Correlation between gene methylation status and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1PN93N0
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/C1PN93N0
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 20182 genes and 7 clinical features across 283 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 415 genes correlated to 'Time to Death'.

    • FLJ42289 ,  RIOK3 ,  TLL2 ,  RPRD2 ,  IGLL1 ,  ...

  • 19 genes correlated to 'AGE'.

    • ELOVL2 ,  MRPS33 ,  TSPYL5 ,  DOK6 ,  ZYG11A ,  ...

  • 97 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  KIF4B ,  CCDC146__1 ,  DNAJB13 ,  ...

  • 81 genes correlated to 'DISTANT.METASTASIS'.

    • C20ORF112 ,  OPRK1 ,  HTR6 ,  PLCD1 ,  MUSK ,  ...

  • 1 gene correlated to 'LYMPH.NODE.METASTASIS'.

    • LIN7B

  • 554 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • KDR ,  OPRK1 ,  FAM38B ,  AVPR1A ,  CLEC2L ,  ...

  • 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=415 shorter survival N=240 longer survival N=175
AGE Spearman correlation test N=19 older N=15 younger N=4
GENDER t test N=97 male N=11 female N=86
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
DISTANT METASTASIS t test N=81 m1 N=73 m0 N=8
LYMPH NODE METASTASIS ANOVA test N=1        
NEOPLASM DISEASESTAGE ANOVA test N=554        
Clinical variable #1: 'Time to Death'

415 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0.1-109.9 (median=28.6)
  censored N = 186
  death N = 94
     
  Significant markers N = 415
  associated with shorter survival 240
  associated with longer survival 175
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
FLJ42289 0.03 1.546e-12 3.1e-08 0.303
RIOK3 10001 2.126e-12 4.3e-08 0.674
TLL2 0.02 3.526e-12 7.1e-08 0.315
RPRD2 56 1.447e-11 2.9e-07 0.681
IGLL1 0.01 4.475e-11 9e-07 0.309
PLCB3 0 6.929e-11 1.4e-06 0.374
CCL26 0.06 8.598e-11 1.7e-06 0.353
CLEC2L 16 1.139e-10 2.3e-06 0.675
ARHGEF12 40 1.166e-10 2.4e-06 0.64
EVI2A 0.04 1.253e-10 2.5e-06 0.346

Figure S1.  Get High-res Image As an example, this figure shows the association of FLJ42289 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.55e-12 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

19 genes related to 'AGE'.

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

AGE Mean (SD) 61.49 (12)
  Significant markers N = 19
  pos. correlated 15
  neg. correlated 4
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ELOVL2 0.4668 1.021e-16 2.06e-12
MRPS33 0.3343 8.161e-09 0.000165
TSPYL5 0.3272 1.74e-08 0.000351
DOK6 0.322 3.012e-08 0.000608
ZYG11A 0.3187 4.237e-08 0.000855
ME3 -0.3138 6.93e-08 0.0014
PVT1 -0.3102 9.963e-08 0.00201
RANBP17 0.309 1.123e-07 0.00226
ADAMTS17 0.3002 2.651e-07 0.00535
SLC10A4 0.299 2.969e-07 0.00599

Figure S2.  Get High-res Image As an example, this figure shows the association of ELOVL2 to 'AGE'. P value = 1.02e-16 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

97 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 96
  MALE 187
     
  Significant markers N = 97
  Higher in MALE 11
  Higher in FEMALE 86
List of top 10 genes differentially expressed by 'GENDER'

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

T(pos if higher in 'MALE') ttestP Q AUC
ALG11__1 18.74 8.723e-35 1.76e-30 0.9806
UTP14C 18.74 8.723e-35 1.76e-30 0.9806
KIF4B -12.12 6.544e-26 1.32e-21 0.8809
CCDC146__1 -10.93 3.001e-23 6.06e-19 0.8112
DNAJB13 -10.18 9e-21 1.82e-16 0.7948
C5ORF27 -10.31 1.602e-20 3.23e-16 0.8155
LRRC41 10.24 2.051e-20 4.14e-16 0.7638
UQCRH 10.24 2.051e-20 4.14e-16 0.7638
CAV2 -9.78 4.191e-19 8.46e-15 0.7968
TLE1 -10.01 6.611e-19 1.33e-14 0.8158

Figure S3.  Get High-res Image As an example, this figure shows the association of ALG11__1 to 'GENDER'. P value = 8.72e-35 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) 92.5 (8)
  Score N
  70 1
  80 3
  90 12
  100 12
     
  Significant markers N = 0
Clinical variable #5: 'DISTANT.METASTASIS'

81 genes related to 'DISTANT.METASTASIS'.

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

DISTANT.METASTASIS Labels N
  M0 232
  M1 51
     
  Significant markers N = 81
  Higher in M1 73
  Higher in M0 8
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'

T(pos if higher in 'M1') ttestP Q AUC
C20ORF112 7.64 2.497e-12 5.04e-08 0.7658
OPRK1 7.41 6.057e-11 1.22e-06 0.7618
HTR6 7.36 1.229e-10 2.48e-06 0.7727
PLCD1 6.55 6.293e-10 1.27e-05 0.7116
MUSK 6.28 4.971e-09 1e-04 0.7143
SESN1__1 6.21 6.506e-09 0.000131 0.7059
STK24 6.38 6.719e-09 0.000136 0.7535
ASB4 6.09 1.01e-08 0.000204 0.695
PDGFB 6.07 1.061e-08 0.000214 0.7147
HAND2__1 6.25 1.185e-08 0.000239 0.7159

Figure S4.  Get High-res Image As an example, this figure shows the association of C20ORF112 to 'DISTANT.METASTASIS'. P value = 2.5e-12 with T-test analysis.

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

One gene related to 'LYMPH.NODE.METASTASIS'.

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

LYMPH.NODE.METASTASIS Labels N
  N0 127
  N1 9
  NX 147
     
  Significant markers N = 1
List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S11.  Get Full Table List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
LIN7B 1.884e-06 0.038

Figure S5.  Get High-res Image As an example, this figure shows the association of LIN7B to 'LYMPH.NODE.METASTASIS'. P value = 1.88e-06 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

554 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 129
  STAGE II 27
  STAGE III 74
  STAGE IV 53
     
  Significant markers N = 554
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
KDR 3.06e-20 6.17e-16
OPRK1 1.202e-18 2.43e-14
FAM38B 2.691e-16 5.43e-12
AVPR1A 3.051e-16 6.16e-12
CLEC2L 8.466e-15 1.71e-10
PCDHGA1__5 3.72e-14 7.51e-10
PCDHGA10__3 3.72e-14 7.51e-10
PCDHGA11__2 3.72e-14 7.51e-10
PCDHGA2__5 3.72e-14 7.51e-10
PCDHGA3__5 3.72e-14 7.51e-10

Figure S6.  Get High-res Image As an example, this figure shows the association of KDR to 'NEOPLASM.DISEASESTAGE'. P value = 3.06e-20 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 283

  • Number of genes = 20182

  • 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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