Correlation between gene methylation status and clinical features
Colon Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C16W99FV
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/C16W99FV
Overview
Introduction

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

Summary

Testing the association between 16770 genes and 13 clinical features across 293 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 ,  SHISA3 ,  SCGB3A1 ,  CRHR2 ,  CBS ,  ...

  • 1 gene correlated to 'PATHOLOGIC_STAGE'.

    • APOL1

  • 1 gene correlated to 'PATHOLOGY_T_STAGE'.

    • PHYHIP

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • CASP1 ,  APOL1 ,  UBE2L6 ,  IL12RB1 ,  ASPHD2 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • KIF13B ,  ARAP2 ,  SOCS6 ,  RHOBTB2 ,  WRN ,  ...

  • 30 genes correlated to 'GENDER'.

    • GPX1 ,  KIF4B ,  POLDIP3 ,  TUBB4 ,  FDPS ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • POFUT1 ,  EPN2 ,  POLR1D ,  REG4 ,  EDEM3 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • CASP1 ,  APOL1 ,  UBE2L6 ,  IL12RB1 ,  CSGALNACT1 ,  ...

  • 30 genes correlated to 'RACE'.

    • SMARCC2 ,  INTS12 ,  DHRS7 ,  UBTF ,  LOC100133161 ,  ...

  • 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=24 lower stage N=6
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) 0.2-148 (median=22.1)
  censored N = 223
  death N = 69
     
  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) 64.88 (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 0.3287 9.248e-09 0.000155
SHISA3 0.3116 5.679e-08 0.000476
SCGB3A1 0.3008 1.682e-07 0.000524
CRHR2 0.2999 1.852e-07 0.000524
CBS 0.2998 1.864e-07 0.000524
OTUD7A 0.2997 1.875e-07 0.000524
CORO6 0.2966 2.554e-07 0.000558
LOC389705 0.2961 2.662e-07 0.000558
C1QL1 0.294 3.272e-07 0.00061
JPH3 0.2884 5.602e-07 0.000939
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 15
  STAGE IIA 93
  STAGE IIB 6
  STAGE IIC 1
  STAGE III 6
  STAGE IIIA 5
  STAGE IIIB 47
  STAGE IIIC 26
  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
APOL1 1.178e-05 0.197
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 40
     
  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 -0.2512 1.395e-05 0.234
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) 0.58 (0.76)
  N
  N0 171
  N1 73
  N2 49
     
  Significant markers N = 30
  pos. correlated 24
  neg. correlated 6
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 0.3382 2.834e-09 4.75e-05
APOL1 0.3299 7.194e-09 6.03e-05
UBE2L6 0.2934 3.152e-07 0.00176
IL12RB1 0.2862 6.247e-07 0.00262
ASPHD2 0.2777 1.374e-06 0.00461
PKN2 0.2744 1.856e-06 0.00519
MED18 0.2661 3.87e-06 0.0082
UBA7 0.2656 4.04e-06 0.0082
FAS 0.2646 4.4e-06 0.0082
PFKFB2 0.2594 6.841e-06 0.0115
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 197
  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
KIF13B 6070 2.766e-07 0.00464 0.7554
ARAP2 5939 2.167e-06 0.016 0.7353
SOCS6 5626 3.769e-06 0.016 0.7326
RHOBTB2 5151 3.822e-06 0.016 0.7405
WRN 5873 4.816e-06 0.0162 0.7271
PBK 5828 8.179e-06 0.0229 0.7216
SMAD4 5655 1.607e-05 0.0385 0.7146
PSORS1C2 2326 1.968e-05 0.0413 0.712
PHC2 2362 2.932e-05 0.0519 0.7076
LGR6 2367 3.097e-05 0.0519 0.7069
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 135
  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 2970 1.861e-26 3.12e-22 0.8608
KIF4B 3124 1.792e-25 1.5e-21 0.8535
POLDIP3 4055 6.073e-20 3.39e-16 0.8099
TUBB4 16429 1.552e-15 6.51e-12 0.7702
FDPS 15890 4.933e-13 1.65e-09 0.745
MDH1B 6323 1.905e-09 5.33e-06 0.7036
ZNF839 6605 1.959e-08 4.69e-05 0.6903
COX7C 6906 2.002e-07 0.00042 0.6762
WBP11P1 14341 3.688e-07 0.000687 0.6723
DAZL 7066 6.429e-07 0.00108 0.6687
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 249
  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 39
     
  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 7854 1.254e-09 2.1e-05 0.8023
EPN2 2066 6.461e-09 5.2e-05 0.7889
POLR1D 7686 1.014e-08 5.2e-05 0.7852
REG4 2133 1.455e-08 5.2e-05 0.7821
EDEM3 2161 2.031e-08 5.2e-05 0.7792
BCL2L1 7622 2.18e-08 5.2e-05 0.7786
TMEM150B 7610 2.512e-08 5.2e-05 0.7774
PDE4B 2198 3.14e-08 5.2e-05 0.7755
EIF6 7589 3.215e-08 5.2e-05 0.7753
ZXDC 2200 3.215e-08 5.2e-05 0.7753
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 193
  R1 3
  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 (4.4)
  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 0.3635 7.985e-10 1.34e-05
APOL1 0.3324 2.322e-08 0.000195
UBE2L6 0.3225 6.295e-08 0.000352
IL12RB1 0.3121 1.73e-07 0.000725
CSGALNACT1 0.3025 4.246e-07 0.00142
FAS 0.2798 3.141e-06 0.00878
AMOTL2 -0.2763 4.223e-06 0.00917
PKN2 0.2759 4.375e-06 0.00917
SRBD1 0.2684 8.059e-06 0.015
MAST3 0.2655 1.016e-05 0.0169
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 58
  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 3.637e-18 6.1e-14
INTS12 9.411e-14 7.89e-10
DHRS7 9.125e-13 5.1e-09
UBTF 7.424e-12 3e-08
LOC100133161 8.959e-12 3e-08
CN5H6.4 1.543e-10 4.31e-07
UNG 2.692e-10 6.45e-07
ZFP62 3.834e-10 8.04e-07
PLA2G4C 7.027e-10 1.31e-06
FAM115C 1.428e-09 2.39e-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 261
     
  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 = 293

  • Number of genes = 16770

  • 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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