Correlation between gene methylation status and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1S46PWS
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Kidney Renal Papillary Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1S46PWS
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 17247 genes and 8 clinical features across 59 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • IGF2BP2

  • 4 genes correlated to 'GENDER'.

    • NARFL ,  ATAD5 ,  CCNYL1 ,  HNRNPD

  • 51 genes correlated to 'PATHOLOGY.T'.

    • DLX6AS ,  GNASAS ,  GP2 ,  GPR150 ,  SLC2A14 ,  ...

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

    • RFX7 ,  GDPD4 ,  ERCC2 ,  DHDH ,  UNC93A ,  ...

  • 38 genes correlated to 'TUMOR.STAGE'.

    • INSM1 ,  ZNF177 ,  DLX6AS ,  MATK ,  NSD1 ,  ...

  • No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', and 'PATHOLOGY.N'.

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=1 younger N=0
GENDER t test N=4 male N=2 female N=2
KARNOFSKY PERFORMANCE SCORE t test   N=0        
PATHOLOGY T Spearman correlation test N=51 higher pT N=36 lower pT N=15
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=25        
TUMOR STAGE Spearman correlation test N=38 higher stage N=32 lower stage N=6
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) 1-182.7 (median=20.9)
  censored N = 45
  death N = 11
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 60.18 (14)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
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
IGF2BP2 0.6161 4.32e-07 0.00745

Figure S1.  Get High-res Image As an example, this figure shows the association of IGF2BP2 to 'AGE'. P value = 4.32e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

4 genes related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 19
  MALE 40
     
  Significant markers N = 4
  Higher in MALE 2
  Higher in FEMALE 2
List of 4 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 4 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
NARFL -7.33 9.043e-10 1.56e-05 0.8921
ATAD5 6.82 1.719e-07 0.00296 0.8947
CCNYL1 -6.43 6.931e-07 0.012 0.8908
HNRNPD 6.38 1.327e-06 0.0229 0.9171

Figure S2.  Get High-res Image As an example, this figure shows the association of NARFL to 'GENDER'. P value = 9.04e-10 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Labels N
  class100 5
  class90 6
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.T'

51 genes related to 'PATHOLOGY.T'.

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

PATHOLOGY.T Mean (SD) 1.93 (1)
  N
  T1 31
  T2 2
  T3 25
  T4 1
     
  Significant markers N = 51
  pos. correlated 36
  neg. correlated 15
List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

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

SpearmanCorr corrP Q
DLX6AS 0.7006 6.54e-10 1.13e-05
GNASAS -0.6969 8.771e-10 1.51e-05
GP2 -0.6568 1.616e-08 0.000279
GPR150 0.6486 2.779e-08 0.000479
SLC2A14 0.6419 4.276e-08 0.000737
DLEU2 -0.6397 4.932e-08 0.00085
PCDHB19P 0.6289 9.587e-08 0.00165
C2ORF55 0.6285 9.86e-08 0.0017
RIMS3 0.6237 1.316e-07 0.00227
ZNF177 0.6231 1.364e-07 0.00235

Figure S3.  Get High-res Image As an example, this figure shows the association of DLX6AS to 'PATHOLOGY.T'. P value = 6.54e-10 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

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

PATHOLOGY.N Mean (SD) 0.57 (0.69)
  N
  N0 15
  N1 10
  N2 3
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGICSPREAD(M)'

25 genes related to 'PATHOLOGICSPREAD(M)'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 37
  M1 4
  MX 17
     
  Significant markers N = 25
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
RFX7 1.268e-11 2.19e-07
GDPD4 4.964e-09 8.56e-05
ERCC2 2.597e-08 0.000448
DHDH 9.285e-08 0.0016
UNC93A 1.432e-07 0.00247
ZPBP 1.813e-07 0.00313
NFE2L1 2.075e-07 0.00358
PCDHA5 2.938e-07 0.00506
KRAS 4.228e-07 0.00729
PPP4R1 4.939e-07 0.00851

Figure S4.  Get High-res Image As an example, this figure shows the association of RFX7 to 'PATHOLOGICSPREAD(M)'. P value = 1.27e-11 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

38 genes related to 'TUMOR.STAGE'.

Table S12.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.07 (1.2)
  N
  Stage 1 30
  Stage 2 1
  Stage 3 20
  Stage 4 7
     
  Significant markers N = 38
  pos. correlated 32
  neg. correlated 6
List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S13.  Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
INSM1 0.707 5.552e-10 9.58e-06
ZNF177 0.6769 5.41e-09 9.33e-05
DLX6AS 0.6731 7.101e-09 0.000122
MATK 0.6487 3.648e-08 0.000629
NSD1 0.6463 4.266e-08 0.000736
TMEM132B -0.6425 5.413e-08 0.000933
WDR16 0.6388 6.818e-08 0.00118
DIO3 0.6329 9.794e-08 0.00169
SERHL 0.6278 1.333e-07 0.0023
DLEU2 -0.6209 1.995e-07 0.00344

Figure S5.  Get High-res Image As an example, this figure shows the association of INSM1 to 'TUMOR.STAGE'. P value = 5.55e-10 with Spearman correlation analysis.

Methods & Data
Input

  • Expresson data file = KIRP-TP.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 59

  • Number of genes = 17247

  • Number of clinical features = 8

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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