Correlation between gene methylation status and clinical features
Pancreatic Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1WH2P27
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/C1WH2P27
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 20431 genes and 14 clinical features across 173 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one genes.

  • 2 genes correlated to 'YEARS_TO_BIRTH'.

    • ELOVL2 ,  RANBP17

  • 3 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • EIF4A1__1 ,  SNORA67 ,  SNORD10

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • LIMCH1 ,  DPY19L2 ,  SLC19A1 ,  RRN3P1 ,  GBA2 ,  ...

  • 4 genes correlated to 'PATHOLOGY_N_STAGE'.

    • EIF4A1__1 ,  SNORA67 ,  SNORD10 ,  AACS

  • 30 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  KIF4B ,  ETF1 ,  MYST2 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • RAB36 ,  LOC284441 ,  WDR69 ,  GDPD5 ,  AMTN ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_M_STAGE', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 'COMPLETENESS_OF_RESECTION', 'NUMBER_OF_LYMPH_NODES', '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=0        
YEARS_TO_BIRTH Spearman correlation test N=2 older N=2 younger N=0
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=3        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=18 lower stage N=12
PATHOLOGY_N_STAGE Wilcoxon test N=4 n1 N=4 n0 N=0
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
COMPLETENESS_OF_RESECTION Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES 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'

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) 0.1-84.1 (median=14.3)
  censored N = 82
  death N = 90
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

2 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 64.8 (11)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S3.  Get Full Table List of 2 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
ELOVL2 0.3701 5.411e-07 0.0111
RANBP17 0.3244 1.338e-05 0.137
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

3 genes related to 'NEOPLASM_DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM_DISEASESTAGE'

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 1
  STAGE IA 6
  STAGE IB 13
  STAGE IIA 26
  STAGE IIB 117
  STAGE III 5
  STAGE IV 4
     
  Significant markers N = 3
List of 3 genes differentially expressed by 'NEOPLASM_DISEASESTAGE'

Table S5.  Get Full Table List of 3 genes differentially expressed by 'NEOPLASM_DISEASESTAGE'

kruskal_wallis_P Q
EIF4A1__1 4.284e-05 0.292
SNORA67 4.284e-05 0.292
SNORD10 4.284e-05 0.292
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) 2.81 (0.54)
  N
  T1 8
  T2 21
  T3 139
  T4 4
     
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
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
LIMCH1 0.3394 5.23e-06 0.0444
DPY19L2 0.3393 5.279e-06 0.0444
SLC19A1 -0.3333 7.889e-06 0.0444
RRN3P1 -0.3295 1.019e-05 0.0444
GBA2 0.3213 1.729e-05 0.0444
RGP1 0.3213 1.729e-05 0.0444
MTMR10 0.3213 1.731e-05 0.0444
MARK3 0.3205 1.814e-05 0.0444
RTDR1__1 0.3155 2.486e-05 0.0444
RASL12 0.3115 3.183e-05 0.0444
Clinical variable #5: 'PATHOLOGY_N_STAGE'

4 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 45
  N1 124
     
  Significant markers N = 4
  Higher in N1 4
  Higher in N0 0
List of 4 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S9.  Get Full Table List of 4 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
EIF4A1__1 4127 1.999e-06 0.0136 0.7396
SNORA67 4127 1.999e-06 0.0136 0.7396
SNORD10 4127 1.999e-06 0.0136 0.7396
AACS 3969 7.636e-06 0.039 0.7275
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 79
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 77
  MALE 96
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S12.  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__1 6981 1.098e-23 1.12e-19 0.9444
UTP14C 6981 1.098e-23 1.12e-19 0.9444
KIF4B 839 2.66e-18 1.78e-14 0.8865
ETF1 6543 3.483e-18 1.78e-14 0.8851
MYST2 6098 2.212e-13 9.04e-10 0.8249
FRG1B 1496 1.839e-11 6.26e-08 0.7976
HAX1 1561 7.042e-11 2.06e-07 0.7888
NCRNA00116 1788 5.664e-09 1.45e-05 0.7581
ANKRD20A4 5425 1.294e-07 0.000294 0.7339
FIGNL1 1951 1.493e-07 0.000305 0.7333
Clinical variable #8: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PANCREAS-ADENOCARCINOMA DUCTAL TYPE 145
  PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE 22
  PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA 4
  PANCREAS-UNDIFFERENTIATED CARCINOMA 1
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
RAB36 4.376e-08 0.000894
LOC284441 4.838e-06 0.0293
WDR69 8.378e-06 0.0293
GDPD5 1.02e-05 0.0293
AMTN 1.135e-05 0.0293
ATP5G1 1.262e-05 0.0293
LASS3 1.306e-05 0.0293
PHF15 1.32e-05 0.0293
SERPINB7 1.354e-05 0.0293
RBM46 1.456e-05 0.0293
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 26.45 (18)
  Significant markers N = 0
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) 1970.67 (13)
  Significant markers N = 0
Clinical variable #11: 'COMPLETENESS_OF_RESECTION'

No gene related to 'COMPLETENESS_OF_RESECTION'.

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

COMPLETENESS_OF_RESECTION Labels N
  R0 108
  R1 51
  R2 2
  RX 4
     
  Significant markers N = 0
Clinical variable #12: 'NUMBER_OF_LYMPH_NODES'

No gene related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 3.04 (3.5)
  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 3
  BLACK OR AFRICAN AMERICAN 7
  WHITE 159
     
  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 3
  NOT HISPANIC OR LATINO 136
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 173

  • Number of genes = 20431

  • Number of clinical features = 14

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle