Correlation between gene methylation status and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C10V8BFP
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/C10V8BFP
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 19843 genes and 10 clinical features across 147 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 758 genes correlated to 'Time to Death'.

    • LRRC43 ,  CACNA1C ,  ZNF853 ,  ZNF334 ,  CCNA1 ,  ...

  • 3 genes correlated to 'AGE'.

    • ACOT8__1 ,  SNX21__1 ,  ARHGAP1

  • 561 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • OTX1 ,  GPR150 ,  DLX6 ,  DLX6AS ,  C10ORF114 ,  ...

  • 261 genes correlated to 'PATHOLOGY.T.STAGE'.

    • OTX1 ,  DLX6 ,  DLX6AS ,  CDO1 ,  LEFTY2 ,  ...

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

    • LRRC26 ,  ID2B ,  PTPRG__1 ,  C7ORF36 ,  BMS1P4 ,  ...

  • 19 genes correlated to 'GENDER'.

    • NARFL ,  PRKRIR ,  PEMT ,  ALG11__2 ,  UTP14C ,  ...

  • 1 gene correlated to 'NUMBERPACKYEARSSMOKED'.

    • BRAF

  • No genes correlated to 'PATHOLOGY.N.STAGE', 'KARNOFSKY.PERFORMANCE.SCORE', and 'YEAROFTOBACCOSMOKINGONSET'.

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=758 shorter survival N=677 longer survival N=81
AGE Spearman correlation test N=3 older N=0 younger N=3
NEOPLASM DISEASESTAGE ANOVA test N=561        
PATHOLOGY T STAGE Spearman correlation test N=261 higher stage N=228 lower stage N=33
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=320        
GENDER t test N=19 male N=6 female N=13
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test N=1 higher numberpackyearssmoked N=0 lower numberpackyearssmoked N=1
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

758 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-194.8 (median=13.2)
  censored N = 119
  death N = 15
     
  Significant markers N = 758
  associated with shorter survival 677
  associated with longer survival 81
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
LRRC43 241 1.446e-09 2.9e-05 0.81
CACNA1C 0 1.616e-09 3.2e-05 0.145
ZNF853 16001 1.666e-09 3.3e-05 0.929
ZNF334 721 1.725e-09 3.4e-05 0.839
CCNA1 9901 2.086e-09 4.1e-05 0.928
FBN2 311 2.311e-09 4.6e-05 0.849
KIAA1409 5301 2.739e-09 5.4e-05 0.836
LOC440563 0 2.888e-09 5.7e-05 0.14
C6ORF174__1 321 3.408e-09 6.8e-05 0.876
ZNF709 1200001 4.175e-09 8.3e-05 0.875

Figure S1.  Get High-res Image As an example, this figure shows the association of LRRC43 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.45e-09 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

3 genes related to 'AGE'.

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

AGE Mean (SD) 59.92 (13)
  Significant markers N = 3
  pos. correlated 0
  neg. correlated 3
List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ACOT8__1 -0.3981 1.012e-06 0.0201
SNX21__1 -0.3981 1.012e-06 0.0201
ARHGAP1 -0.3911 1.625e-06 0.0323

Figure S2.  Get High-res Image As an example, this figure shows the association of ACOT8__1 to 'AGE'. P value = 1.01e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

561 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S5.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 90
  STAGE II 8
  STAGE III 36
  STAGE IV 9
     
  Significant markers N = 561
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
OTX1 1.724e-15 3.42e-11
GPR150 8.573e-13 1.7e-08
DLX6 8.634e-13 1.71e-08
DLX6AS 8.634e-13 1.71e-08
C10ORF114 2.928e-12 5.81e-08
DLEU2 3.658e-12 7.26e-08
PCDHA1__11 4.277e-12 8.49e-08
PCDHA10__5 4.277e-12 8.49e-08
PCDHA11__3 4.277e-12 8.49e-08
PCDHA12__3 4.277e-12 8.49e-08

Figure S3.  Get High-res Image As an example, this figure shows the association of OTX1 to 'NEOPLASM.DISEASESTAGE'. P value = 1.72e-15 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

261 genes related to 'PATHOLOGY.T.STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.65 (0.91)
  N
  1 95
  2 10
  3 41
  4 1
     
  Significant markers N = 261
  pos. correlated 228
  neg. correlated 33
List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
OTX1 0.5539 3.393e-13 6.73e-09
DLX6 0.5462 8.358e-13 1.66e-08
DLX6AS 0.5462 8.358e-13 1.66e-08
CDO1 0.5212 1.304e-11 2.59e-07
LEFTY2 0.507 5.632e-11 1.12e-06
NSD1 0.5023 9.045e-11 1.79e-06
GPR150 0.4978 1.414e-10 2.8e-06
FLOT2 0.4953 1.797e-10 3.56e-06
ZNF154 0.4872 3.9e-10 7.74e-06
SH2D3A 0.4848 4.904e-10 9.73e-06

Figure S4.  Get High-res Image As an example, this figure shows the association of OTX1 to 'PATHOLOGY.T.STAGE'. P value = 3.39e-13 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

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

PATHOLOGY.N.STAGE Mean (SD) 0.52 (0.66)
  N
  0 25
  1 15
  2 4
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 52
  M1 5
  MX 82
     
  Significant markers N = 320
List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
LRRC26 3.077e-14 6.11e-10
ID2B 3.663e-13 7.27e-09
PTPRG__1 3.663e-13 7.27e-09
C7ORF36 1.163e-11 2.31e-07
BMS1P4 1.222e-11 2.42e-07
PCDHA1__6 2.902e-11 5.76e-07
PCDHA10__3 2.902e-11 5.76e-07
PCDHA11__2 2.902e-11 5.76e-07
PCDHA12__2 2.902e-11 5.76e-07
PCDHA13__1 2.902e-11 5.76e-07

Figure S5.  Get High-res Image As an example, this figure shows the association of LRRC26 to 'PATHOLOGY.M.STAGE'. P value = 3.08e-14 with ANOVA analysis.

Clinical variable #7: 'GENDER'

19 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 45
  MALE 102
     
  Significant markers N = 19
  Higher in MALE 6
  Higher in FEMALE 13
List of top 10 genes differentially expressed by 'GENDER'

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

T(pos if higher in 'MALE') ttestP Q AUC
NARFL -8.85 5.363e-15 1.06e-10 0.8098
PRKRIR 9.25 5.617e-13 1.11e-08 0.9176
PEMT -7.03 2.729e-10 5.41e-06 0.8059
ALG11__2 7.77 7.081e-10 1.4e-05 0.9357
UTP14C 7.77 7.081e-10 1.4e-05 0.9357
MCF2L -6.55 1.62e-09 3.21e-05 0.771
WBP11P1 6.66 7.041e-09 0.00014 0.8327
FDPS 6.69 8.572e-09 0.00017 0.8739
RUSC1__1 6.69 8.572e-09 0.00017 0.8739
C5ORF27 -6.07 2.16e-08 0.000428 0.7699

Figure S6.  Get High-res Image As an example, this figure shows the association of NARFL to 'GENDER'. P value = 5.36e-15 with T-test analysis.

Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S14.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 91.05 (11)
  Score N
  40 1
  70 1
  80 4
  90 17
  100 15
     
  Significant markers N = 0
Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

One gene related to 'NUMBERPACKYEARSSMOKED'.

Table S15.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 36.78 (56)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S16.  Get Full Table List of one gene significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
BRAF -0.9958 1.542e-08 0.000306

Figure S7.  Get High-res Image As an example, this figure shows the association of BRAF to 'NUMBERPACKYEARSSMOKED'. P value = 1.54e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #10: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S17.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1978.6 (21)
  Value N
  1951 1
  1960 1
  1991 1
  1993 1
  1998 1
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 147

  • Number of genes = 19843

  • Number of clinical features = 10

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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