Correlation between gene methylation status and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1FF3R0T
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/C1FF3R0T
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 19515 genes and 8 clinical features across 154 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 15 genes correlated to 'AGE'.

    • KCNS2 ,  WTIP ,  RAB3D ,  ZIC1 ,  CDCA7 ,  ...

  • 61 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • SEPSECS ,  SMU1 ,  BIVM ,  KDELC1 ,  MSRB2 ,  ...

  • 261 genes correlated to 'PATHOLOGY.N.STAGE'.

    • MSC ,  PTP4A3 ,  FAM157A ,  GRAMD1A ,  THBD ,  ...

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

    • SEPSECS ,  ANKAR ,  MMP2 ,  SLC12A9 ,  AHR ,  ...

  • 24 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  ALDH3A1 ,  C14ORF182 ,  CCDC23__1 ,  ...

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

    • SEPSECS ,  BIVM ,  KDELC1 ,  CCDC94 ,  C5ORF42 ,  ...

  • No genes correlated to 'Time to Death', and 'PATHOLOGY.T.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=15 older N=14 younger N=1
NEOPLASM DISEASESTAGE ANOVA test N=61        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE t test N=261 class1 N=59 class0 N=202
PATHOLOGY M STAGE ANOVA test N=24        
GENDER t test N=24 male N=6 female N=18
COMPLETENESS OF RESECTION ANOVA test N=8        
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) 0-113 (median=13.9)
  censored N = 88
  death N = 63
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

15 genes related to 'AGE'.

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

AGE Mean (SD) 61.2 (14)
  Significant markers N = 15
  pos. correlated 14
  neg. correlated 1
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
KCNS2 0.4529 4.654e-09 9.08e-05
WTIP 0.4214 6.416e-08 0.00125
RAB3D 0.4163 9.604e-08 0.00187
ZIC1 0.4026 2.728e-07 0.00532
CDCA7 0.4018 2.89e-07 0.00564
SHOX2 0.3982 3.76e-07 0.00734
MAP1B 0.3937 5.236e-07 0.0102
DCHS1 0.3973 5.245e-07 0.0102
BAALC__1 0.3868 8.535e-07 0.0166
C8ORF56__1 0.3868 8.535e-07 0.0166

Figure S1.  Get High-res Image As an example, this figure shows the association of KCNS2 to 'AGE'. P value = 4.65e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

61 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 58
  STAGE II 36
  STAGE III 2
  STAGE IIIA 34
  STAGE IIIB 4
  STAGE IIIC 6
  STAGE IV 1
  STAGE IVA 1
  STAGE IVB 2
     
  Significant markers N = 61
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
SEPSECS 1.621e-72 3.16e-68
SMU1 3.666e-47 7.15e-43
BIVM 6.512e-26 1.27e-21
KDELC1 6.512e-26 1.27e-21
MSRB2 3.412e-20 6.66e-16
CCDC94 2.642e-19 5.16e-15
ANKAR 6.243e-17 1.22e-12
TRIM5 1.583e-16 3.09e-12
EIF5 6.425e-16 1.25e-11
CD47 2.827e-15 5.51e-11

Figure S2.  Get High-res Image As an example, this figure shows the association of SEPSECS to 'NEOPLASM.DISEASESTAGE'. P value = 1.62e-72 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) 2.01 (0.96)
  N
  1 61
  2 40
  3 44
  4 9
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

261 genes related to 'PATHOLOGY.N.STAGE'.

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

PATHOLOGY.N.STAGE Labels N
  class0 101
  class1 3
     
  Significant markers N = 261
  Higher in class1 59
  Higher in class0 202
List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

T(pos if higher in 'class1') ttestP Q AUC
MSC -11.95 2.142e-20 4.18e-16 0.9109
PTP4A3 -16.66 2.228e-20 4.35e-16 0.9604
FAM157A -15.47 4.439e-19 8.66e-15 0.9538
GRAMD1A -10.43 9.053e-18 1.77e-13 0.8086
THBD -11 1.882e-17 3.67e-13 0.8218
TSPAN15 -10.22 3.229e-17 6.3e-13 0.8647
PCDHGA1__14 -10.04 6.598e-17 1.29e-12 0.8647
PCDHGA2__14 -10.04 6.598e-17 1.29e-12 0.8647
PCDHGA3__12 -10.04 6.598e-17 1.29e-12 0.8647
PCDHGA4__11 -10.04 6.598e-17 1.29e-12 0.8647

Figure S3.  Get High-res Image As an example, this figure shows the association of MSC to 'PATHOLOGY.N.STAGE'. P value = 2.14e-20 with T-test analysis.

Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 121
  M1 3
  MX 30
     
  Significant markers N = 24
List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
SEPSECS 3.196e-14 6.24e-10
ANKAR 9.604e-14 1.87e-09
MMP2 2.024e-12 3.95e-08
SLC12A9 5.334e-12 1.04e-07
AHR 5.732e-10 1.12e-05
HSPB11 7.718e-09 0.000151
LRRC42 7.718e-09 0.000151
C12ORF34__1 2.06e-08 0.000402
MGC14436__1 2.06e-08 0.000402
PTPN4 3.854e-08 0.000752

Figure S4.  Get High-res Image As an example, this figure shows the association of SEPSECS to 'PATHOLOGY.M.STAGE'. P value = 3.2e-14 with ANOVA analysis.

Clinical variable #7: 'GENDER'

24 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 61
  MALE 93
     
  Significant markers N = 24
  Higher in MALE 6
  Higher in FEMALE 18
List of top 10 genes differentially expressed by 'GENDER'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
ALG11__1 14.03 8.073e-22 1.58e-17 0.9542
UTP14C 14.03 8.073e-22 1.58e-17 0.9542
ALDH3A1 -7.07 5.386e-11 1.05e-06 0.7876
C14ORF182 -5.51 2.238e-07 0.00437 0.7446
CCDC23__1 -5.41 2.616e-07 0.0051 0.7243
ERMAP__1 -5.41 2.616e-07 0.0051 0.7243
FAM83A -5.43 2.746e-07 0.00536 0.7379
LOC100131726 -5.43 2.746e-07 0.00536 0.7379
ZC3H4 5.38 2.793e-07 0.00545 0.7233
CCDC121__1 5.45 2.935e-07 0.00573 0.7564

Figure S5.  Get High-res Image As an example, this figure shows the association of ALG11__1 to 'GENDER'. P value = 8.07e-22 with T-test analysis.

Clinical variable #8: 'COMPLETENESS.OF.RESECTION'

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

Table S13.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 126
  R1 11
  R2 1
  RX 11
     
  Significant markers N = 8
List of 8 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S14.  Get Full Table List of 8 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
SEPSECS 2.291e-80 4.47e-76
BIVM 4.095e-28 7.99e-24
KDELC1 4.095e-28 7.99e-24
CCDC94 7.863e-25 1.53e-20
C5ORF42 2.59e-15 5.05e-11
MMP2 2.6e-11 5.07e-07
C1ORF101 1.099e-09 2.14e-05
ACAD9 4.569e-07 0.00891

Figure S6.  Get High-res Image As an example, this figure shows the association of SEPSECS to 'COMPLETENESS.OF.RESECTION'. P value = 2.29e-80 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 154

  • Number of genes = 19515

  • 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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