Correlation between gene methylation status and clinical features
Skin Cutaneous Melanoma (Metastatic)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1QV3KZX
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/C1QV3KZX
Overview
Introduction

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

Summary

Testing the association between 16726 genes and 14 clinical features across 367 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one genes.

  • 30 genes correlated to 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'.

    • VPS53 ,  CNPY1 ,  CCR6 ,  LIN7B ,  C12ORF71 ,  ...

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • IFITM1 ,  GBP2 ,  NR4A3 ,  C10ORF140 ,  MSRB3 ,  ...

  • 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 ,  UBE2F ,  HDAC7 ,  C14ORF72 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • ABCA7 ,  CCNF ,  C10ORF68 ,  PKP2 ,  C9ORF37 ,  ...

  • 30 genes correlated to 'BRESLOW_THICKNESS'.

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

  • 4 genes correlated to 'GENDER'.

    • KIF4B ,  NICN1 ,  ZNF839 ,  NCRNA00116

  • No genes correlated to '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=30   N=NA   N=NA
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test N=30   N=NA   N=NA
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=27 lower stage N=3
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=2 lower stage N=28
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=4 male N=4 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'

30 genes 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=46.5)
  censored N = 127
  death N = 125
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 genes differentially expressed by 'TIME_FROM_SPECIMEN_DX_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time from Specimen Diagnosis 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
VPS53 4.43e-07 0.0052 0.355
CNPY1 8.78e-07 0.0052 0.649
CCR6 9.29e-07 0.0052 0.639
LIN7B 2.06e-06 0.0084 0.385
C12ORF71 2.51e-06 0.0084 0.395
CDK15 3.75e-06 0.0094 0.385
DSTYK 4.19e-06 0.0094 0.38
PDK2 4.62e-06 0.0094 0.389
TBX21 5.28e-06 0.0094 0.621
ITGB1BP1 5.62e-06 0.0094 0.371
Clinical variable #2: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 genes related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S3.  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 = 172
  death N = 194
     
  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 S4.  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
IFITM1 1.64e-08 0.00028 0.643
GBP2 3.55e-08 3e-04 0.627
NR4A3 2.51e-07 0.0011 0.376
C10ORF140 2.57e-07 0.0011 0.413
MSRB3 8.94e-07 0.0025 0.387
C1ORF204 9.66e-07 0.0025 0.381
C5ORF38 1.03e-06 0.0025 0.395
CEL 1.37e-06 0.0026 0.4
HCP5 1.59e-06 0.0026 0.614
C8ORF42 1.66e-06 0.0026 0.386
Clinical variable #3: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

Table S5.  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 S6.  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 0.000435
RSPO4 0.2824 5.205e-08 0.000435
HIST1H1D 0.2777 8.857e-08 0.000494
ANKRD45 0.2697 2.121e-07 0.000685
NFATC2 0.2685 2.403e-07 0.000685
PDGFD 0.2669 2.84e-07 0.000685
LOC100128675 0.2669 2.868e-07 0.000685
TES 0.2641 3.816e-07 0.000798
PARP12 0.2615 5.026e-07 0.000894
C9ORF122 0.2609 5.346e-07 0.000894
Clinical variable #4: 'PATHOLOGIC_STAGE'

2 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  I/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 39
  STAGE IIIA 15
  STAGE IIIB 35
  STAGE IIIC 55
  STAGE IV 21
     
  Significant markers N = 2
List of 2 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S8.  Get Full Table List of 2 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
PTP4A2 1.314e-05 0.219
CAPN3 2.623e-05 0.219
Clinical variable #5: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.45 (1.2)
  N
  T0 23
  T1 41
  T2 73
  T3 80
  T4 68
     
  Significant markers N = 30
  pos. correlated 27
  neg. correlated 3
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

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

SpearmanCorr corrP Q
GAL3ST3 -0.2908 5.86e-07 0.0098
FAM100B 0.277 2.036e-06 0.017
UBE2F 0.2721 3.126e-06 0.0174
HDAC7 0.2683 4.36e-06 0.0182
C14ORF72 0.2656 5.47e-06 0.0183
TNFSF9 0.2612 7.887e-06 0.0219
IKBKE 0.259 9.429e-06 0.0219
SEMA4D 0.2558 1.225e-05 0.0219
PLEKHB1 0.2558 1.227e-05 0.0219
IQCG 0.255 1.308e-05 0.0219
Clinical variable #6: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

Table S11.  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 2
  neg. correlated 28
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S12.  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 0.139
CCNF -0.2289 2.934e-05 0.221
C10ORF68 -0.2248 4.081e-05 0.221
PKP2 -0.2183 6.884e-05 0.221
C9ORF37 -0.2154 8.608e-05 0.221
MFAP2 -0.2135 0.0001002 0.221
DND1 -0.2114 0.0001173 0.221
AANAT -0.2091 0.00014 0.221
BANP -0.2064 0.0001714 0.221
NUCB1 -0.2051 0.0001886 0.221
Clinical variable #7: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

Table S13.  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 S14.  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 S15.  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 S16.  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 S17.  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 0.0252
FAM100B 0.278 3.976e-06 0.0252
SPATS2L 0.2704 7.386e-06 0.0252
ASF1A 0.27 7.652e-06 0.0252
IKBKE 0.2688 8.446e-06 0.0252
SGK1 -0.2664 1.02e-05 0.0252
C7ORF44 0.266 1.054e-05 0.0252
PARP12 0.2612 1.536e-05 0.0283
ITPRIP 0.2611 1.555e-05 0.0283
GAL3ST3 -0.2573 2.078e-05 0.0283
Clinical variable #11: 'GENDER'

4 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 137
  MALE 230
     
  Significant markers N = 4
  Higher in MALE 4
  Higher in FEMALE 0
List of 4 genes differentially expressed by 'GENDER'

Table S19.  Get Full Table List of 4 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 2.8e-08 0.7202
NICN1 10429 6.042e-08 0.000505 0.669
ZNF839 11307 6.057e-06 0.0338 0.6412
NCRNA00116 11832 6.599e-05 0.276 0.6245
Clinical variable #12: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 318
  YES 48
     
  Significant markers N = 0
Clinical variable #13: 'RACE'

No gene related to 'RACE'.

Table S21.  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 S22.  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 = 16726

  • 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, 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)
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