Correlation between gene methylation status and clinical features
Kidney Chromophobe (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C11N80HP
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/C11N80HP
Overview
Introduction

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

Summary

Testing the association between 16633 genes and 12 clinical features across 66 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 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • SYMPK ,  LOC643719 ,  MIP ,  MCRS1 ,  MANSC1 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • MGAT5B ,  LRP11 ,  IFNG ,  C1ORF133 ,  RPP25 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • LRPAP1 ,  NPBWR1 ,  FOXC1 ,  C14ORF50 ,  IPP ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • FOXC1 ,  RPP30 ,  TSPYL5 ,  TAC1 ,  FAM135B ,  ...

  • 1 gene correlated to 'GENDER'.

    • UTP14C

  • 30 genes correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • GRHL1 ,  LY6E ,  LOC729020 ,  C14ORF64 ,  POLR2C ,  ...

  • No genes correlated to 'YEARS_TO_BIRTH', 'PATHOLOGY_M_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', 'YEAR_OF_TOBACCO_SMOKING_ONSET', '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
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test N=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test   N=0        
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
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=29 lower stage N=1
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=1 male N=1 female N=0
KARNOFSKY_PERFORMANCE_SCORE Wilcoxon test   N=0        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test N=30 higher number_pack_years_smoked N=26 lower number_pack_years_smoked N=4
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
RACE Kruskal-Wallis 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) 1-153.7 (median=72.7)
  censored N = 55
  death N = 10
     
  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
SYMPK 1.02e-08 0.00017 0.913
LOC643719 7.38e-07 0.0061 0.879
MIP 2.17e-06 0.012 0.181
MCRS1 5.2e-06 0.022 0.854
MANSC1 1.69e-05 0.056 0.789
VIPR2 2.07e-05 0.057 0.739
C17ORF107 4.82e-05 0.11 0.856
NMD3 7.69e-05 0.14 0.407
MGAT5 7.84e-05 0.14 0.165
TREML4 0.000115 0.18 0.222
Clinical variable #2: 'YEARS_TO_BIRTH'

No gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 51.52 (14)
  Significant markers N = 0
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 21
  STAGE II 25
  STAGE III 14
  STAGE IV 6
     
  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
MGAT5B 1.758e-05 0.217
LRP11 7.295e-05 0.217
IFNG 0.0001231 0.217
C1ORF133 0.0001252 0.217
RPP25 0.0001327 0.217
SOX6 0.0001373 0.217
NAGK 0.0001476 0.217
FOXC1 0.0001636 0.217
LOC389332 0.0001663 0.217
ACY3 0.0001678 0.217
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) 2.02 (0.85)
  N
  T1 21
  T2 25
  T3 18
  T4 2
     
  Significant markers N = 30
  pos. correlated 28
  neg. correlated 2
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
LRPAP1 0.5393 2.985e-06 0.0496
NPBWR1 0.5042 1.587e-05 0.0945
FOXC1 0.4974 2.154e-05 0.0945
C14ORF50 0.4961 2.272e-05 0.0945
IPP 0.4875 3.306e-05 0.11
RPP25 0.4792 4.699e-05 0.13
EPHA10 0.4697 6.933e-05 0.143
MT1F 0.4686 7.243e-05 0.143
DMRT3 0.467 7.722e-05 0.143
SCUBE2 0.4598 0.0001028 0.166
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.16 (0.47)
  N
  N0 40
  N1 3
  N2 2
     
  Significant markers N = 30
  pos. correlated 29
  neg. correlated 1
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S9.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
FOXC1 0.5448 0.0001093 0.208
RPP30 -0.543 0.0001164 0.208
TSPYL5 0.5396 0.0001308 0.208
TAC1 0.539 0.0001335 0.208
FAM135B 0.5372 0.0001421 0.208
SPSB3 0.532 0.0001694 0.208
LOC643719 0.5292 0.0001858 0.208
PCDH10 0.5286 0.0001896 0.208
CHODL 0.5268 0.0002013 0.208
WNT1 0.525 0.0002137 0.208
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 34
  class1 2
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

One gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 27
  MALE 39
     
  Significant markers N = 1
  Higher in MALE 1
  Higher in FEMALE 0
List of one gene differentially expressed by 'GENDER'

Table S12.  Get Full Table List of one gene 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 961 1.512e-08 0.000252 0.9126
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Labels N
  class100 10
  class90 3
     
  Significant markers N = 0
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

30 genes related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 25.09 (22)
  Significant markers N = 30
  pos. correlated 26
  neg. correlated 4
List of top 10 genes differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

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

SpearmanCorr corrP Q
GRHL1 0.9704 7.42e-07 0.0117
LY6E 0.9658 1.403e-06 0.0117
LOC729020 0.9431 1.353e-05 0.075
C14ORF64 0.9248 4.594e-05 0.153
POLR2C 0.9248 4.594e-05 0.153
NFATC2IP 0.9203 5.946e-05 0.165
CTSO 0.9066 0.0001188 0.275
UGGT1 0.9021 0.0001462 0.275
BRP44L 0.8975 0.0001781 0.275
FCRLA -0.8975 0.0001781 0.275
Clinical variable #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1973.75 (15)
  Significant markers N = 0
Clinical variable #11: 'RACE'

No gene related to 'RACE'.

Table S17.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 4
  WHITE 58
     
  Significant markers N = 0
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 4
  NOT HISPANIC OR LATINO 32
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = KICH-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 66

  • Number of genes = 16633

  • Number of clinical features = 12

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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