stage N=0 lower stage N=1
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=22 lower stage N=8
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 Wilcoxon test N=30 colon mucinous adenocarcinoma N=30 colon adenocarcinoma N=0
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=26 lower number_of_lymph_nodes N=4
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) 0.1-148 (median=21.8)
  censored N = 223
  death N = 68
     
  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) 64.95 (13)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
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
GPR1 0.3293 9.183e-09 0.000182
OTUD7A 0.3155 4.001e-08 0.000397
C1QL1 0.3064 1.012e-07 0.000669
SHISA3 0.3013 1.68e-07 0.000769
SCGB3A1 0.2983 2.272e-07 0.000769
CORO6 0.298 2.326e-07 0.000769
CBS 0.2919 4.195e-07 0.00119
LOC389705 0.2902 4.909e-07 0.00122
ST8SIA4 0.2883 5.915e-07 0.0013
TMEM130 0.282 1.054e-06 0.00192
Clinical variable #3: 'PATHOLOGIC_STAGE'

One gene related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 43
  STAGE IA 1
  STAGE II 14
  STAGE IIA 94
  STAGE IIB 5
  STAGE IIC 1
  STAGE III 7
  STAGE IIIA 6
  STAGE IIIB 47
  STAGE IIIC 25
  STAGE IV 23
  STAGE IVA 16
  STAGE IVB 2
     
  Significant markers N = 1
List of one gene differentially expressed by 'PATHOLOGIC_STAGE'

Table S5.  Get Full Table List of one gene differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
UBE2L6 1.368e-05 0.271
Clinical variable #4: 'PATHOLOGY_T_STAGE'

One gene related to 'PATHOLOGY_T_STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 2.94 (0.61)
  N
  T1 7
  T2 43
  T3 202
  T4 39
     
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene differentially expressed by 'PATHOLOGY_T_STAGE'

Table S7.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
PHYHIP -0.2648 4.657e-06 0.0923
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) 0.59 (0.76)
  N
  N0 170
  N1 73
  N2 49
     
  Significant markers N = 30
  pos. correlated 22
  neg. correlated 8
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
CASP1__1 0.3442 1.513e-09 3e-05
APOL1 0.3199 2.257e-08 0.000224
UBE2L6 0.3039 1.18e-07 0.00078
IL12RB1 0.2927 3.523e-07 0.00175
ASPHD2 0.2819 9.82e-07 0.00389
PKN2 0.2777 1.434e-06 0.00474
PWP2 0.2723 2.333e-06 0.00661
UBA7 0.2698 2.913e-06 0.00722
ACTA2__1 0.2667 3.81e-06 0.00755
FAS 0.2667 3.81e-06 0.00755
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 194
  class1 41
     
  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
ARAP2 5813 3.47e-06 0.0313 0.7308
KIF13B 5781 3.595e-06 0.0313 0.7306
PBK 5782 5.056e-06 0.0313 0.7269
EBF1 2212 8.146e-06 0.0313 0.7219
RHOBTB2 4978 1.301e-05 0.0313 0.7272
PURG 5684 1.598e-05 0.0313 0.7146
WRN 5684 1.598e-05 0.0313 0.7146
SMAD4 5567 1.654e-05 0.0313 0.7146
SOCS6 5417 1.715e-05 0.0313 0.7165
PSORS1C1__2 2284 1.875e-05 0.0313 0.7128
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 134
  MALE 158
     
  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
GPX1 2871 7.391e-27 1.47e-22 0.8644
KIF4B 3261 2.262e-24 2.24e-20 0.846
POLDIP3 4142 3.193e-19 1.58e-15 0.8044
RNU12 4142 3.193e-19 1.58e-15 0.8044
MIR220B 16254 3.207e-15 1.06e-11 0.7677
TUBB4 16254 3.207e-15 1.06e-11 0.7677
PSRC1 5443 8.529e-13 2.42e-09 0.7429
FDPS 15685 1.33e-12 2.93e-09 0.7408
RUSC1 15685 1.33e-12 2.93e-09 0.7408
PAFAH1B2 5669 8.028e-12 1.59e-08 0.7322
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 235
  YES 5
     
  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
  COLON ADENOCARCINOMA 251
  COLON MUCINOUS ADENOCARCINOMA 38
     
  Significant markers N = 30
  Higher in COLON MUCINOUS ADENOCARCINOMA 30
  Higher in COLON ADENOCARCINOMA 0
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'

W(pos if higher in 'COLON MUCINOUS ADENOCARCINOMA') wilcoxontestP Q AUC
POFUT1 7732 6.807e-10 1.35e-05 0.8107
POLR1D 7534 8.506e-09 4.73e-05 0.7899
BCL2L1 7493 1.406e-08 4.73e-05 0.7856
EPN2 2058 1.646e-08 4.73e-05 0.7842
REG4 2069 1.88e-08 4.73e-05 0.7831
TMEM150B 7464 1.997e-08 4.73e-05 0.7826
EIF6 7458 2.146e-08 4.73e-05 0.7819
FAM83C__1 7458 2.146e-08 4.73e-05 0.7819
TJAP1 7458 2.146e-08 4.73e-05 0.7819
PDE4B 2149 4.867e-08 8.21e-05 0.7747
Clinical variable #10: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 190
  R1 2
  R2 5
  RX 24
     
  Significant markers N = 0
Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.01 (4.5)
  Significant markers N = 30
  pos. correlated 26
  neg. correlated 4
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

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

SpearmanCorr corrP Q
CASP1__1 0.3712 3.788e-10 7.51e-06
UBE2L6 0.3356 1.895e-08 0.000188
APOL1 0.3208 8.346e-08 0.000491
IL12RB1 0.319 9.906e-08 0.000491
CSGALNACT1 0.2983 6.892e-07 0.00273
AMOTL2 -0.2864 1.96e-06 0.00559
PWP2 0.2863 1.974e-06 0.00559
ACTA2__1 0.2806 3.208e-06 0.00707
FAS 0.2806 3.208e-06 0.00707
PKN2 0.2779 4.006e-06 0.00784
Clinical variable #12: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 11
  BLACK OR AFRICAN AMERICAN 57
  WHITE 205
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
SMARCC2 2.301e-18 4.56e-14
LOC100133161 1.138e-12 1.13e-08
GSTCD__1 2.447e-12 1.21e-08
INTS12__1 2.447e-12 1.21e-08
UBTF 1.654e-11 5.98e-08
DHRS7 1.81e-11 5.98e-08
PLA2G4C 9.859e-11 2.79e-07
FAM115C 4.198e-10 1.04e-06
UNG 5.555e-10 1.22e-06
ZFP62 8.774e-10 1.74e-06
Clinical variable #13: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 4
  NOT HISPANIC OR LATINO 258
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 292

  • Number of genes = 19824

  • 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, 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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