Correlation between gene methylation status and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1NK3CN1
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/C1NK3CN1
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 20021 genes and 11 clinical features across 198 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • PLVAP

  • 88 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • ERCC3 ,  C7ORF63 ,  CCND1 ,  DYX1C1 ,  LCMT1 ,  ...

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

    • HSH2D ,  NR1D1 ,  THRA ,  LRP1 ,  PRPF6 ,  ...

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

    • RBMS3 ,  SLC2A12 ,  TLR4 ,  NRGN ,  NBPF3 ,  ...

  • 15 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  KIF4B ,  DDX55 ,  GRHL1 ,  ...

  • 1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • ARL3

  • 5 genes correlated to 'NUMBER.OF.LYMPH.NODES'.

    • CDK2 ,  SILV ,  PHLDB3 ,  CORO7 ,  VASN

  • No genes correlated to 'Time to Death', 'PATHOLOGY.N.STAGE', 'NUMBERPACKYEARSSMOKED', and 'GLEASON_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=0        
AGE Spearman correlation test N=1 older N=0 younger N=1
NEOPLASM DISEASESTAGE ANOVA test N=88        
PATHOLOGY T STAGE Spearman correlation test N=32 higher stage N=10 lower stage N=22
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=26        
GENDER t test N=15 male N=4 female N=11
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=1 lower score N=0
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test N=5 higher number.of.lymph.nodes N=0 lower number.of.lymph.nodes N=5
GLEASON_SCORE Spearman correlation 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) 0.1-140.8 (median=8.2)
  censored N = 133
  death N = 58
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 67.51 (11)
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'AGE' by Spearman correlation test

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

SpearmanCorr corrP Q
PLVAP -0.3318 1.911e-06 0.0383

Figure S1.  Get High-res Image As an example, this figure shows the association of PLVAP to 'AGE'. P value = 1.91e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

88 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 2
  STAGE II 66
  STAGE III 67
  STAGE IV 59
     
  Significant markers N = 88
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
ERCC3 1.264e-26 2.53e-22
C7ORF63 1.577e-25 3.16e-21
CCND1 1.809e-19 3.62e-15
DYX1C1 5.472e-19 1.1e-14
LCMT1 1.798e-18 3.6e-14
CCDC62 8.098e-18 1.62e-13
TBKBP1 3.983e-17 7.97e-13
MINPP1 1.07e-14 2.14e-10
SMOX 2.058e-14 4.12e-10
IFRD2 3.547e-13 7.1e-09

Figure S2.  Get High-res Image As an example, this figure shows the association of ERCC3 to 'NEOPLASM.DISEASESTAGE'. P value = 1.26e-26 with ANOVA analysis.

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

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

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.82 (0.72)
  N
  0 1
  1 1
  2 57
  3 91
  4 29
     
  Significant markers N = 32
  pos. correlated 10
  neg. correlated 22
List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S7.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSH2D 0.3708 3.224e-07 0.00646
NR1D1 -0.3683 3.935e-07 0.00788
THRA -0.3683 3.935e-07 0.00788
LRP1 -0.3672 4.262e-07 0.00853
PRPF6 0.3628 6.013e-07 0.012
SAMD10 0.3628 6.013e-07 0.012
TBX4 -0.3612 6.774e-07 0.0136
C2ORF70 0.3602 7.335e-07 0.0147
BHMT2 -0.3566 9.574e-07 0.0192
DMGDH -0.3566 9.574e-07 0.0192

Figure S3.  Get High-res Image As an example, this figure shows the association of HSH2D to 'PATHOLOGY.T.STAGE'. P value = 3.22e-07 with Spearman correlation analysis.

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

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

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

PATHOLOGY.N.STAGE Mean (SD) 0.59 (0.92)
  N
  0 122
  1 18
  2 34
  3 7
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

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

PATHOLOGY.M.STAGE Labels N
  M0 106
  M1 5
  MX 86
     
  Significant markers N = 26
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
RBMS3 1.257e-12 2.52e-08
SLC2A12 8.443e-09 0.000169
TLR4 9.12e-09 0.000183
NRGN 2.157e-08 0.000432
NBPF3 2.314e-08 0.000463
LRRC45__2 3.925e-08 0.000786
STRA13__1 3.925e-08 0.000786
SP100 6.278e-08 0.00126
ACSM3 7.263e-08 0.00145
TFCP2 1.216e-07 0.00243

Figure S4.  Get High-res Image As an example, this figure shows the association of RBMS3 to 'PATHOLOGY.M.STAGE'. P value = 1.26e-12 with ANOVA analysis.

Clinical variable #7: 'GENDER'

15 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 49
  MALE 149
     
  Significant markers N = 15
  Higher in MALE 4
  Higher in FEMALE 11
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 9.93 2.572e-13 5.15e-09 0.9585
UTP14C 9.93 2.572e-13 5.15e-09 0.9585
KIF4B -6.26 2.265e-08 0.000453 0.7803
DDX55 5.43 1.668e-07 0.00334 0.705
GRHL1 -5.33 2.853e-07 0.00571 0.6044
HIST1H4K 5.33 3.199e-07 0.0064 0.6264
ALDH3B2 -5.37 5.939e-07 0.0119 0.7237
TP53INP2 -5.08 9.461e-07 0.0189 0.6777
TNKS1BP1 -5.09 1.039e-06 0.0208 0.677
ACBD5 -5.24 1.116e-06 0.0223 0.7289

Figure S5.  Get High-res Image As an example, this figure shows the association of ALG11__1 to 'GENDER'. P value = 2.57e-13 with T-test analysis.

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

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S13.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 78.91 (16)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S14.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
ARL3 0.6599 4.261e-08 0.000853

Figure S6.  Get High-res Image As an example, this figure shows the association of ARL3 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 4.26e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 37.72 (25)
  Significant markers N = 0
Clinical variable #10: 'NUMBER.OF.LYMPH.NODES'

5 genes related to 'NUMBER.OF.LYMPH.NODES'.

Table S16.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 1.51 (3.3)
  Significant markers N = 5
  pos. correlated 0
  neg. correlated 5
List of 5 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S17.  Get Full Table List of 5 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
CDK2 -0.4084 3.414e-07 0.00684
SILV -0.4084 3.414e-07 0.00684
PHLDB3 -0.4044 4.535e-07 0.00908
CORO7 -0.3813 2.226e-06 0.0446
VASN -0.3813 2.226e-06 0.0446

Figure S7.  Get High-res Image As an example, this figure shows the association of CDK2 to 'NUMBER.OF.LYMPH.NODES'. P value = 3.41e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #11: 'GLEASON_SCORE'

No gene related to 'GLEASON_SCORE'.

Table S18.  Basic characteristics of clinical feature: 'GLEASON_SCORE'

GLEASON_SCORE Mean (SD) 6.43 (0.67)
  Score N
  6 27
  7 13
  8 1
  9 1
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 198

  • Number of genes = 20021

  • 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)
Copyright © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle