Correlation between gene methylation status and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1BP021B
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/C1BP021B
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 12 clinical features across 368 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'.

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

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • PPP1R15A ,  SDSL ,  RPS10 ,  POLR2D ,  BAZ2B ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • YWHAG ,  PPP1R15A ,  LOC647979 ,  BAZ2B ,  SDSL ,  ...

  • 30 genes correlated to 'GENDER'.

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

  • 30 genes correlated to 'RESIDUAL_TUMOR'.

    • GLB1 ,  TMPPE ,  MYNN ,  CEP97 ,  OXSR1 ,  ...

  • 30 genes correlated to 'RACE'.

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

  • 30 genes correlated to 'ETHNICITY'.

    • FARSA ,  C12ORF48__1 ,  NUP37 ,  MAST4 ,  MALT1 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', 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=26 younger N=4
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=9 lower stage N=21
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
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
RESIDUAL_TUMOR 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=19.8)
  censored N = 236
  death N = 131
     
  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) 59.59 (13)
  Significant markers N = 30
  pos. correlated 26
  neg. correlated 4
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
CDCA7 0.4125 1.997e-16 3.57e-12
CCNI2 0.4091 3.676e-16 3.57e-12
STK32C 0.373 1.71e-13 1.11e-09
WTIP 0.3626 8.804e-13 4.27e-09
CNIH3 0.3604 1.239e-12 4.81e-09
GFPT2 0.359 1.535e-12 4.97e-09
SIX2 0.3553 2.686e-12 7.45e-09
ACRBP 0.354 3.231e-12 7.84e-09
N4BP3 0.3521 4.331e-12 9.34e-09
AFAP1 0.3461 1.045e-11 2.03e-08
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 172
  STAGE II 85
  STAGE III 3
  STAGE IIIA 63
  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 'PATHOLOGIC_STAGE'

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

kruskal_wallis_P Q
PPP1R15A 3.118e-05 0.171
SDSL 3.905e-05 0.171
RPS10 4.951e-05 0.171
POLR2D 4.955e-05 0.171
BAZ2B 5.336e-05 0.171
YWHAG 6.175e-05 0.171
GLS 7e-05 0.171
C7ORF28B 7.306e-05 0.171
LOC541471 0.0001054 0.171
TTC21B 0.0001066 0.171
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 (0.9)
  N
  T0 1
  T1 182
  T2 92
  T3 78
  T4 13
     
  Significant markers N = 30
  pos. correlated 9
  neg. correlated 21
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 -0.3057 2.348e-09 4.56e-05
PPP1R15A -0.2936 1.038e-08 0.000101
LOC647979 -0.2869 2.401e-08 0.000155
BAZ2B -0.2849 3.313e-08 0.000161
SDSL 0.2778 6.555e-08 0.000196
CCDC159__1 0.2771 7.061e-08 0.000196
TMEM205__1 0.2771 7.061e-08 0.000196
TTC21B -0.278 9.317e-08 0.000213
ZNF554 0.2735 1.056e-07 0.000213
ZFR -0.2729 1.13e-07 0.000213
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 253
  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 267
  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 118
  MALE 250
     
  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 28292 7.102e-46 6.9e-42 0.9591
UTP14C 28292 7.102e-46 6.9e-42 0.9591
FAM35A 6698 5.8e-17 2.82e-13 0.771
GLUD1__1 6698 5.8e-17 2.82e-13 0.771
C17ORF73 6830 9.172e-17 3.56e-13 0.7685
CUX2 7520 3.185e-14 1.03e-10 0.7451
CNOT4 7701 1.356e-13 3.76e-10 0.7389
FAM83A 7869 5.043e-13 1.09e-09 0.7333
LOC100131726 7869 5.043e-13 1.09e-09 0.7333
ALDH3A1 8106 3.052e-12 5.93e-09 0.7252
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 342
  YES 7
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  FIBROLAMELLAR CARCINOMA 2
  HEPATOCELLULAR CARCINOMA 359
  HEPATOCHOLANGIOCARCINOMA (MIXED) 7
     
  Significant markers N = 0
Clinical variable #10: 'RESIDUAL_TUMOR'

30 genes related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 323
  R1 15
  R2 1
  RX 22
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

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

kruskal_wallis_P Q
GLB1 1.697e-05 0.0908
TMPPE 1.697e-05 0.0908
MYNN 1.715e-05 0.0908
CEP97 1.87e-05 0.0908
OXSR1 3.406e-05 0.0921
ANKRD28 4.558e-05 0.0921
TTC21B 4.986e-05 0.0921
RARB 5.558e-05 0.0921
USP15 6.333e-05 0.0921
SLC35B3 7.547e-05 0.0921
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 160
  BLACK OR AFRICAN AMERICAN 17
  WHITE 180
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
SNX16 8.124e-29 1.58e-24
FAM63B 7.97e-26 7.74e-22
IL6 1.304e-23 8.44e-20
CXXC4 4.891e-23 2.37e-19
BBS12 1.254e-22 4.06e-19
LOC729338 1.254e-22 4.06e-19
ILF3__1 1.588e-21 3.85e-18
LOC147727 1.588e-21 3.85e-18
GMFB 6.638e-21 1.43e-17
OTUD4 1.24e-20 2.41e-17
Clinical variable #12: 'ETHNICITY'

30 genes related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 14
  NOT HISPANIC OR LATINO 337
     
  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 = 368

  • Number of genes = 19418

  • 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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