Correlation between gene methylation status and clinical features
Skin Cutaneous Melanoma (Metastatic)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1QR4WF8
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/C1QR4WF8
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 19745 genes and 14 clinical features across 367 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • CMBL ,  RSPO4 ,  HIST1H1D ,  ANKRD45 ,  NFATC2 ,  ...

  • 2 genes correlated to 'PATHOLOGIC_STAGE'.

    • PTP4A2 ,  CAPN3

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • GAL3ST3 ,  FAM100B ,  C14ORF72 ,  UBE2F ,  IQCG ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • ABCA7 ,  CCNF ,  C10ORF68 ,  CCDC7__1 ,  PKP2 ,  ...

  • 30 genes correlated to 'BRESLOW_THICKNESS'.

    • HDAC7 ,  FAM100B ,  SPATS2L ,  ASF1A ,  IKBKE ,  ...

  • 3 genes correlated to 'GENDER'.

    • KIF4B ,  NICN1__1 ,  ZNF839

  • No genes correlated to 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP', 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_M_STAGE', 'MELANOMA_ULCERATION', 'MELANOMA_PRIMARY_KNOWN', 'RADIATION_THERAPY', 'RACE', 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
TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=30 younger N=0
PATHOLOGIC_STAGE Kruskal-Wallis test N=2        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=28 lower stage N=2
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=1 lower stage N=29
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
MELANOMA_ULCERATION Wilcoxon test   N=0        
MELANOMA_PRIMARY_KNOWN Wilcoxon test   N=0        
BRESLOW_THICKNESS Spearman correlation test N=30 higher breslow_thickness N=23 lower breslow_thickness N=7
GENDER Wilcoxon test N=3 male N=3 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'

No gene related to 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'.

Table S1.  Basic characteristics of clinical feature: 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'

TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP Duration (Months) 0-346.5 (median=47.1)
  censored N = 122
  death N = 124
     
  Significant markers N = 0
Clinical variable #2: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S2.  Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0.2-369.9 (median=53.2)
  censored N = 177
  death N = 189
     
  Significant markers N = 0
Clinical variable #3: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 56.32 (16)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
CMBL 0.287 3.115e-08 0.000514
RSPO4 0.2824 5.205e-08 0.000514
HIST1H1D 0.2777 8.857e-08 0.000583
ANKRD45 0.2697 2.121e-07 0.000809
NFATC2 0.2685 2.403e-07 0.000809
PDGFD 0.2669 2.84e-07 0.000809
LOC100128675 0.2669 2.868e-07 0.000809
TES 0.2641 3.816e-07 0.000942
PARP12 0.2615 5.026e-07 0.00096
ANKRD18A 0.2609 5.346e-07 0.00096
Clinical variable #4: 'PATHOLOGIC_STAGE'

2 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  I OR II NOS 13
  STAGE 0 7
  STAGE I 29
  STAGE IA 18
  STAGE IB 28
  STAGE II 25
  STAGE IIA 14
  STAGE IIB 19
  STAGE IIC 15
  STAGE III 40
  STAGE IIIA 15
  STAGE IIIB 33
  STAGE IIIC 56
  STAGE IV 21
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of 2 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
PTP4A2 8.055e-06 0.159
CAPN3 2.805e-05 0.277
Clinical variable #5: '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) 2.47 (1.2)
  N
  T0 23
  T1 41
  T2 73
  T3 78
  T4 71
     
  Significant markers N = 30
  pos. correlated 28
  neg. correlated 2
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
GAL3ST3 -0.2853 9.277e-07 0.0183
FAM100B 0.2668 4.734e-06 0.0291
C14ORF72 0.2656 5.24e-06 0.0291
UBE2F 0.2624 6.884e-06 0.0291
IQCG 0.259 9.118e-06 0.0291
HDAC7 0.2587 9.375e-06 0.0291
TNFSF9 0.2575 1.032e-05 0.0291
IKBKE 0.2525 1.549e-05 0.0329
ZMYND11 0.2492 2.022e-05 0.0329
C7ORF44 0.2483 2.167e-05 0.0329
Clinical variable #6: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.85 (1.1)
  N
  N0 177
  N1 66
  N2 39
  N3 45
     
  Significant markers N = 30
  pos. correlated 1
  neg. correlated 29
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
ABCA7 -0.2437 8.302e-06 0.164
CCNF -0.2289 2.934e-05 0.201
C10ORF68 -0.2248 4.081e-05 0.201
CCDC7__1 -0.2248 4.081e-05 0.201
PKP2 -0.2183 6.884e-05 0.203
C9ORF37 -0.2154 8.608e-05 0.203
EHMT1__1 -0.2154 8.608e-05 0.203
MFAP2 -0.2135 0.0001002 0.203
DND1 -0.2114 0.0001173 0.203
AANAT -0.2091 0.00014 0.203
Clinical variable #7: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 319
  class1 22
     
  Significant markers N = 0
Clinical variable #8: 'MELANOMA_ULCERATION'

No gene related to 'MELANOMA_ULCERATION'.

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

MELANOMA_ULCERATION Labels N
  NO 134
  YES 91
     
  Significant markers N = 0
Clinical variable #9: 'MELANOMA_PRIMARY_KNOWN'

No gene related to 'MELANOMA_PRIMARY_KNOWN'.

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

MELANOMA_PRIMARY_KNOWN Labels N
  NO 47
  YES 320
     
  Significant markers N = 0
Clinical variable #10: 'BRESLOW_THICKNESS'

30 genes related to 'BRESLOW_THICKNESS'.

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

BRESLOW_THICKNESS Mean (SD) 3.47 (4.7)
  Significant markers N = 30
  pos. correlated 23
  neg. correlated 7
List of top 10 genes differentially expressed by 'BRESLOW_THICKNESS'

Table S15.  Get Full Table List of top 10 genes significantly correlated to 'BRESLOW_THICKNESS' by Spearman correlation test

SpearmanCorr corrP Q
HDAC7 0.2844 2.324e-06 0.0297
FAM100B 0.278 3.976e-06 0.0297
SPATS2L 0.2704 7.386e-06 0.0297
ASF1A 0.27 7.652e-06 0.0297
IKBKE 0.2688 8.446e-06 0.0297
SGK1 -0.2664 1.02e-05 0.0297
C7ORF44 0.266 1.054e-05 0.0297
PARP12 0.2612 1.536e-05 0.0311
ITPRIP 0.2611 1.555e-05 0.0311
GAL3ST3 -0.2573 2.078e-05 0.0311
Clinical variable #11: 'GENDER'

3 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 137
  MALE 230
     
  Significant markers N = 3
  Higher in MALE 3
  Higher in FEMALE 0
List of 3 genes differentially expressed by 'GENDER'

Table S17.  Get Full Table List of 3 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
KIF4B 8815 1.672e-12 3.3e-08 0.7202
NICN1__1 10429 6.042e-08 0.000597 0.669
ZNF839 11307 6.057e-06 0.0399 0.6412
Clinical variable #12: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

Table S18.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 317
  YES 48
     
  Significant markers N = 0
Clinical variable #13: 'RACE'

No gene related to 'RACE'.

Table S19.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 5
  BLACK OR AFRICAN AMERICAN 1
  WHITE 353
     
  Significant markers N = 0
Clinical variable #14: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S20.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 7
  NOT HISPANIC OR LATINO 352
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = SKCM-TM.meth.by_min_clin_corr.data.txt

  • Clinical data file = SKCM-TM.merged_data.txt

  • Number of patients = 367

  • Number of genes = 19745

  • Number of clinical features = 14

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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