Correlation between gene methylation status and clinical features
Rectum Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1KK9B7W
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/C1KK9B7W
Overview
Introduction

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

Summary

Testing the association between 16882 genes and 11 clinical features across 98 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

  • 7 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • C4A ,  APOBEC4 ,  NECAP1 ,  SLC39A5 ,  VPS25 ,  ...

  • 9 genes correlated to 'GENDER'.

    • UTP14C ,  DDX43 ,  KIF4B ,  TUBB4 ,  PXDNL ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • WSCD1 ,  CDK8 ,  TPT1 ,  SLC5A6 ,  UGT2B10 ,  ...

  • No genes correlated to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', 'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.

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=7   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test   N=0        
PATHOLOGIC_STAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test   N=0        
PATHOLOGY_N_STAGE Spearman correlation test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=9 male N=9 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Wilcoxon test N=30 rectal mucinous adenocarcinoma N=30 rectal adenocarcinoma N=0
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

7 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) 0.4-129.3 (median=24.8)
  censored N = 79
  death N = 18
     
  Significant markers N = 7
  associated with shorter survival NA
  associated with longer survival NA
List of 7 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of 7 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
C4A 1.5e-05 0.25 0.402
APOBEC4 3.33e-05 0.25 0.723
NECAP1 4.47e-05 0.25 0.676
SLC39A5 6.32e-05 0.25 0.665
VPS25 7.52e-05 0.25 0.583
TOP2A 0.000115 0.3 0.675
OAZ1 0.000126 0.3 0.627
Clinical variable #2: 'YEARS_TO_BIRTH'

No gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 62.94 (12)
  Significant markers N = 0
Clinical variable #3: 'PATHOLOGIC_STAGE'

No gene related to 'PATHOLOGIC_STAGE'.

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGIC_STAGE'

PATHOLOGIC_STAGE Labels N
  STAGE I 11
  STAGE II 6
  STAGE IIA 20
  STAGE IIB 2
  STAGE IIC 1
  STAGE III 3
  STAGE IIIA 7
  STAGE IIIB 15
  STAGE IIIC 10
  STAGE IV 6
  STAGE IVA 7
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY_T_STAGE'

No gene related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.91 (0.63)
  N
  T1 4
  T2 12
  T3 69
  T4 11
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.79 (0.8)
  N
  N0 42
  N1 30
  N2 22
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 69
  class1 12
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

9 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 44
  MALE 54
     
  Significant markers N = 9
  Higher in MALE 9
  Higher in FEMALE 0
List of 9 genes differentially expressed by 'GENDER'

Table S9.  Get Full Table List of 9 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
UTP14C 2249 3.602e-14 6.08e-10 0.9465
DDX43 450 1.382e-07 0.00117 0.8106
KIF4B 461 2.114e-07 0.00119 0.806
TUBB4 1846 2.651e-06 0.0112 0.7769
PXDNL 616 4.466e-05 0.151 0.7407
TSC2 1747 6.633e-05 0.187 0.7353
ANGPTL1 1733 0.0001006 0.243 0.7294
ZSWIM5 1728 0.0001165 0.246 0.7273
ZDHHC16 1723 0.0001347 0.253 0.7252
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 69
  YES 11
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  RECTAL ADENOCARCINOMA 90
  RECTAL MUCINOUS ADENOCARCINOMA 6
     
  Significant markers N = 30
  Higher in RECTAL MUCINOUS ADENOCARCINOMA 30
  Higher in RECTAL ADENOCARCINOMA 0
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

W(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA') wilcoxontestP Q AUC
WSCD1 523 0.0001325 0.225 0.9685
CDK8 522 0.0001408 0.225 0.9667
TPT1 514 0.0002282 0.225 0.9519
SLC5A6 510 0.0002889 0.225 0.9444
UGT2B10 509 0.0003063 0.225 0.9426
TMEM150B 508 0.0003247 0.225 0.9407
KIAA0355 506 0.0003645 0.225 0.937
LRRC49 505 0.0003862 0.225 0.9352
POLG 35 0.0004089 0.225 0.9337
GMDS 36 0.000409 0.225 0.9333
Clinical variable #10: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 66
  R1 2
  R2 2
  RX 5
     
  Significant markers N = 0
Clinical variable #11: 'NUMBER_OF_LYMPH_NODES'

No gene related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 3.09 (5.7)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 98

  • Number of genes = 16882

  • Number of clinical features = 11

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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