Correlation between gene methylation status and clinical features
Pancreatic Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1M61HXD
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/C1M61HXD
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 20374 genes and 12 clinical features across 73 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • ELOVL2

  • 32 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • DOC2B ,  PLD6 ,  MSH2 ,  DNAJB3 ,  UGT1A10__3 ,  ...

  • 1 gene correlated to 'PATHOLOGY.M.STAGE'.

    • ANAPC4

  • 10 genes correlated to 'GENDER'.

    • SDHD ,  TIMM8B ,  PRKRIR ,  ALG11__1 ,  UTP14C ,  ...

  • 16 genes correlated to 'HISTOLOGICAL.TYPE'.

    • GALNT12 ,  FAM45A__1 ,  FAM45B__1 ,  ISYNA1 ,  KIAA0182 ,  ...

  • 140 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • CD3D ,  CD3G ,  WFDC2 ,  RPTOR ,  PPP1R9B ,  ...

  • No genes correlated to 'Time to Death', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'NUMBERPACKYEARSSMOKED', 'YEAROFTOBACCOSMOKINGONSET', and 'NUMBER.OF.LYMPH.NODES'.

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 Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=1 older N=1 younger N=0
NEOPLASM DISEASESTAGE ANOVA test N=32        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE t test   N=0        
PATHOLOGY M STAGE ANOVA test N=1        
GENDER t test N=10 male N=8 female N=2
HISTOLOGICAL TYPE ANOVA test N=16        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=140        
NUMBER OF LYMPH NODES Spearman correlation 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 (Months) 0-49.4 (median=7.4)
  censored N = 41
  death N = 29
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 65.74 (11)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ELOVL2 0.5237 1.993e-06 0.0406

Figure S1.  Get High-res Image As an example, this figure shows the association of ELOVL2 to 'AGE'. P value = 1.99e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

32 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE IA 2
  STAGE IB 4
  STAGE IIA 11
  STAGE IIB 51
  STAGE III 2
  STAGE IV 3
     
  Significant markers N = 32
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
DOC2B 1.714e-11 3.49e-07
PLD6 1.232e-09 2.51e-05
MSH2 7.111e-09 0.000145
DNAJB3 1.174e-08 0.000239
UGT1A10__3 1.174e-08 0.000239
UGT1A3__1 1.174e-08 0.000239
UGT1A4__1 1.174e-08 0.000239
UGT1A5__2 1.174e-08 0.000239
UGT1A6__3 1.174e-08 0.000239
UGT1A7__3 1.174e-08 0.000239

Figure S2.  Get High-res Image As an example, this figure shows the association of DOC2B to 'NEOPLASM.DISEASESTAGE'. P value = 1.71e-11 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

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

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.9 (0.45)
  N
  1 2
  2 5
  3 64
  4 2
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 18
  class1 54
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

One gene related to 'PATHOLOGY.M.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 39
  M1 3
  MX 31
     
  Significant markers N = 1
List of one gene differentially expressed by 'PATHOLOGY.M.STAGE'

Table S9.  Get Full Table List of one gene differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
ANAPC4 1.302e-06 0.0265

Figure S3.  Get High-res Image As an example, this figure shows the association of ANAPC4 to 'PATHOLOGY.M.STAGE'. P value = 1.3e-06 with ANOVA analysis.

Clinical variable #7: 'GENDER'

10 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 36
  MALE 37
     
  Significant markers N = 10
  Higher in MALE 8
  Higher in FEMALE 2
List of 10 genes differentially expressed by 'GENDER'

Table S11.  Get Full Table List of 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
SDHD 8.58 1.634e-12 3.33e-08 0.9099
TIMM8B 8.58 1.634e-12 3.33e-08 0.9099
PRKRIR 8.61 1.668e-12 3.4e-08 0.9039
ALG11__1 9.16 2.493e-12 5.08e-08 0.9392
UTP14C 9.16 2.493e-12 5.08e-08 0.9392
TLE1 -8.43 1.77e-11 3.61e-07 0.9167
ETF1 6.98 1.373e-09 2.8e-05 0.8581
EIF3J 6.87 3.322e-09 6.77e-05 0.8604
KIF4B -6.53 1.692e-08 0.000345 0.8874
MYST2 5.42 8.194e-07 0.0167 0.8063

Figure S4.  Get High-res Image As an example, this figure shows the association of SDHD to 'GENDER'. P value = 1.63e-12 with T-test analysis.

Clinical variable #8: 'HISTOLOGICAL.TYPE'

16 genes related to 'HISTOLOGICAL.TYPE'.

Table S12.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  PANCREAS-ADENOCARCINOMA DUCTAL TYPE 65
  PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE 5
  PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA 2
     
  Significant markers N = 16
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
GALNT12 2.801e-10 5.71e-06
FAM45A__1 5.998e-10 1.22e-05
FAM45B__1 5.998e-10 1.22e-05
ISYNA1 1.126e-09 2.29e-05
KIAA0182 3.156e-09 6.43e-05
ME1 3.647e-09 7.43e-05
SLC2A6 1.15e-08 0.000234
PIM1 6.224e-08 0.00127
AHSG 1.407e-07 0.00287
CTBS 1.74e-07 0.00354

Figure S5.  Get High-res Image As an example, this figure shows the association of GALNT12 to 'HISTOLOGICAL.TYPE'. P value = 2.8e-10 with ANOVA analysis.

Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 24.51 (14)
  Significant markers N = 0
Clinical variable #10: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

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

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1971.74 (14)
  Significant markers N = 0
Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

140 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S16.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 43
  R1 24
  RX 2
     
  Significant markers N = 140
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
CD3D 1.753e-12 3.57e-08
CD3G 1.753e-12 3.57e-08
WFDC2 1.509e-11 3.07e-07
RPTOR 2.965e-11 6.04e-07
PPP1R9B 8.607e-11 1.75e-06
DDX4 1.148e-10 2.34e-06
EFCAB6 1.516e-10 3.09e-06
RTP1 1.697e-10 3.46e-06
INPPL1 2.248e-10 4.58e-06
ST6GAL1 2.29e-10 4.66e-06

Figure S6.  Get High-res Image As an example, this figure shows the association of CD3D to 'COMPLETENESS.OF.RESECTION'. P value = 1.75e-12 with ANOVA analysis.

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) 2.85 (3.1)
  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 = 73

  • Number of genes = 20374

  • 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)
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