Correlation between gene methylation status and clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C10R9NG6
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/C10R9NG6
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 13407 genes and 4 clinical features across 194 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • TMEM20 ,  IGFBP7 ,  BTBD3 ,  CAMK4 ,  CBLN3 ,  ...

  • 21 genes correlated to 'GENDER'.

    • DECR1 ,  NRXN2 ,  FYTTD1 ,  KIAA0226 ,  FAM190A ,  ...

  • 2 genes correlated to 'RACE'.

    • TNFRSF21 ,  TNNI3

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'

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=6 younger N=24
GENDER Wilcoxon test N=21 male N=21 female N=0
RACE Kruskal-Wallis test N=2        
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) 0-94.1 (median=12)
  censored N = 65
  death N = 128
     
  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) 55.11 (16)
  Significant markers N = 30
  pos. correlated 6
  neg. correlated 24
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
TMEM20 -0.4547 2.74e-11 3.67e-07
IGFBP7 0.4357 2.155e-10 1.44e-06
BTBD3 0.4237 7.463e-10 3.34e-06
CAMK4 -0.3929 1.46e-08 3.9e-05
CBLN3 -0.3902 1.857e-08 3.9e-05
KHNYN -0.3902 1.857e-08 3.9e-05
JAKMIP1 -0.3892 2.037e-08 3.9e-05
IRS2 0.3831 3.507e-08 5.88e-05
SASH1 -0.3671 1.405e-07 0.000209
FRMD5 -0.3646 1.723e-07 0.000228
Clinical variable #3: 'GENDER'

21 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 89
  MALE 105
     
  Significant markers N = 21
  Higher in MALE 21
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S5.  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
DECR1 540 2.874e-26 3.85e-22 0.9422
NRXN2 1346 1.401e-17 9.39e-14 0.856
FYTTD1 1689 1.935e-14 6.49e-11 0.8193
KIAA0226 1689 1.935e-14 6.49e-11 0.8193
FAM190A 2042 1.489e-11 2.9e-08 0.7815
TMSL3 2042 1.489e-11 2.9e-08 0.7815
VAMP2 2043 1.515e-11 2.9e-08 0.7814
CASP2 2163 1.207e-10 2.02e-07 0.7685
LRRC2 6936 6.352e-09 8.52e-06 0.7422
TDGF1 6936 6.352e-09 8.52e-06 0.7422
Clinical variable #4: 'RACE'

2 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 13
  WHITE 177
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'RACE'

Table S7.  Get Full Table List of 2 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
TNFRSF21 6.333e-06 0.0556
TNNI3 8.289e-06 0.0556
Methods & Data
Input

  • Expresson data file = LAML-TB.meth.by_min_clin_corr.data.txt

  • Clinical data file = LAML-TB.merged_data.txt

  • Number of patients = 194

  • Number of genes = 13407

  • Number of clinical features = 4

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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