Correlation between gene methylation status and clinical features
Adrenocortical Carcinoma (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/C10G3JHC
Overview
Introduction

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

Summary

Testing the association between 16497 genes and 11 clinical features across 80 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • DEXI ,  ENPP7 ,  TFDP2 ,  MPPED2 ,  MTMR7 ,  ...

  • 3 genes correlated to 'YEARS_TO_BIRTH'.

    • RRM2B ,  FAM161A ,  EPS8L1

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • SOX9 ,  MALL ,  EN1 ,  C1ORF162 ,  SSC5D ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • SOX9 ,  SSC5D ,  ACER3 ,  APOBEC3G ,  DLL4 ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  KIF4B ,  B3GNT1 ,  CYFIP2 ,  DPYSL2 ,  ...

  • No genes correlated to 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', 'NUMBER_OF_LYMPH_NODES', 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=3 older N=1 younger N=2
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=18 lower stage N=12
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test   N=0        
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) 4.1-153.6 (median=39.2)
  censored N = 50
  death N = 29
     
  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
DEXI 2.52e-09 3.3e-05 0.769
ENPP7 4.04e-09 3.3e-05 0.8
TFDP2 3.18e-08 0.00014 0.748
MPPED2 3.36e-08 0.00014 0.812
MTMR7 7.48e-08 0.00022 0.799
FOXJ2 8.03e-08 0.00022 0.702
PYY 1.39e-07 0.00025 0.782
MEGF11 1.64e-07 0.00025 0.736
LOC646471 1.64e-07 0.00025 0.728
ZBTB4 1.72e-07 0.00025 0.761
Clinical variable #2: 'YEARS_TO_BIRTH'

3 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 46.4 (16)
  Significant markers N = 3
  pos. correlated 1
  neg. correlated 2
List of 3 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of 3 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
RRM2B -0.4708 1.047e-05 0.173
FAM161A -0.4526 2.499e-05 0.175
EPS8L1 0.4473 3.186e-05 0.175
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 9
  STAGE II 37
  STAGE III 16
  STAGE IV 16
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
SOX9 5.556e-06 0.0917
MALL 0.0001091 0.256
EN1 0.0001204 0.256
C1ORF162 0.0001374 0.256
SSC5D 0.0001421 0.256
ACER3 0.0001458 0.256
NAALADL1 0.0001526 0.256
SOX18 0.0001527 0.256
REXO1 0.0001756 0.256
CCL5 0.0002216 0.256
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.46 (0.98)
  N
  T1 9
  T2 42
  T3 9
  T4 18
     
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
SOX9 0.472 1.288e-05 0.177
SSC5D -0.453 3.114e-05 0.177
ACER3 0.4468 4.114e-05 0.177
APOBEC3G 0.439 5.803e-05 0.177
DLL4 0.4278 9.356e-05 0.177
C9ORF86 -0.4255 0.000103 0.177
PSAP 0.4239 0.0001099 0.177
GEMIN7 -0.4224 0.0001172 0.177
FUT2 -0.4221 0.0001186 0.177
SHANK3 0.4221 0.0001186 0.177
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 68
  N1 10
     
  Significant markers N = 0
Clinical variable #6: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 49
  MALE 31
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S11.  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
UTP14C 1420 7.126e-11 1.18e-06 0.9348
KIF4B 242 3.295e-07 0.00272 0.8407
B3GNT1 1199 1.455e-05 0.0743 0.7893
CYFIP2 1191 2.077e-05 0.0743 0.7841
DPYSL2 1185 2.703e-05 0.0743 0.7801
RAB12 334 2.703e-05 0.0743 0.7801
PLIN2 1181 3.215e-05 0.0758 0.7775
LAMC3 1173 4.529e-05 0.0923 0.7722
SLC25A39 1170 5.143e-05 0.0923 0.7702
AOC3 1168 5.594e-05 0.0923 0.7689
Clinical variable #7: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 60
  YES 17
     
  Significant markers N = 0
Clinical variable #8: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  ADRENOCORTICAL CARCINOMA- MYXOID TYPE 1
  ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE 3
  ADRENOCORTICAL CARCINOMA- USUAL TYPE 76
     
  Significant markers N = 0
Clinical variable #9: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 55
  R1 6
  R2 10
  RX 6
     
  Significant markers N = 0
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

No gene related to 'NUMBER_OF_LYMPH_NODES'.

Table S15.  Basic characteristics of clinical feature: 'NUMBER_OF_LYMPH_NODES'

NUMBER_OF_LYMPH_NODES Mean (SD) 2.82 (9.9)
  Significant markers N = 0
Clinical variable #11: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S16.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 8
  NOT HISPANIC OR LATINO 30
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = ACC-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 80

  • Number of genes = 16497

  • 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)