Correlation between gene methylation status and clinical features
Testicular Germ Cell Tumors (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C16972PG
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/C16972PG
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 20645 genes and 8 clinical features across 133 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • PTPRD ,  MOCS1 ,  SCG3 ,  DCLK1 ,  HS6ST3 ,  ...

  • 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • HAS1 ,  ELMO1 ,  NLGN1 ,  PDE3A ,  ADAMTS20 ,  ...

  • 2 genes correlated to 'PATHOLOGY_T_STAGE'.

    • LOC286016 ,  TNPO3

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • CLDN20__1 ,  SNRK ,  TFB1M__1 ,  LRP2BP ,  ATXN7L3 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_N_STAGE', 'RACE', 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=29 younger N=1
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=2 higher stage N=0 lower stage N=2
PATHOLOGY_N_STAGE Spearman correlation test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
RACE Kruskal-Wallis test   N=0        
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) 0.1-229.2 (median=38)
  censored N = 129
  death N = 3
     
  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) 31.96 (9.3)
  Significant markers N = 30
  pos. correlated 29
  neg. correlated 1
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
PTPRD 0.3918 3.108e-06 0.00919
MOCS1 0.3845 4.907e-06 0.00919
SCG3 0.3772 7.627e-06 0.00919
DCLK1 0.3752 8.573e-06 0.00919
HS6ST3 0.3732 9.651e-06 0.00919
GATA6 0.3723 1.022e-05 0.00919
PCDHA1__5 0.3699 1.172e-05 0.00919
PCDHA10__5 0.3699 1.172e-05 0.00919
PCDHA11__5 0.3699 1.172e-05 0.00919
PCDHA12__5 0.3699 1.172e-05 0.00919
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 19
  STAGE IA 25
  STAGE IB 11
  STAGE II 5
  STAGE IIA 6
  STAGE IIB 1
  STAGE IIC 1
  STAGE III 2
  STAGE IIIA 1
  STAGE IIIB 6
  STAGE IIIC 5
  STAGE IS 46
     
  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
HAS1 3.547e-05 0.0833
ELMO1 4.748e-05 0.0833
NLGN1 5.335e-05 0.0833
PDE3A 7.31e-05 0.0833
ADAMTS20 7.913e-05 0.0833
PLXDC2 7.994e-05 0.0833
NEDD4L 9.281e-05 0.0833
SMAD6 9.849e-05 0.0833
BASP1__1 0.0001004 0.0833
LOC285696__1 0.0001004 0.0833
Clinical variable #4: 'PATHOLOGY_T_STAGE'

2 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.48 (0.59)
  N
  T1 75
  T2 51
  T3 6
     
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S7.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
LOC286016 -0.3682 1.399e-05 0.144
TNPO3 -0.3682 1.399e-05 0.144
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 Mean (SD) 0.25 (0.51)
  N
  N0 46
  N1 11
  N2 2
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 114
  class1 4
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
CLDN20__1 446 0.001219 0.0935 0.9781
SNRK 446 0.001219 0.0935 0.9781
TFB1M__1 446 0.001219 0.0935 0.9781
LRP2BP 444 0.001352 0.0935 0.9737
ATXN7L3 443 0.001424 0.0935 0.9715
ZNF302 443 0.001424 0.0935 0.9715
ATP7B__1 441 0.001577 0.0935 0.9671
GLDC 441 0.001577 0.0935 0.9671
MCPH1 441 0.001577 0.0935 0.9671
NCRNA00174 441 0.001577 0.0935 0.9671
Clinical variable #7: 'RACE'

No gene related to 'RACE'.

Table S11.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 4
  BLACK OR AFRICAN AMERICAN 6
  WHITE 118
     
  Significant markers N = 0
Clinical variable #8: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 11
  NOT HISPANIC OR LATINO 111
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 133

  • Number of genes = 20645

  • Number of clinical features = 8

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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