Correlation between gene methylation status and clinical features
Adrenocortical Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C14X56X3
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/C14X56X3
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 19519 genes and 11 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.

  • 3 genes correlated to 'YEARS_TO_BIRTH'.

    • RRM2B ,  FAM161A ,  EPS8L1

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • SOX9 ,  MALL ,  EN1 ,  C1ORF162 ,  NAT14 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • SOX9 ,  NAT14 ,  SSC5D ,  ACER3 ,  APOBEC3G ,  ...

  • 30 genes correlated to 'GENDER'.

    • ALG11__2 ,  UTP14C ,  KIF4B ,  B3GNT1__1 ,  CYFIP2 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', '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=0        
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=17 lower stage N=13
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'

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) 4.1-153.6 (median=38.5)
  censored N = 51
  death N = 28
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

3 genes related to 'YEARS_TO_BIRTH'.

Table S2.  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 S3.  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 0.204
FAM161A -0.4526 2.499e-05 0.207
EPS8L1 0.4473 3.186e-05 0.207
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

Table S4.  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 S5.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
SOX9 5.556e-06 0.108
MALL 0.0001091 0.251
EN1 0.0001204 0.251
C1ORF162 0.0001374 0.251
NAT14 0.0001421 0.251
SSC5D 0.0001421 0.251
ACER3 0.0001458 0.251
NAALADL1 0.0001526 0.251
SOX18 0.0001527 0.251
REXO1 0.0001756 0.251
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) 2.46 (0.98)
  N
  T1 9
  T2 42
  T3 9
  T4 18
     
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
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
SOX9 0.472 1.288e-05 0.176
NAT14 -0.453 3.114e-05 0.176
SSC5D -0.453 3.114e-05 0.176
ACER3 0.4468 4.114e-05 0.176
APOBEC3G 0.439 5.803e-05 0.176
DLL4 0.4278 9.356e-05 0.176
C9ORF86__2 -0.4255 0.000103 0.176
LOC100131193__2 -0.4255 0.000103 0.176
PSAP 0.4239 0.0001099 0.176
GEMIN7 -0.4224 0.0001172 0.176
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 Labels N
  N0 68
  N1 10
     
  Significant markers N = 0
Clinical variable #6: 'GENDER'

30 genes related to 'GENDER'.

Table S9.  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 S10.  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
ALG11__2 1420 7.126e-11 6.95e-07 0.9348
UTP14C 1420 7.126e-11 6.95e-07 0.9348
KIF4B 242 3.295e-07 0.00214 0.8407
B3GNT1__1 1199 1.455e-05 0.071 0.7893
CYFIP2 1191 2.077e-05 0.0754 0.7841
DPYSL2 1185 2.703e-05 0.0754 0.7801
RAB12 334 2.703e-05 0.0754 0.7801
PLIN2 1181 3.215e-05 0.0784 0.7775
LAMC3 1173 4.529e-05 0.0982 0.7722
SLC25A39 1170 5.143e-05 0.0993 0.7702
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 60
  YES 17
     
  Significant markers N = 0
Clinical variable #8: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

Table S12.  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 S13.  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 S14.  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 S15.  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 = 19519

  • 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, 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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