Correlation between gene methylation status and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1NK3C2D
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1NK3C2D
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 20125 genes and 7 clinical features across 85 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 41 genes correlated to 'Time to Death'.

    • SYT2 ,  MMP25 ,  TSLP ,  TMEM132E__1 ,  ALKBH6 ,  ...

  • 2 genes correlated to 'AGE'.

    • ACOT8 ,  SNX21__1

  • 5 genes correlated to 'GENDER'.

    • ATAD5 ,  CCNYL1 ,  NARFL ,  HNRNPD ,  PRKRIR

  • 104 genes correlated to 'DISTANT.METASTASIS'.

    • ERCC2 ,  NFE2L1 ,  PCDHA1__8 ,  PCDHA10__5 ,  PCDHA11__3 ,  ...

  • 234 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • TMEM132B ,  C5ORF39 ,  LOC153684 ,  DUS3L ,  HES2 ,  ...

  • 119 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • DLX6AS__1 ,  ZNF177 ,  GPR150 ,  INSM1 ,  NSD1 ,  ...

  • No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'

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=41 shorter survival N=40 longer survival N=1
AGE Spearman correlation test N=2 older N=0 younger N=2
GENDER t test N=5 male N=3 female N=2
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
DISTANT METASTASIS ANOVA test N=104        
LYMPH NODE METASTASIS ANOVA test N=234        
NEOPLASM DISEASESTAGE ANOVA test N=119        
Clinical variable #1: 'Time to Death'

41 genes related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0-182.7 (median=15.1)
  censored N = 66
  death N = 12
     
  Significant markers N = 41
  associated with shorter survival 40
  associated with longer survival 1
List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
SYT2 6201 2.382e-07 0.0048 0.834
MMP25 1001 3.001e-07 0.006 0.754
TSLP 150001 3.756e-07 0.0076 0.622
TMEM132E__1 47001 4.405e-07 0.0089 0.724
ALKBH6 261 8.546e-07 0.017 0.673
C19ORF46__1 261 8.546e-07 0.017 0.673
NPR1 1501 8.971e-07 0.018 0.812
VWA2 661 9.179e-07 0.018 0.829
NT5DC2__1 1901 9.906e-07 0.02 0.79
ELOVL5 0 1.063e-06 0.021 0.124

Figure S1.  Get High-res Image As an example, this figure shows the association of SYT2 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.38e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

Table S3.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 59.87 (13)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ACOT8 -0.5094 1.02e-06 0.0205
SNX21__1 -0.5094 1.02e-06 0.0205

Figure S2.  Get High-res Image As an example, this figure shows the association of ACOT8 to 'AGE'. P value = 1.02e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

5 genes related to 'GENDER'.

Table S5.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 28
  MALE 57
     
  Significant markers N = 5
  Higher in MALE 3
  Higher in FEMALE 2
List of 5 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 5 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
ATAD5 9.29 5.413e-12 1.09e-07 0.9217
CCNYL1 -9.05 4.003e-11 8.06e-07 0.9229
NARFL -6.73 2.259e-09 4.55e-05 0.8158
HNRNPD 7.49 6.551e-09 0.000132 0.9016
PRKRIR 6.8 4.631e-08 0.000932 0.9117

Figure S3.  Get High-res Image As an example, this figure shows the association of ATAD5 to 'GENDER'. P value = 5.41e-12 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 92 (13)
  Score N
  40 1
  90 10
  100 9
     
  Significant markers N = 0
Clinical variable #5: 'DISTANT.METASTASIS'

104 genes related to 'DISTANT.METASTASIS'.

Table S8.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 45
  M1 4
  MX 34
     
  Significant markers N = 104
List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
ERCC2 2.433e-10 4.9e-06
NFE2L1 6.964e-10 1.4e-05
PCDHA1__8 2.489e-09 5.01e-05
PCDHA10__5 2.489e-09 5.01e-05
PCDHA11__3 2.489e-09 5.01e-05
PCDHA12__3 2.489e-09 5.01e-05
PCDHA13__2 2.489e-09 5.01e-05
PCDHA2__8 2.489e-09 5.01e-05
PCDHA3__7 2.489e-09 5.01e-05
PCDHA4__6 2.489e-09 5.01e-05

Figure S4.  Get High-res Image As an example, this figure shows the association of ERCC2 to 'DISTANT.METASTASIS'. P value = 2.43e-10 with ANOVA analysis.

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

234 genes related to 'LYMPH.NODE.METASTASIS'.

Table S10.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 20
  N1 10
  N2 4
  NX 51
     
  Significant markers N = 234
List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
TMEM132B 5.476e-12 1.1e-07
C5ORF39 5.779e-11 1.16e-06
LOC153684 5.779e-11 1.16e-06
DUS3L 1.105e-10 2.22e-06
HES2 3.96e-10 7.97e-06
ZNF844 6.358e-10 1.28e-05
STX8__1 1.162e-09 2.34e-05
WDR16 1.162e-09 2.34e-05
ZNF177 1.315e-09 2.64e-05
NR2E1 1.639e-09 3.3e-05

Figure S5.  Get High-res Image As an example, this figure shows the association of TMEM132B to 'LYMPH.NODE.METASTASIS'. P value = 5.48e-12 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

119 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S12.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 47
  STAGE II 5
  STAGE III 23
  STAGE IV 8
     
  Significant markers N = 119
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

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

ANOVA_P Q
DLX6AS__1 3.395e-11 6.83e-07
ZNF177 5.437e-10 1.09e-05
GPR150 8.27e-10 1.66e-05
INSM1 2.648e-09 5.33e-05
NSD1 3.101e-09 6.24e-05
C2ORF55 3.185e-09 6.41e-05
NRN1 5.148e-09 0.000104
DIO3 6.111e-09 0.000123
DLEU2 1.087e-08 0.000219
TMEM132B 1.166e-08 0.000235

Figure S6.  Get High-res Image As an example, this figure shows the association of DLX6AS__1 to 'NEOPLASM.DISEASESTAGE'. P value = 3.4e-11 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = KIRP-TP.meth.by_min_expr_corr.data.txt

  • Clinical data file = KIRP-TP.clin.merged.picked.txt

  • Number of patients = 85

  • Number of genes = 20125

  • Number of clinical features = 7

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

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

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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