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 20234 genes and 9 clinical features across 283 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

  • 364 genes correlated to 'Time to Death'.

    • CSNK2A1 ,  DMRT1 ,  PRDM6 ,  PAX6 ,  IL31RA ,  ...

  • 58 genes correlated to 'AGE'.

    • ZFP64 ,  GSX1 ,  DRD5 ,  FOXG1 ,  SPDYA ,  ...

  • 51 genes correlated to 'GENDER'.

    • C10ORF99 ,  LRRC41 ,  UQCRH ,  SNORA48 ,  KIF4B ,  ...

  • 1157 genes correlated to 'PATHOLOGY.T'.

    • GSC2 ,  ZIC1 ,  FERD3L ,  BARHL2 ,  SOX1 ,  ...

  • 4 genes correlated to 'PATHOLOGY.N'.

    • LOC339535 ,  RECQL4 ,  RGS19 ,  GOSR1

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

    • C2ORF40 ,  ITGB6 ,  OPRK1 ,  SLC25A2 ,  TBX20 ,  ...

  • 1234 genes correlated to 'TUMOR.STAGE'.

    • GSC2 ,  TLX2 ,  ZIC1 ,  POU4F2 ,  NKX6-2 ,  ...

  • 60 genes correlated to 'NEOADJUVANT.THERAPY'.

    • DDR2 ,  GINS4 ,  EFHA2 ,  UQCRH ,  LCN12 ,  ...

  • 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=364 shorter survival N=268 longer survival N=96
AGE Spearman correlation test N=58 older N=58 younger N=0
GENDER t test N=51 male N=18 female N=33
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
PATHOLOGY T Spearman correlation test N=1157 higher pT N=808 lower pT N=349
PATHOLOGY N t test N=4 n1 N=2 n0 N=2
PATHOLOGICSPREAD(M) t test N=167 m1 N=165 m0 N=2
TUMOR STAGE Spearman correlation test N=1234 higher stage N=1016 lower stage N=218
NEOADJUVANT THERAPY t test N=60 yes N=23 no N=37
Clinical variable #1: 'Time to Death'

364 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 = 364
  associated with shorter survival 268
  associated with longer survival 96
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
CSNK2A1 300000001 2.697e-13 5.5e-09 0.714
DMRT1 3101 6.44e-11 1.3e-06 0.669
PRDM6 571 1.178e-10 2.4e-06 0.678
PAX6 111 1.656e-10 3.3e-06 0.677
IL31RA 0.01 2.209e-10 4.5e-06 0.316
CD300LB 0.01 3.696e-10 7.5e-06 0.335
BHLHE23 35 4.032e-10 8.2e-06 0.675
PTPN22 0.01 4.04e-10 8.2e-06 0.344
KCNJ3 5600001 4.143e-10 8.4e-06 0.615
IRF4 101 4.225e-10 8.5e-06 0.651

Figure S1.  Get High-res Image As an example, this figure shows the association of CSNK2A1 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.7e-13 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

58 genes related to 'AGE'.

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

AGE Mean (SD) 61.49 (12)
  Significant markers N = 58
  pos. correlated 58
  neg. correlated 0
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
ZFP64 0.3601 4.352e-10 8.81e-06
GSX1 0.3547 8.164e-10 1.65e-05
DRD5 0.3362 6.649e-09 0.000135
FOXG1 0.3359 6.838e-09 0.000138
SPDYA 0.3339 8.508e-09 0.000172
LHFPL4 0.3311 1.15e-08 0.000233
KCNS2 0.3308 1.182e-08 0.000239
CALB1 0.3184 4.342e-08 0.000878
SP8 0.3175 4.782e-08 0.000967
DHX40 0.3167 5.177e-08 0.00105

Figure S2.  Get High-res Image As an example, this figure shows the association of ZFP64 to 'AGE'. P value = 4.35e-10 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

51 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 96
  MALE 187
     
  Significant markers N = 51
  Higher in MALE 18
  Higher in FEMALE 33
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
C10ORF99 -12.44 3.657e-27 7.4e-23 0.8503
LRRC41 10.07 5.453e-20 1.1e-15 0.7435
UQCRH 9.66 1.047e-18 2.12e-14 0.7445
SNORA48 -9.57 7.952e-18 1.61e-13 0.815
KIF4B -9.12 6.467e-17 1.31e-12 0.8063
GLUD1 -8.93 1.578e-15 3.19e-11 0.8035
FAM35A -8.79 3.195e-15 6.46e-11 0.7909
C5ORF27 -7.92 4.032e-13 8.15e-09 0.7672
TUBB4 7.46 1.955e-12 3.96e-08 0.7809
NARFL -7.22 5.141e-12 1.04e-07 0.7175

Figure S3.  Get High-res Image As an example, this figure shows the association of C10ORF99 to 'GENDER'. P value = 3.66e-27 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: 'PATHOLOGY.T'

1157 genes related to 'PATHOLOGY.T'.

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

PATHOLOGY.T Mean (SD) 1.97 (0.98)
  N
  T1 132
  T2 36
  T3 107
  T4 8
     
  Significant markers N = 1157
  pos. correlated 808
  neg. correlated 349
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
GSC2 0.5286 8.992e-22 1.82e-17
ZIC1 0.5277 1.085e-21 2.19e-17
FERD3L 0.5253 1.792e-21 3.63e-17
BARHL2 0.5166 1.037e-20 2.1e-16
SOX1 0.5163 1.102e-20 2.23e-16
ZIC4 0.5156 1.281e-20 2.59e-16
DMRTA2 0.5146 1.535e-20 3.11e-16
NKX6-2 0.5141 1.718e-20 3.48e-16
KCNQ1DN 0.5077 6.052e-20 1.22e-15
TLX3 0.5023 1.714e-19 3.47e-15

Figure S4.  Get High-res Image As an example, this figure shows the association of GSC2 to 'PATHOLOGY.T'. P value = 8.99e-22 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

4 genes related to 'PATHOLOGY.N'.

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

PATHOLOGY.N Labels N
  N0 127
  N1 9
     
  Significant markers N = 4
  Higher in N1 2
  Higher in N0 2
List of 4 genes differentially expressed by 'PATHOLOGY.N'

Table S11.  Get Full Table List of 4 genes differentially expressed by 'PATHOLOGY.N'

T(pos if higher in 'N1') ttestP Q AUC
LOC339535 7.52 7.544e-08 0.00153 0.8285
RECQL4 5.9 2.878e-07 0.00582 0.6667
RGS19 -5.53 7.076e-07 0.0143 0.79
GOSR1 -4.98 2.457e-06 0.0497 0.6597

Figure S5.  Get High-res Image As an example, this figure shows the association of LOC339535 to 'PATHOLOGY.N'. P value = 7.54e-08 with T-test analysis.

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

167 genes related to 'PATHOLOGICSPREAD(M)'.

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

PATHOLOGICSPREAD(M) Labels N
  M0 232
  M1 51
     
  Significant markers N = 167
  Higher in M1 165
  Higher in M0 2
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
C2ORF40 7.3 1.626e-10 3.29e-06 0.7699
ITGB6 6.66 2.5e-10 5.06e-06 0.6754
OPRK1 6.67 1.98e-09 4.01e-05 0.7509
SLC25A2 6.35 2.086e-09 4.22e-05 0.6967
TBX20 6.8 2.183e-09 4.42e-05 0.7716
ZSCAN18 6.38 7.296e-09 0.000148 0.7394
TMEM155 6.61 7.448e-09 0.000151 0.7868
SPAG6 6.43 7.519e-09 0.000152 0.7393
COMP 6.39 1.269e-08 0.000257 0.7577
CRMP1 6.39 1.349e-08 0.000273 0.7657

Figure S6.  Get High-res Image As an example, this figure shows the association of C2ORF40 to 'PATHOLOGICSPREAD(M)'. P value = 1.63e-10 with T-test analysis.

Clinical variable #8: 'TUMOR.STAGE'

1234 genes related to 'TUMOR.STAGE'.

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

TUMOR.STAGE Mean (SD) 2.19 (1.2)
  N
  Stage 1 130
  Stage 2 24
  Stage 3 73
  Stage 4 56
     
  Significant markers N = 1234
  pos. correlated 1016
  neg. correlated 218
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
GSC2 0.5533 4.239e-24 8.58e-20
TLX2 0.5397 8.631e-23 1.75e-18
ZIC1 0.5352 2.261e-22 4.57e-18
POU4F2 0.5332 3.404e-22 6.89e-18
NKX6-2 0.5329 3.679e-22 7.44e-18
TLX3 0.5283 9.685e-22 1.96e-17
ZIC4 0.5271 1.229e-21 2.49e-17
FERD3L 0.5257 1.637e-21 3.31e-17
HTR1A 0.5257 1.647e-21 3.33e-17
SOX1 0.5251 1.857e-21 3.76e-17

Figure S7.  Get High-res Image As an example, this figure shows the association of GSC2 to 'TUMOR.STAGE'. P value = 4.24e-24 with Spearman correlation analysis.

Clinical variable #9: 'NEOADJUVANT.THERAPY'

60 genes related to 'NEOADJUVANT.THERAPY'.

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

NEOADJUVANT.THERAPY Labels N
  NO 4
  YES 279
     
  Significant markers N = 60
  Higher in YES 23
  Higher in NO 37
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
DDR2 -17.19 8.492e-24 1.72e-19 0.9409
GINS4 16.44 9.478e-22 1.92e-17 0.8683
EFHA2 10.15 2.654e-17 5.37e-13 0.6398
UQCRH 9.03 3.552e-16 7.19e-12 0.5296
LCN12 -10.36 2.107e-14 4.26e-10 0.8656
DUSP23 -8.08 2.632e-14 5.32e-10 0.7545
OR10R2 -9.44 6.043e-14 1.22e-09 0.6389
OR5H15 -11.92 9.727e-14 1.97e-09 0.7751
MIR543 -8.28 5.828e-13 1.18e-08 0.7814
LOC154822 -9.27 1.99e-12 4.02e-08 0.8244

Figure S8.  Get High-res Image As an example, this figure shows the association of DDR2 to 'NEOADJUVANT.THERAPY'. P value = 8.49e-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 = 283

  • Number of genes = 20234

  • 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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