Correlation between gene methylation status and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C16Q1W09
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/C16Q1W09
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 19501 genes and 11 clinical features across 167 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

  • 58 genes correlated to 'AGE'.

    • KCNS2 ,  WTIP ,  AFAP1 ,  RAB3D ,  CDCA7 ,  ...

  • 80 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  ALDH3A1 ,  C17ORF73 ,  CCDC121__1 ,  ...

  • 17 genes correlated to 'RACE'.

    • SCAMP5 ,  NCLN ,  C15ORF53 ,  SERHL2 ,  CDCA7 ,  ...

  • No genes correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'COMPLETENESS.OF.RESECTION', and 'ETHNICITY'.

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=58 older N=38 younger N=20
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=80 male N=80 female N=0
HISTOLOGICAL TYPE Kruskal-Wallis test   N=0        
COMPLETENESS OF RESECTION Kruskal-Wallis test   N=0        
RACE Kruskal-Wallis test N=17        
ETHNICITY Wilcoxon 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) 0-113 (median=13.8)
  censored N = 95
  death N = 68
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

58 genes related to 'AGE'.

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

AGE Mean (SD) 61.59 (14)
  Significant markers N = 58
  pos. correlated 38
  neg. correlated 20
List of top 10 genes differentially expressed by 'AGE'

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

SpearmanCorr corrP Q
KCNS2 0.4489 1.466e-09 2.86e-05
WTIP 0.4175 2.425e-08 0.000473
AFAP1 0.4113 4.059e-08 0.000792
RAB3D 0.4088 4.997e-08 0.000974
CDCA7 0.4003 9.986e-08 0.00195
ZIC1 0.3997 1.049e-07 0.00205
BAALC__1 0.3885 2.521e-07 0.00491
C8ORF56__1 0.3885 2.521e-07 0.00491
MAP1B 0.386 3.046e-07 0.00594
DCHS1 0.3888 3.446e-07 0.00672
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 64
  STAGE II 39
  STAGE III 2
  STAGE IIIA 36
  STAGE IIIB 5
  STAGE IIIC 6
  STAGE IV 1
  STAGE IVA 1
  STAGE IVB 2
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.T.STAGE'

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

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.99 (0.95)
  N
  1 67
  2 43
  3 47
  4 9
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

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

PATHOLOGY.N.STAGE Labels N
  class0 106
  class1 3
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

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

PATHOLOGY.M.STAGE Labels N
  M0 127
  M1 3
  MX 37
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

80 genes related to 'GENDER'.

Table S8.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 62
  MALE 105
     
  Significant markers N = 80
  Higher in MALE 80
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S9.  Get Full Table List of top 10 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__1 6226 7.605e-23 1.48e-18 0.9564
UTP14C 6226 7.605e-23 1.48e-18 0.9564
ALDH3A1 1467 3.201e-09 6.24e-05 0.7747
C17ORF73 1492 5.279e-09 0.000103 0.7708
CCDC121__1 4935 2.649e-08 0.000516 0.7581
GPN1__1 4935 2.649e-08 0.000516 0.7581
ZNF35 4885 6.754e-08 0.00132 0.7504
C14ORF182 1632 7.684e-08 0.0015 0.7493
CCDC23 1640 8.898e-08 0.00173 0.7481
ERMAP 1640 8.898e-08 0.00173 0.7481
Clinical variable #8: 'HISTOLOGICAL.TYPE'

No gene related to 'HISTOLOGICAL.TYPE'.

Table S10.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  FIBROLAMELLAR CARCINOMA 1
  HEPATOCELLULAR CARCINOMA 163
  HEPATOCHOLANGIOCARCINOMA (MIXED) 3
     
  Significant markers N = 0
Clinical variable #9: '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 138
  R1 11
  R2 1
  RX 12
     
  Significant markers N = 0
Clinical variable #10: 'RACE'

17 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 39
  BLACK OR AFRICAN AMERICAN 8
  WHITE 113
     
  Significant markers N = 17
List of top 10 genes differentially expressed by 'RACE'

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

ANOVA_P Q
SCAMP5 1.51e-09 2.95e-05
NCLN 2.189e-09 4.27e-05
C15ORF53 9.673e-09 0.000189
SERHL2 9.756e-09 0.00019
CDCA7 6.67e-08 0.0013
DHRS7 4.367e-07 0.00851
SNX16 1.514e-06 0.0295
OSBPL10 1.546e-06 0.0301
ZNF860 1.546e-06 0.0301
C21ORF56 4.605e-06 0.0898
Clinical variable #11: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 5
  NOT HISPANIC OR LATINO 149
     
  Significant markers N = 0
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 = 167

  • Number of genes = 19501

  • Number of clinical features = 11

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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