Correlation between gene methylation status and clinical features
Thymoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C10864TH
Overview
Introduction

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

Summary

Testing the association between 17098 genes and 8 clinical features across 124 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • ANKRD24 ,  WIPF3 ,  FAM195A ,  TRIM14 ,  SLC25A22 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • VRK2 ,  TMEM62 ,  ABCC4 ,  JRKL ,  FAM3C ,  ...

  • 29 genes correlated to 'GENDER'.

    • TFDP1 ,  KIF4B ,  ZBTB20 ,  RAD21 ,  WBP11P1 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • MLC1 ,  FRMPD2 ,  IFI30 ,  SOCS1 ,  KRT80 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • PI16 ,  ZNF721 ,  C17ORF58 ,  MS4A2 ,  KCNMB2 ,  ...

  • 3 genes correlated to 'RACE'.

    • LOC401010 ,  GRAPL ,  PM20D1

  • No genes correlated to 'TUMOR_TISSUE_SITE', 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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=0 younger N=30
TUMOR_TISSUE_SITE Wilcoxon test   N=0        
GENDER Wilcoxon test N=29 male N=29 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RACE Kruskal-Wallis test N=3        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 genes 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) 0.5-150.4 (median=40.6)
  censored N = 115
  death N = 8
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
ANKRD24 3.7e-06 0.044 0.354
WIPF3 6.53e-06 0.044 0.073
FAM195A 7.7e-06 0.044 0.088
TRIM14 1.2e-05 0.051 0.078
SLC25A22 1.79e-05 0.055 0.111
JUN 1.94e-05 0.055 0.135
GADD45GIP1 4.17e-05 0.086 0.185
PDZRN3 4.34e-05 0.086 0.135
FAM106A 5e-05 0.086 0.32
SLC5A10 5.03e-05 0.086 0.161
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

Table S3.  Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH Mean (SD) 58.15 (13)
  Significant markers N = 30
  pos. correlated 0
  neg. correlated 30
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
VRK2 -0.4898 8.945e-09 9.32e-05
TMEM62 -0.4853 1.281e-08 9.32e-05
ABCC4 -0.4822 1.635e-08 9.32e-05
JRKL -0.4677 4.913e-08 0.00021
FAM3C -0.4556 1.189e-07 0.000238
ARL9 -0.4548 1.261e-07 0.000238
ADAM11 -0.4544 1.296e-07 0.000238
GHDC -0.4536 1.368e-07 0.000238
UTP23 -0.4536 1.374e-07 0.000238
RBM9 -0.4534 1.393e-07 0.000238
Clinical variable #3: 'TUMOR_TISSUE_SITE'

No gene related to 'TUMOR_TISSUE_SITE'.

Table S5.  Basic characteristics of clinical feature: 'TUMOR_TISSUE_SITE'

TUMOR_TISSUE_SITE Labels N
  ANTERIOR MEDIASTINUM 27
  THYMUS 97
     
  Significant markers N = 0
Clinical variable #4: 'GENDER'

29 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 60
  MALE 64
     
  Significant markers N = 29
  Higher in MALE 29
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S7.  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
TFDP1 79 3.497e-20 5.98e-16 0.9794
KIF4B 102 1.013e-19 8.66e-16 0.9734
ZBTB20 3701 5.46e-19 3.11e-15 0.9638
RAD21 3568 1.758e-16 7.52e-13 0.9292
WBP11P1 3494 3.618e-15 1.24e-11 0.9099
TUBB8 568 1.404e-11 4e-08 0.8521
FRG1B 701 1.112e-09 2.72e-06 0.8174
YARS2 710 1.471e-09 3.14e-06 0.8151
GLUD1 830 5.108e-08 9.7e-05 0.7839
AMDHD2 837 6.215e-08 0.000106 0.782
Clinical variable #5: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 81
  YES 43
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
MLC1 2583 1.009e-05 0.0768 0.7416
FRMPD2 922 1.71e-05 0.0768 0.7353
IFI30 932 2.164e-05 0.0768 0.7324
SOCS1 946 2.996e-05 0.0768 0.7284
KRT80 957 3.855e-05 0.0768 0.7252
EP400 2516 4.834e-05 0.0768 0.7224
NETO1 2516 4.834e-05 0.0768 0.7224
SEPT5 2515 4.944e-05 0.0768 0.7221
PALMD 972 5.408e-05 0.0768 0.7209
PLXNA4 977 6.046e-05 0.0768 0.7195
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  THYMOMA; TYPE A 17
  THYMOMA; TYPE AB 38
  THYMOMA; TYPE B1 15
  THYMOMA; TYPE B2 31
  THYMOMA; TYPE B3 12
  THYMOMA; TYPE C 11
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
PI16 4.251e-15 1.76e-11
ZNF721 7.283e-15 1.76e-11
C17ORF58 7.395e-15 1.76e-11
MS4A2 1.026e-14 1.76e-11
KCNMB2 1.271e-14 1.76e-11
MFHAS1 1.287e-14 1.76e-11
BIRC2 1.31e-14 1.76e-11
C17ORF63 1.412e-14 1.76e-11
UTS2D 1.618e-14 1.76e-11
NAPA 1.64e-14 1.76e-11
Clinical variable #7: 'RACE'

3 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 13
  BLACK OR AFRICAN AMERICAN 6
  WHITE 103
     
  Significant markers N = 3
List of 3 genes differentially expressed by 'RACE'

Table S13.  Get Full Table List of 3 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
LOC401010 3.301e-06 0.0564
GRAPL 1.097e-05 0.0867
PM20D1 1.522e-05 0.0867
Clinical variable #8: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 10
  NOT HISPANIC OR LATINO 100
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = THYM-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 124

  • Number of genes = 17098

  • Number of clinical features = 8

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)