Kidney Renal Clear Cell Carcinoma: Correlation between gene methylation status and clinical features
Maintained by Juok Cho (Broad Institute)
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 17380 genes and 9 clinical features across 263 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

  • 334 genes correlated to 'Time to Death'.

    • PPP3CB ,  RPRD2 ,  RIOK3 ,  ARHGEF12 ,  PTK2B ,  ...

  • 16 genes correlated to 'AGE'.

    • ELOVL2 ,  MRPS33 ,  UNC80 ,  DOK6 ,  TSPYL5 ,  ...

  • 86 genes correlated to 'GENDER'.

    • UTP14C ,  KIF4B ,  CCDC146 ,  CAV2 ,  UQCRH ,  ...

  • 621 genes correlated to 'PATHOLOGY.T'.

    • KDR ,  DBX2 ,  ACTA1 ,  OPRK1 ,  NR5A1 ,  ...

  • 8 genes correlated to 'PATHOLOGY.N'.

    • CARD16 ,  CASP1 ,  SFXN5 ,  LOC339535 ,  VGF ,  ...

  • 46 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • CMTM8 ,  C20ORF112 ,  OPRK1 ,  HTR6 ,  PLCD1 ,  ...

  • 648 genes correlated to 'TUMOR.STAGE'.

    • KDR ,  ACTA1 ,  OPRK1 ,  DBX2 ,  FAM38B ,  ...

  • 130 genes correlated to 'NEOADJUVANT.THERAPY'.

    • ALDOB ,  NKIRAS2 ,  PLXNB2 ,  MREG ,  UQCRH ,  ...

  • 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=334 shorter survival N=203 longer survival N=131
AGE Spearman correlation test N=16 older N=14 younger N=2
GENDER t test N=86 male N=8 female N=78
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
PATHOLOGY T Spearman correlation test N=621 higher pT N=314 lower pT N=307
PATHOLOGY N t test N=8 n1 N=1 n0 N=7
PATHOLOGICSPREAD(M) t test N=46 m1 N=43 m0 N=3
TUMOR STAGE Spearman correlation test N=648 higher stage N=418 lower stage N=230
NEOADJUVANT THERAPY t test N=130 yes N=51 no N=79
Clinical variable #1: 'Time to Death'

334 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=28.3)
  censored N = 169
  death N = 91
     
  Significant markers N = 334
  associated with shorter survival 203
  associated with longer survival 131
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
PPP3CB 0 2.657e-11 4.6e-07 0.332
RPRD2 50 7.839e-11 1.4e-06 0.676
RIOK3 6001 1.019e-10 1.8e-06 0.661
ARHGEF12 38 2.288e-10 4e-06 0.64
PTK2B 0 3.038e-10 5.3e-06 0.327
IGLL1 0.01 4.758e-10 8.3e-06 0.315
MBNL2 26 5.475e-10 9.5e-06 0.666
EVI2A 0.04 5.908e-10 1e-05 0.352
CCL26 0.07 6e-10 1e-05 0.358
SALL1 10.1 9.264e-10 1.6e-05 0.601

Figure S1.  Get High-res Image As an example, this figure shows the association of PPP3CB to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.66e-11 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

16 genes related to 'AGE'.

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

AGE Mean (SD) 61.45 (12)
  Significant markers N = 16
  pos. correlated 14
  neg. correlated 2
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 0.4639 1.942e-15 3.38e-11
MRPS33 0.3348 2.626e-08 0.000456
UNC80 0.3241 7.597e-08 0.00132
DOK6 0.3232 8.284e-08 0.00144
TSPYL5 0.3108 2.681e-07 0.00466
RANBP17 0.3102 2.837e-07 0.00493
ZYG11A 0.3056 4.319e-07 0.0075
C7ORF13 0.3039 5.048e-07 0.00877
RNF32 0.3039 5.048e-07 0.00877
PVT1 -0.3001 7.108e-07 0.0123

Figure S2.  Get High-res Image As an example, this figure shows the association of ELOVL2 to 'AGE'. P value = 1.94e-15 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

86 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 89
  MALE 174
     
  Significant markers N = 86
  Higher in MALE 8
  Higher in FEMALE 78
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
UTP14C 17.16 3.173e-32 5.52e-28 0.9707
KIF4B -11.32 3.408e-23 5.92e-19 0.875
CCDC146 -10.37 3.088e-21 5.37e-17 0.8014
CAV2 -9.9 2.448e-19 4.25e-15 0.8063
UQCRH 9.84 6.178e-19 1.07e-14 0.7634
DNAJB13 -9.53 1.652e-18 2.87e-14 0.7841
SNORA48 -9.2 1.193e-16 2.07e-12 0.8144
TLE1 -9.1 3.69e-16 6.41e-12 0.8029
RERG -8.38 7.736e-15 1.34e-10 0.7701
ADAMTS10 -8.1 2.187e-14 3.8e-10 0.733

Figure S3.  Get High-res Image As an example, this figure shows the association of UTP14C to 'GENDER'. P value = 3.17e-32 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) 92.92 (8.6)
  Score N
  70 1
  80 3
  90 8
  100 12
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.T'

621 genes related to 'PATHOLOGY.T'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.99 (0.99)
  N
  T1 121
  T2 32
  T3 102
  T4 8
     
  Significant markers N = 621
  pos. correlated 314
  neg. correlated 307
List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S9.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
KDR 0.4995 5.251e-18 9.13e-14
DBX2 0.4605 3.275e-15 5.69e-11
ACTA1 0.456 6.593e-15 1.15e-10
OPRK1 0.452 1.196e-14 2.08e-10
NR5A1 0.436 1.251e-13 2.17e-09
SLC35F1 0.4346 1.528e-13 2.66e-09
AVPR1A 0.4299 2.97e-13 5.16e-09
RRM2 -0.4298 3.015e-13 5.24e-09
FAM38B 0.4207 1.047e-12 1.82e-08
DLL3 0.4205 1.08e-12 1.88e-08

Figure S4.  Get High-res Image As an example, this figure shows the association of KDR to 'PATHOLOGY.T'. P value = 5.25e-18 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

8 genes related to 'PATHOLOGY.N'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Labels N
  N0 118
  N1 9
     
  Significant markers N = 8
  Higher in N1 1
  Higher in N0 7
List of 8 genes differentially expressed by 'PATHOLOGY.N'

Table S11.  Get Full Table List of 8 genes differentially expressed by 'PATHOLOGY.N'

T(pos if higher in 'N1') ttestP Q AUC
CARD16 -6.25 1.428e-08 0.000248 0.6638
CASP1 -6.25 1.428e-08 0.000248 0.6638
SFXN5 -6.21 3.299e-07 0.00573 0.7109
LOC339535 6.16 7.641e-07 0.0133 0.7881
VGF -5.16 9.519e-07 0.0165 0.6544
ALKBH2 -5.09 1.458e-06 0.0253 0.6704
SYNCRIP -5.02 2.001e-06 0.0348 0.7354
PLAG1 -4.91 2.814e-06 0.0489 0.7702

Figure S5.  Get High-res Image As an example, this figure shows the association of CARD16 to 'PATHOLOGY.N'. P value = 1.43e-08 with T-test analysis.

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

46 genes related to 'PATHOLOGICSPREAD(M)'.

Table S12.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 213
  M1 50
     
  Significant markers N = 46
  Higher in M1 43
  Higher in M0 3
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

T(pos if higher in 'M1') ttestP Q AUC
CMTM8 7.28 1.127e-11 1.96e-07 0.7229
C20ORF112 7.2 2.774e-11 4.82e-07 0.7599
OPRK1 7.21 1.558e-10 2.71e-06 0.7607
HTR6 6.94 8.291e-10 1.44e-05 0.7642
PLCD1 6.33 1.963e-09 3.41e-05 0.7057
SESN1 6.03 1.465e-08 0.000255 0.7013
GFPT1 5.96 2.322e-08 0.000403 0.6902
MUSK 5.95 2.423e-08 0.000421 0.7065
STK24 6.09 2.51e-08 0.000436 0.7486
ASB4 5.98 3.053e-08 0.00053 0.7233

Figure S6.  Get High-res Image As an example, this figure shows the association of CMTM8 to 'PATHOLOGICSPREAD(M)'. P value = 1.13e-11 with T-test analysis.

Clinical variable #8: 'TUMOR.STAGE'

648 genes related to 'TUMOR.STAGE'.

Table S14.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.22 (1.2)
  N
  Stage 1 119
  Stage 2 21
  Stage 3 68
  Stage 4 55
     
  Significant markers N = 648
  pos. correlated 418
  neg. correlated 230
List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S15.  Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
KDR 0.5203 1.214e-19 2.11e-15
ACTA1 0.4863 5.134e-17 8.92e-13
OPRK1 0.4738 3.991e-16 6.93e-12
DBX2 0.4657 1.47e-15 2.56e-11
FAM38B 0.4601 3.481e-15 6.05e-11
NR5A1 0.4537 9.253e-15 1.61e-10
DLL3 0.4516 1.267e-14 2.2e-10
AVPR1A 0.4515 1.287e-14 2.24e-10
NEUROD2 0.4464 2.764e-14 4.8e-10
RRM2 -0.4402 6.83e-14 1.19e-09

Figure S7.  Get High-res Image As an example, this figure shows the association of KDR to 'TUMOR.STAGE'. P value = 1.21e-19 with Spearman correlation analysis.

Clinical variable #9: 'NEOADJUVANT.THERAPY'

130 genes related to 'NEOADJUVANT.THERAPY'.

Table S16.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 4
  YES 259
     
  Significant markers N = 130
  Higher in YES 51
  Higher in NO 79
List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
ALDOB -12.78 1.119e-24 1.94e-20 0.8243
NKIRAS2 12.69 2.189e-23 3.8e-19 0.8909
PLXNB2 11.55 6.458e-22 1.12e-17 0.7761
MREG 11.5 6.338e-20 1.1e-15 0.7597
UQCRH 9.8 1.679e-19 2.91e-15 0.6467
RDX 20.73 8.317e-19 1.44e-14 0.9479
METTL3 -17.65 1.49e-18 2.59e-14 0.8983
KIAA0114 8.87 5.748e-16 9.98e-12 0.7191
GRIN1 -10.33 6.6e-16 1.15e-11 0.8485
GTF2A1L -8.6 7.988e-16 1.39e-11 0.6525

Figure S8.  Get High-res Image As an example, this figure shows the association of ALDOB to 'NEOADJUVANT.THERAPY'. P value = 1.12e-24 with T-test analysis.

Methods & Data
Input

  • Expresson data file = KIRC.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 263

  • Number of genes = 17380

  • Number of clinical features = 9

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

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] 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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