Correlation between gene methylation status and clinical features
Uveal Melanoma (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/C1FQ9W49
Overview
Introduction

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

Summary

Testing the association between 16206 genes and 7 clinical features across 80 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 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • NF1 ,  LBXCOR1 ,  LRRC27 ,  CCDC54 ,  SGSH ,  ...

  • 1 gene correlated to 'YEARS_TO_BIRTH'.

    • MDGA2

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • ACTN4 ,  LGALS3BP ,  PSMG3 ,  PPAPDC1B ,  SRA1 ,  ...

  • 10 genes correlated to 'GENDER'.

    • DKFZP434L187 ,  CROCC ,  GABPA ,  GLUD1 ,  SFRS6 ,  ...

  • No genes correlated to 'PATHOLOGIC_STAGE', 'PATHOLOGY_M_STAGE', and 'RADIATION_THERAPY'.

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=1 older N=0 younger N=1
PATHOLOGIC_STAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=0 lower stage N=30
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=10 male N=10 female N=0
RADIATION_THERAPY 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) 0.1-85.5 (median=25.4)
  censored N = 56
  death N = 23
     
  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
NF1 1.2e-10 9.6e-07 0.163
LBXCOR1 2.08e-10 9.6e-07 0.246
LRRC27 2.15e-10 9.6e-07 0.839
CCDC54 2.37e-10 9.6e-07 0.185
SGSH 3.79e-10 1.2e-06 0.262
RPS20 7.35e-10 1.8e-06 0.24
C1QC 8.09e-10 1.8e-06 0.186
TMEM132B 9.03e-10 1.8e-06 0.158
ARHGEF11 1.22e-09 2.2e-06 0.273
ELL2 2.65e-09 4.3e-06 0.839
Clinical variable #2: 'YEARS_TO_BIRTH'

One gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 61.65 (14)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of one gene significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
MDGA2 -0.4645 1.422e-05 0.23
Clinical variable #3: 'PATHOLOGIC_STAGE'

No gene related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE IIA 12
  STAGE IIB 27
  STAGE IIIA 25
  STAGE IIIB 10
  STAGE IIIC 1
  STAGE IV 4
     
  Significant markers N = 0
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) 3.25 (0.74)
  N
  T2 14
  T3 32
  T4 34
     
  Significant markers N = 30
  pos. correlated 0
  neg. correlated 30
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
ACTN4 -0.4844 5.305e-06 0.0767
LGALS3BP -0.4728 9.47e-06 0.0767
PSMG3 -0.463 1.529e-05 0.0826
PPAPDC1B -0.456 2.128e-05 0.0862
SRA1 -0.4389 4.655e-05 0.0964
PIM3 -0.4373 4.991e-05 0.0964
APBB1IP -0.4369 5.076e-05 0.0964
ZFP36 -0.4352 5.483e-05 0.0964
TMEM165 -0.4335 5.893e-05 0.0964
NEK6 -0.4327 6.119e-05 0.0964
Clinical variable #5: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 51
  class1 4
     
  Significant markers N = 0
Clinical variable #6: 'GENDER'

10 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 35
  MALE 45
     
  Significant markers N = 10
  Higher in MALE 10
  Higher in FEMALE 0
List of 10 genes differentially expressed by 'GENDER'

Table S10.  Get Full Table List of 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
DKFZP434L187 1451 1.275e-10 2.07e-06 0.9213
CROCC 273 6.194e-07 0.00502 0.8267
GABPA 1279 1.917e-06 0.0104 0.8121
GLUD1 337 1.275e-05 0.0517 0.786
SFRS6 354 2.675e-05 0.0867 0.7752
TOR2A 382 8.568e-05 0.207 0.7575
PPHLN1 383 8.92e-05 0.207 0.7568
C5ORF23 387 0.0001047 0.212 0.7543
AMDHD2 398 0.0001615 0.272 0.7473
LOC389791 1176 0.0001679 0.272 0.7467
Clinical variable #7: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 76
  YES 3
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = UVM-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 80

  • Number of genes = 16206

  • Number of clinical features = 7

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)