Correlation between gene methylation status and clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1XP73DM
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/C1XP73DM
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 20424 genes and 5 clinical features across 364 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 220 genes correlated to 'AGE'.

    • ANGPT4 ,  ALS2CL ,  GPR158 ,  HDAC11 ,  PLA2G15 ,  ...

  • 2974 genes correlated to 'HISTOLOGICAL.TYPE'.

    • APBB1IP ,  SSTR1 ,  CARD11 ,  PPIH ,  CRYAB__1 ,  ...

  • 15 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • MAP2K2 ,  FIZ1 ,  ZNF524 ,  C17ORF28 ,  C14ORF93 ,  ...

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

    • TNFRSF19 ,  ALOX12 ,  MYRIP ,  POLR1A__1 ,  LACTB2 ,  ...

  • No genes correlated to 'Time to Death'

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=220 older N=65 younger N=155
HISTOLOGICAL TYPE ANOVA test N=2974        
RADIATIONS RADIATION REGIMENINDICATION t test N=15 yes N=14 no N=1
COMPLETENESS OF RESECTION ANOVA test N=22        
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) 0-191.8 (median=16.9)
  censored N = 319
  death N = 42
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

220 genes related to 'AGE'.

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

AGE Mean (SD) 63.96 (11)
  Significant markers N = 220
  pos. correlated 65
  neg. correlated 155
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ANGPT4 0.3519 5.069e-12 1.04e-07
ALS2CL -0.3437 1.665e-11 3.4e-07
GPR158 -0.336 4.987e-11 1.02e-06
HDAC11 -0.3305 1.07e-10 2.18e-06
PLA2G15 -0.3284 1.41e-10 2.88e-06
ADAMTS15 -0.3269 1.727e-10 3.53e-06
NOS2 -0.3258 2.014e-10 4.11e-06
ITPK1 -0.3186 5.267e-10 1.08e-05
NCRNA00203 -0.3186 5.267e-10 1.08e-05
PLUNC 0.3184 5.397e-10 1.1e-05

Figure S1.  Get High-res Image As an example, this figure shows the association of ANGPT4 to 'AGE'. P value = 5.07e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

2974 genes related to 'HISTOLOGICAL.TYPE'.

Table S4.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA 268
  MIXED SEROUS AND ENDOMETRIOID 18
  SEROUS ENDOMETRIAL ADENOCARCINOMA 78
     
  Significant markers N = 2974
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
APBB1IP 1.409e-48 2.88e-44
SSTR1 5.391e-44 1.1e-39
CARD11 9.268e-44 1.89e-39
PPIH 2.985e-43 6.1e-39
CRYAB__1 8.038e-41 1.64e-36
HSPB2__1 8.038e-41 1.64e-36
ADAMTS16 1.278e-40 2.61e-36
CRYAB 5.399e-40 1.1e-35
HSPB2 5.399e-40 1.1e-35
PRKCB 1.023e-38 2.09e-34

Figure S2.  Get High-res Image As an example, this figure shows the association of APBB1IP to 'HISTOLOGICAL.TYPE'. P value = 1.41e-48 with ANOVA analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

15 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S6.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 84
  YES 280
     
  Significant markers N = 15
  Higher in YES 14
  Higher in NO 1
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
MAP2K2 5.89 2.539e-08 0.000519 0.7009
FIZ1 5.79 4.595e-08 0.000938 0.6995
ZNF524 5.79 4.595e-08 0.000938 0.6995
C17ORF28 5.42 2.145e-07 0.00438 0.6852
C14ORF93 5.31 3.7e-07 0.00755 0.6955
GIPR 5.31 4.819e-07 0.00984 0.6875
PEF1 5.13 6.365e-07 0.013 0.6584
RGPD4 5.23 7.252e-07 0.0148 0.6743
MST1 4.99 1.305e-06 0.0267 0.6496
RNF123__1 4.99 1.305e-06 0.0267 0.6496

Figure S3.  Get High-res Image As an example, this figure shows the association of MAP2K2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.54e-08 with T-test analysis.

Clinical variable #5: 'COMPLETENESS.OF.RESECTION'

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

Table S8.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 241
  R1 18
  R2 12
  RX 26
     
  Significant markers N = 22
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
TNFRSF19 1.313e-09 2.68e-05
ALOX12 5.848e-09 0.000119
MYRIP 1.839e-08 0.000376
POLR1A__1 6.581e-07 0.0134
LACTB2 1.253e-06 0.0256
XKR9 1.253e-06 0.0256
CRYBA2 1.283e-06 0.0262
TANC1 1.534e-06 0.0313
EWSR1 1.56e-06 0.0318
RHBDD3 1.56e-06 0.0318

Figure S4.  Get High-res Image As an example, this figure shows the association of TNFRSF19 to 'COMPLETENESS.OF.RESECTION'. P value = 1.31e-09 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 364

  • Number of genes = 20424

  • Number of clinical features = 5

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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