Correlation between gene methylation status and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1J965F9
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/C1J965F9
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 19418 genes and 11 clinical features across 363 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • CCNI2 ,  CDCA7 ,  STK32C ,  WTIP ,  CNIH3 ,  ...

  • 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • SDSL ,  POLR2D ,  RPS10 ,  LOC541471 ,  PPP1R15A ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • YWHAG ,  PPP1R15A ,  LOC647979 ,  CCDC159__1 ,  TMEM205__1 ,  ...

  • 30 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  FAM35A ,  GLUD1__1 ,  C17ORF73 ,  ...

  • 30 genes correlated to 'COMPLETENESS_OF_RESECTION'.

    • GLB1 ,  TMPPE ,  OXSR1 ,  USP15 ,  ANKRD13C ,  ...

  • 30 genes correlated to 'RACE'.

    • SNX16 ,  FAM63B ,  IL6 ,  CXXC4 ,  BBS12 ,  ...

  • 30 genes correlated to 'ETHNICITY'.

    • C12ORF48__1 ,  NUP37 ,  PCIF1 ,  FARSA ,  OCEL1 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', and 'HISTOLOGICAL_TYPE'.

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
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=10 lower stage N=20
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
COMPLETENESS_OF_RESECTION Kruskal-Wallis test N=30        
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) 0-120.8 (median=19.3)
  censored N = 252
  death N = 110
     
  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) 59.62 (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
CCNI2 0.4068 8.757e-16 1.1e-11
CDCA7 0.4054 1.134e-15 1.1e-11
STK32C 0.3713 3.265e-13 2.11e-09
WTIP 0.361 1.605e-12 7.79e-09
CNIH3 0.3571 2.864e-12 1.11e-08
GFPT2 0.3542 4.438e-12 1.44e-08
ACRBP 0.3505 7.604e-12 2.04e-08
SIX2 0.3498 8.404e-12 2.04e-08
N4BP3 0.3489 9.593e-12 2.07e-08
AFAP1 0.3419 2.626e-11 3.93e-08
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 169
  STAGE II 84
  STAGE III 3
  STAGE IIIA 62
  STAGE IIIB 8
  STAGE IIIC 9
  STAGE IV 3
  STAGE IVA 1
  STAGE IVB 2
     
  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
SDSL 3.085e-05 0.151
POLR2D 3.095e-05 0.151
RPS10 3.863e-05 0.151
LOC541471 5.672e-05 0.151
PPP1R15A 5.787e-05 0.151
TTC21B 6.82e-05 0.151
C7ORF28B 8.316e-05 0.151
GLS 8.397e-05 0.151
BAZ2B 8.761e-05 0.151
ZNF669 0.000108 0.151
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) 1.78 (0.91)
  N
  T0 1
  T1 179
  T2 91
  T3 77
  T4 13
     
  Significant markers N = 30
  pos. correlated 10
  neg. correlated 20
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
YWHAG -0.3007 5.596e-09 0.000109
PPP1R15A -0.2878 2.574e-08 0.000119
LOC647979 -0.2851 3.704e-08 0.000119
CCDC159__1 0.2839 4.043e-08 0.000119
TMEM205__1 0.2839 4.043e-08 0.000119
TTC21B -0.2871 4.164e-08 0.000119
SDSL 0.2834 4.296e-08 0.000119
ZNF554 0.2799 6.375e-08 0.00014
BAZ2B -0.2809 6.487e-08 0.00014
BUB1 -0.2773 8.827e-08 0.000171
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Labels N
  N0 252
  N1 4
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 266
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 116
  MALE 247
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S11.  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 27554 1.078e-45 1.05e-41 0.9617
UTP14C 27554 1.078e-45 1.05e-41 0.9617
FAM35A 6363 2.751e-17 1.34e-13 0.776
GLUD1__1 6363 2.751e-17 1.34e-13 0.776
C17ORF73 6586 1.025e-16 3.98e-13 0.7701
CUX2 7346 7.066e-14 2.29e-10 0.7436
FAM83A 7437 1.479e-13 3.59e-10 0.7404
LOC100131726 7437 1.479e-13 3.59e-10 0.7404
CNOT4 7488 2.229e-13 4.81e-10 0.7387
ALDH3A1 7799 2.546e-12 4.94e-09 0.7278
Clinical variable #8: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  FIBROLAMELLAR CARCINOMA 2
  HEPATOCELLULAR CARCINOMA 354
  HEPATOCHOLANGIOCARCINOMA (MIXED) 7
     
  Significant markers N = 0
Clinical variable #9: 'COMPLETENESS_OF_RESECTION'

30 genes related to 'COMPLETENESS_OF_RESECTION'.

Table S13.  Basic characteristics of clinical feature: 'COMPLETENESS_OF_RESECTION'

COMPLETENESS_OF_RESECTION Labels N
  R0 320
  R1 15
  R2 1
  RX 20
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'COMPLETENESS_OF_RESECTION'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS_OF_RESECTION'

kruskal_wallis_P Q
GLB1 3.385e-06 0.0329
TMPPE 3.385e-06 0.0329
OXSR1 1.759e-05 0.082
USP15 2.02e-05 0.082
ANKRD13C 3.116e-05 0.082
HHLA3 3.116e-05 0.082
CEP97 3.241e-05 0.082
NRD1 3.38e-05 0.082
SMARCC1 4.058e-05 0.0829
MYNN 4.684e-05 0.0829
Clinical variable #10: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 159
  BLACK OR AFRICAN AMERICAN 17
  WHITE 176
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

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

kruskal_wallis_P Q
SNX16 1.623e-28 3.15e-24
FAM63B 1.114e-25 1.08e-21
IL6 2.184e-23 1.41e-19
CXXC4 6.494e-23 3.15e-19
BBS12 1.531e-22 4.96e-19
LOC729338 1.531e-22 4.96e-19
ILF3__1 5.164e-21 1.25e-17
LOC147727 5.164e-21 1.25e-17
GMFB 9.583e-21 2.07e-17
OTUD4 1.922e-20 3.73e-17
Clinical variable #11: 'ETHNICITY'

30 genes related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 334
     
  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 = LIHC-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 363

  • Number of genes = 19418

  • Number of clinical features = 11

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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