Correlation between gene methylation status and clinical features
Esophageal Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1GQ6WCM
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/C1GQ6WCM
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 19880 genes and 8 clinical features across 57 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 7 genes correlated to 'AGE'.

    • TMEM92 ,  FAM46A ,  PRSS3 ,  ARHGAP26 ,  SYT14 ,  ...

  • 102 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • C20ORF177 ,  PPP1R3D ,  KRT81 ,  GADD45B ,  AIPL1 ,  ...

  • 42 genes correlated to 'PATHOLOGY.M.STAGE'.

    • C20ORF177 ,  PPP1R3D ,  TOB2 ,  CSTF2T ,  PRKG1 ,  ...

  • 2 genes correlated to 'GENDER'.

    • PTGER4 ,  SLC23A2

  • 1 gene correlated to 'NUMBERPACKYEARSSMOKED'.

    • CKS2

  • No genes correlated to 'Time to Death', 'PATHOLOGY.T.STAGE', and 'PATHOLOGY.N.STAGE'.

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=7 older N=1 younger N=6
NEOPLASM DISEASESTAGE ANOVA test N=102        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=42        
GENDER t test N=2 male N=2 female N=0
NUMBERPACKYEARSSMOKED Spearman correlation test N=1 higher numberpackyearssmoked N=1 lower numberpackyearssmoked 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) 0-30.7 (median=1.1)
  censored N = 41
  death N = 13
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

7 genes related to 'AGE'.

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

AGE Mean (SD) 62.09 (12)
  Significant markers N = 7
  pos. correlated 1
  neg. correlated 6
List of 7 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 7 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
TMEM92 -0.6381 9.309e-08 0.00185
FAM46A -0.6168 3.232e-07 0.00642
PRSS3 -0.6094 4.881e-07 0.0097
ARHGAP26 -0.6012 7.606e-07 0.0151
SYT14 0.5837 1.886e-06 0.0375
CPVL -0.5812 2.133e-06 0.0424
WWC1 -0.5791 2.369e-06 0.0471

Figure S1.  Get High-res Image As an example, this figure shows the association of TMEM92 to 'AGE'. P value = 9.31e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

102 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 1
  STAGE IA 3
  STAGE IB 2
  STAGE II 1
  STAGE IIA 16
  STAGE IIB 11
  STAGE III 5
  STAGE IIIA 8
  STAGE IIIB 5
  STAGE IIIC 2
  STAGE IV 1
     
  Significant markers N = 102
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
C20ORF177 6.314e-60 1.26e-55
PPP1R3D 6.314e-60 1.26e-55
KRT81 8.164e-47 1.62e-42
GADD45B 5.016e-35 9.97e-31
AIPL1 7.855e-27 1.56e-22
TOB2 1.602e-26 3.18e-22
CPT2 2.633e-24 5.23e-20
CSTF2T 2.527e-23 5.02e-19
PRKG1 2.527e-23 5.02e-19
TMEM85 3.887e-22 7.72e-18

Figure S2.  Get High-res Image As an example, this figure shows the association of C20ORF177 to 'NEOPLASM.DISEASESTAGE'. P value = 6.31e-60 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) 2.57 (0.76)
  N
  1 6
  2 15
  3 32
  4 3
     
  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 Mean (SD) 0.69 (0.81)
  N
  0 27
  1 20
  2 6
  3 2
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

42 genes related to 'PATHOLOGY.M.STAGE'.

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

PATHOLOGY.M.STAGE Labels N
  M0 43
  M1A 1
  MX 8
     
  Significant markers N = 42
List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
C20ORF177 2.093e-69 4.16e-65
PPP1R3D 2.093e-69 4.16e-65
TOB2 8.395e-33 1.67e-28
CSTF2T 6.13e-30 1.22e-25
PRKG1 6.13e-30 1.22e-25
TMEM85 1.43e-28 2.84e-24
ABCE1 5.778e-27 1.15e-22
ANAPC10 5.778e-27 1.15e-22
TRAFD1 1.443e-18 2.87e-14
PROZ 2.241e-18 4.45e-14

Figure S3.  Get High-res Image As an example, this figure shows the association of C20ORF177 to 'PATHOLOGY.M.STAGE'. P value = 2.09e-69 with ANOVA analysis.

Clinical variable #7: 'GENDER'

2 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 8
  MALE 49
     
  Significant markers N = 2
  Higher in MALE 2
  Higher in FEMALE 0
List of 2 genes differentially expressed by 'GENDER'

Table S11.  Get Full Table List of 2 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
PTGER4 6.12 6.73e-07 0.0134 0.824
SLC23A2 5.45 1.414e-06 0.0281 0.7679

Figure S4.  Get High-res Image As an example, this figure shows the association of PTGER4 to 'GENDER'. P value = 6.73e-07 with T-test analysis.

Clinical variable #8: 'NUMBERPACKYEARSSMOKED'

One gene related to 'NUMBERPACKYEARSSMOKED'.

Table S12.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 35.57 (16)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S13.  Get Full Table List of one gene significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
CKS2 0.7236 1.95e-06 0.0388

Figure S5.  Get High-res Image As an example, this figure shows the association of CKS2 to 'NUMBERPACKYEARSSMOKED'. P value = 1.95e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

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

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

  • Number of patients = 57

  • Number of genes = 19880

  • Number of clinical features = 8

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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