Correlation between gene methylation status and clinical features
Pancreatic Adenocarcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Z89B59
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 20483 genes and 12 clinical features across 93 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one genes.

  • 8 genes correlated to 'GENDER'.

    • ALG11 ,  UTP14C ,  KIF4B ,  ETF1 ,  MYST2 ,  ...

  • No genes correlated to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'NUMBERPACKYEARSSMOKED', 'COMPLETENESS.OF.RESECTION', 'NUMBER.OF.LYMPH.NODES', and 'RACE'.

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 to Death Cox regression test   N=0        
AGE Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE Kruskal-Wallis test   N=0        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE Wilcoxon test   N=0        
PATHOLOGY M STAGE Kruskal-Wallis test   N=0        
GENDER Wilcoxon test N=8 male N=8 female N=0
HISTOLOGICAL TYPE Kruskal-Wallis test   N=0        
NUMBERPACKYEARSSMOKED 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        
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Years) 1-1991 (median=181)
  censored N = 56
  death N = 11
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 66.05 (11)
  Significant markers N = 0
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S3.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE IA 2
  STAGE IB 5
  STAGE IIA 15
  STAGE IIB 66
  STAGE III 2
  STAGE IV 3
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.T.STAGE'

No gene related to 'PATHOLOGY.T.STAGE'.

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.9 (0.42)
  N
  1 2
  2 7
  3 82
  4 2
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

No gene related to 'PATHOLOGY.N.STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 23
  class1 69
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

No gene related to 'PATHOLOGY.M.STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 43
  M1 3
  MX 47
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

8 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 46
  MALE 47
     
  Significant markers N = 8
  Higher in MALE 8
  Higher in FEMALE 0
List of 8 genes differentially expressed by 'GENDER'

Table S8.  Get Full Table List of 8 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 2063 4.627e-14 9.48e-10 0.9542
UTP14C 2063 4.627e-14 9.48e-10 0.9542
KIF4B 168 2.352e-12 4.82e-08 0.9223
ETF1 1859 2.308e-09 4.73e-05 0.8599
MYST2 1749 2.91e-07 0.00596 0.809
HAX1 459 1.791e-06 0.0367 0.7877
FRG1B 480 3.943e-06 0.0807 0.778
ANKRD20A4 1676 4.918e-06 0.101 0.7752
Clinical variable #8: 'HISTOLOGICAL.TYPE'

No gene related to 'HISTOLOGICAL.TYPE'.

Table S9.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  PANCREAS-ADENOCARCINOMA DUCTAL TYPE 81
  PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE 8
  PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA 3
     
  Significant markers N = 0
Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 25.26 (16)
  Significant markers N = 0
Clinical variable #10: 'COMPLETENESS.OF.RESECTION'

No gene related to 'COMPLETENESS.OF.RESECTION'.

Table S11.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 58
  R1 28
  R2 1
  RX 2
     
  Significant markers N = 0
Clinical variable #11: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S12.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.64 (2.9)
  Significant markers N = 0
Clinical variable #12: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 6
  WHITE 81
     
  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 = 93

  • Number of genes = 20483

  • Number of clinical features = 12

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

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] 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)